This article presents a use-inspired perspective of the opportunities and
challenges in a massively digitized power grid. It argues that the intricate
interplay of data availability, computing capability, and artificial
intelligence (AI) algorithm development are the three key factors driving the
adoption of digitized solutions in the power grid. The impact of these three
factors on critical functions of power system operation and planning practices
are reviewed and illustrated with industrial practice case studies. Open
challenges and research opportunities for data, computing, and AI algorithms
are articulated within the context of the power industry's tremendous
decarbonization efforts.
It argues that the intricate interplay of data availability, computing capability, and artificial intelligence (AI) algorithm development are the three key factors driving the adoption of digitized solutions in the power grid.
The impact of these three factors on critical functions of power system operation and planning practices are reviewed and illustrated with industrial practice case studies.
Open challenges and research opportunities for data, computing, and AI algorithms are articulated within the context of the power industry’s tremendous decarbonization efforts.
Critical energy/electric infrastructure information
臨界エネルギー・電気インフラ情報
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Direct current Alternating current Artificial intelligence
直流 交流電流 人工知能
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AC AGC Automatic generation control AI AMI Advanced metering infrastructure ARX Autoregressive with exogenous input ASIC Application-specific integrated circuit BLAS Basic linear algebra subroutine CEII CNN Convolutional neural network DC DER Distributed energy resource DFR Digital fault recorder DMS Distribution management system DSA Dynamic security analysis ED ELM Extreme learning machine EMS EV FDR FTR GPU Graphic processing unit HIL
AC AGC自動生成制御 AI AMI 高度な計測インフラ ARX 外部入力付き自動回帰 ASIC アプリケーション固有の集積回路 BLAS 基本線形代数サブルーチン CEII CNN 畳み込みニューラルネットワーク DC DER 分散エネルギー資源 DFR デジタルフォールトレコーダ DMS 配電管理システム DSA 動的セキュリティ分析 ED ELM エクストリーム学習マシン EMS EV FDR FTR GPU グラフィ処理ユニット HIL
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Energy management system Electric vehicle Frequency disturbance recorder Financial transmission right
エネルギー管理システム 電気自動車の周波数外乱レコーダー金融送信権
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Economic dispatch Hardware-in-loop
経済派遣 hardware‐in-loop
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L. Xie and X. Zheng are with the Department of Electrical and Computer Engineering at Texas A&M University (email: le.xie@tamu.edu, zxt0515@tamu.edu).
Y. Sun and T. Bruton are with Oncor Electric Delivery (email: Yannan.Sun@oncor.com , Tony.Bruton@oncor.co m).
Y. SunとT. BrutonはOncor Electric Delivery(電子メール: Yannan.Sun@oncor.com 、Tony.Bruton@oncor.co m)と提携している。
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The work of L. Xie, X. Zheng, and T. Huang was supported in part by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) through the Solar Energy Technologies Office (SETO) under Grant DE-EE0009031, and in part by the National Science Foundation under Grant OAC-1934675, ECCS-2035688, and ECCS-1611301.
L. Xie, X. Zheng, T. Huang の業績は、Grant DE-EE0009031 の下で太陽エネルギー技術局 (SETO) を通じて、米国エネルギー省のエネルギー効率・再生可能エネルギー局 (EERE) と、Grant OAC-1934675 、ECCS-2035688 、ECCS-1611301 によって支援された。
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Point of common coupling Neural network Optimal power flow
共通結合のポイント ニューラルネットワークの最適潮流
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Inverter-based resource Internet of things
インバータを用いたモノのインターネット
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HVAC Heating, ventilation, and air conditioning IBR IoT KNN K-nearest neighbors LMP Local marginal price LSTM Long short term memory MDP Markov decision process MIO Mixed integer optimization NN OPF PCA Principle component analysis PCC PMU Phasor measurement unit PV RF RIC RL RPCA Robust principle component analysis RTU Remote terminal unit SCADA Supervisory control and data acquisition State estimation SE Satisfiability modulo theory SMT Sequence of events SOE SSA Static security analysis SVM Support vector machine SVR UC
HVAC Heating, ventilation, and air conditioning IBR IoT KNN K-nearest neighbors LMP Local marginal price LSTM Long short term memory MDP Markov decision process MIO Mixed integer optimization NN OPF PCA Principle component analysis PCC PMU Phasor measurement unit PV RF RIC RL RPCA Robust principle component analysis RTU Remote terminal unit SCADA Supervisory control and data acquisition State estimation SE Satisfiability modulo theory SMT Sequence of events SOE SSA Static security analysis SVM Support vector machine SVR UC 訳抜け防止モード: HVAC 加熱、換気、空調 IBR IoT KNN K - 近隣のLMP 局所限界価格 LSTM 長期メモリ MDP Markov 決定プロセス MIO 混合整数最適化 NN OPF PCA 原理成分分析 PCC PMU パラメータ測定ユニット PV RF RIC RL RPCA ロバスト原理成分分析 RTU リモート端末ユニット SCADA スーパーバイザ制御 データ取得状態推定 SE 満足度変調理論 SMT イベントのシーケンス SOE SSA 静的セキュリティ解析 SVM サポートベクタマシン SVR UC
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Photovoltaic Random forest Residential/Industri al/Commercial Reinforcement learning
太陽光発電林の居住/産業/商業強化学習
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Support vector regression Unit commitment
サポートベクトル回帰 ユニットコミットメント
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I. INTRODUCTION
I. イントロダクション
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Digitization of the electric power grid, which broadly refers to the deployment of sensing, communication, and computational capabilities, has been an integral part of the electrification process over the past century and is a key enabling factor that drives power grid transformation by spreading its outreach vertically over plants, transmission grids, distribution grids, and end-use customers.
As data availability and computing capacity continue to grow, large-scale power grids are built and operated with very high levels of reliability and efficiency, providing electricity services to billions of customers.
The state of today’s power grids in the United States (U.S.) can be summarized in three aspects:
米国(米国)における今日の電力網の状態は、以下の3つの側面で要約できる。
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(i) for system reliability, the average duration of annual electric power interruptions in the U.S. varied from 3 to 8 hours in the period between 2013 and 2020 [1];
(ii) for cost of electricity, the average wholesale electricity price across the U.S. varied from $30 to $60 per MWh in the period between 2016 and 2021 [2]; and
PROCEEDINGS OF THE IEEE 2 carbon footprint, electricity generation in the U.S. produced an average of about 0.4 kilograms of carbon dioxide emissions per kWh in 2020 [3].
In response to climate change, which has emerged as a global concern, rapid decarbonization is imperative to reduce carbon emission, a quarter of which are contributed by the electricity sector.
It is foreseeable that numerous decarbonization measures will cause profound changes in the electricity sector in the next few decades [4].
今後数十年で多くの脱炭対策が電力部門に大きな変化をもたらすと予想されている[4]。
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Such changes have two major drivers:
このような変更には2つの大きな要因がある。
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(i) the energy portfolio transition from high-carbon to low/zero-carbon generation sources, such as hydrogen, nuclear, wind and solar-based commercial generation units and distributed energy resources (DERs), and
The proliferation of power electronics-based inverters is changing system dynamic characteristics.
パワーエレクトロニクスベースのインバータの普及はシステムの動的特性を変化させている。
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Increasing numbers of DERs at grid edge are strengthening the interaction between transmission and distribution systems.
グリッドエッジにおけるDERの数の増加は,伝送系と配電系の相互作用を強化している。
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Rapid expansion of electric vehicles (EVs) will lead to substantial changes in electricity demand patterns.
電気自動車(EV)の急速な拡大は、電力需要パターンに大きな変化をもたらすだろう。
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Therefore, it is imperative for the grid operators to adopt a more flexible and risk-aware approach.
したがって、グリッドオペレーターがより柔軟でリスク対応のアプローチを採用することが不可欠である。
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Given the massive data availability and computing capacity provided by digitized power grids, data-driven artificial intelligence (AI) methods are feasible solutions for complementing traditional model-based approaches to address these complex emerging challenges.
From a broader economic perspective, AI has transformed a variety of domains over the past decade [5], including language processing [6], speech recognition [7], facial recognition [8], real-time object detection [9], multiplayer game [10]– [12], recommendation system [13], intelligent robotics [14]– [16], driving assistant system [17], disease diagnosis [18], drug discovery [19], finance [20], and others.
We attribute such unprecedented success of AI as an intricate interplay between three factors, namely, massive data acquisition, high computing performance, and advanced AI algorithms [21]– [23].
The availability of data from heterogeneous resources has been increasing at an unprecedented rate [24]–[26] and provides fuel for developing AI-based, data-driven applications for valuable knowledge extraction in wide-range domains.
In addition, remarkable improvements in computing performance have enabled a variety of practical large-scale AI models, credited to the collective advances in hardware, software, and computing architecture [27].
Alongside rapidly-growing AI infrastructure that provides massive data and computing capacity, numerous advanced AI algorithms have been developed in the past decade.
State-of-the-art performance on benchmark datasets for tasks in multiple research fields has been improved by pre-trained models [28]–[31] and novel AI model architectures [32]–[35].
Given the widespread success of AI applications, the development and deployment of interpretable, robust, and scalable AI may help to accommodate the emerging changes brought by decarbonization, aiming to reduce carbon emission and meanwhile “keep the lights on” in a reliable and economic
way (Fig. 1). However, to facilitate the process towards decarbonization, many open questions persist in implementing practical AI approaches in digitized power grids, including domain-agnostic computing and AI advances, use-inspired AI algorithm development, and cyber-physical security and privacy in a massively digitized power grid.
To this end, this paper aims to provide a comprehensive review of the stateof-the-art practice of power grid digitization transformation, which focuses on three backbone factors: data, computing, and algorithms.
Specifically, this paper provides a review of the recent progress in data acquisition, computing capability, and AI algorithms that are applicable to power systems.
Section VI provides an industry perspective on AI adoption.
第6節は、AIの採用に関する業界的な視点を提供する。
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Finally, Section VII concludes the paper with remarks on future directions for power grid modernization.
最後に、第7節では、電力網の近代化に向けた今後の方向性について述べて、論文を締めくくっている。
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Fig. 1. Tri-factors of digitization are enabling technologies that facilitate the process towards power grid decarbonization while simultaneously meeting requirements in the aspects of reliability, cost of electricity, and carbon emission, while power grid decarbonization steers use-inspired development of power grid digitization.
SYSTEMS Modern power grids are being driven by strong momentum of decarbonization [36] with decentralization and transportation electrification.
システム 現代の電力網は, 分散型化と輸送電化により, 脱炭素化の強い勢いで推進されている.
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Fig 2 shows the brief conceptual diagram of a modern power grid, which can be separated into transmission and distribution systems.
図2は、送電系統と配電系統に分けられる現代の電力網の簡単な概念図を示している。
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Transmission systems refer to bulk systems that have voltages higher than 66 kV and consist of generation, substation and transmission lines, which are usually operated by state-wide or cross-state system operators.
Distribution systems refer to close-to-users systems that have voltages lower than 33 kV and connect to residential, commercial and industrial load, which are usually operated by local utility companies.
Power grid decarbonization is changing the energy portfolio in terms of generation resources, such as increasing commercial-size solar PV and wind farms in transmission systems, and DERs such as rooftop solar PV in distribution systems.
Power electronics-based inverters are thus being deployed to convert electricity by renewables from
電力電子機器をベースとするインバータは、再生可能エネルギーで電力を変換する。
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Advanced AI algorithmsPower grid digitizationEnabling technologyUse-inspir ed developmentCost of electricity Power grid decarbonization
高度なAIアルゴリズム 電力グリッドのデジタル化 テクノロジー 電力グリッドの非炭化
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英語（論文から抽出）
日本語訳
スコア
PROCEEDINGS OF THE IEEE 3 Fig. 2.
IEEEの成果 3 図2。
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Conceptual diagram of a modern power grid, consisting of transmission and distribution systems.
送電系統と配電系統からなる近代電力網の概念図。
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The high-voltage transmission system consists mainly of generation, substation, and transmission lines.
高電圧伝送システムは、主に発電、変電所、送電線で構成される。
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The low-voltage distribution system supplies electricity to residential, commercial, and industrial load.
低電圧の配電システムは住宅、商業および工業用負荷に電力を供給する。
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Decarbonization has promoted utility-scale renewable generation, distributed energy resources and electric vehicles, while reducing investment in thermal generation.
Digitization has contributed to the reform and upgrade of control centers through the development of cloud data storage and computing and the deployment of massive digitized sensors across the grid.
Transportation electrification introduces a rapidly expanding number of electric vehicles into distribution systems.
輸送電化は、急速に拡大する電気自動車を流通システムに導入する。
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The modern power system operations in high-voltage transmission systems can be broken down into two categories [37].
高電圧伝送システムにおける現代の電力系統の運用は2つのカテゴリー[37]に分けられる。
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The first category is physical operations, which are responsible for the grid’s physical security1 and resource adequacy;2 the second concerns market operation.
Both physical and market operations are summarized in Fig 3.
物理と市場の両方の業務は、図3にまとめられている。
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A. Functions of Physical Operation and Planning
A. 身体操作と計画の機能
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Power system operation and planning fulfills the reliability of power systems via multiple functions including real-time monitoring, control, protection, and system reliability analysis.
A system-wide monitoring system collects and processes measurements, and presents intuitive information to system operators via visualization and alarming.
A control system performs control actions either manually or by automated procedures.
制御システムは、手動または自動手順で制御動作を行う。
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A protection system executes prescribed corrective measures upon detection of anomalies within targeted system components, which is achieved mainly by local sensors and actuators.
Load and renewable forecasting provides input for both system and market operation, by estimating uncertain net load
負荷及び再生可能予測は、不確実な純負荷を推定することにより、システムと市場の両方に入力を提供する
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1Physical security in power systems refers to the ability to resist contingency disturbances, such as a transmission line short circuit and loss of system components.
2Resource adequacy in power systems refers to the ability to supply electricity that accommodates load variation, renewable uncertainty, and system component outages.
2Resource Adequacy in power systemは、負荷変動、不確実性、システムコンポーネントの停止を許容する電力を供給する能力を指す。
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and renewable generation of various projection horizons.
様々な予測地平線を再生できるのです
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Load forecasting covers various prediction horizons spanning hours, days, weeks, months, and years ahead, whereas renewable forecasting provides only hours and days-ahead predictions.
In real-world power grids, short-term load forecasting typically has high accuracy and renewable forecasting also has acceptable errors that can be mitigated by real-time operation of dispatchable resources.
Real-time monitoring and control are implemented mostly by energy management systems (EMS) in the control center, the primary functional modules of which mainly include supervisory control and data acquisition (SCADA), state estimation (SE), and automatic generation control (AGC).
The SCADA system fulfills measurement acquisition and control telemetry through communication channels between the control center and remote terminal units (RTUs), at the respective electrical station or device.
Typically, the data acquisition function collects measurements every 2 to 10 seconds, of which the data stream is a key enabling factor for realizing other functionalities such as state estimation, real-time control, unit commitment, and economic dispatch.
For accurate situational awareness of the system’s current operation, function SE provides the steady-state estimation of system variables that are not directly observed in streaming SCADA data.
(ii) balance power generation, load demand and cross-area interchange in real time.
(ii)発電・負荷需要・地域間交流をリアルタイムにバランスさせる。
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Droop-based generator governors that are responsible for primary control perform instantaneous power quality corrections before triggering protection relays.
Similarly, a distribution management system (DMS) enables real-time monitoring in the distribution system, with a few similar functions to EMS, such as SCADA and event analysis [39].
It is worth noting that most field devices in the distribution systems are manually operated rather than remotely controlled, indicating a lower level of automation compared to the transmission system.
Security analysis focuses on the process of system state transitions initiated by reasonable disturbances such as short circuits and loss of system components.
Static security analysis (SSA) evaluates the viability of post-event equilibrium by calculating power flow or optimal power flow to check whether a power or voltage violation happens after an N − 1 contingency.4
静的セキュリティ分析(SSA)は、N−1共振器の後に電力または電圧違反が発生したかどうかを確認するために、電力フローまたは最適電力フローを計算することにより、後平衡の生存可能性を評価する。 訳抜け防止モード: 静的セキュリティ分析(SSA)は、電力フローまたは最適電力フローを計算し、ポスト-イベント平衡の生存可能性を評価する。 電源または電圧違反は N − 1 同時発生後に起こる
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3One cycle of a 60-Hz electric power system is about 16 ms. 4The N − 1 contingency refers to loss of a single system component, such
60Hzの電力システムの1サイクルは約16ミリ秒である。 訳抜け防止モード: 60Hzの電力システムの1サイクルは約16ミリ秒である。 4 N − 1 は単一系成分の損失を意味する。
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as generation outage and transmission line tripping.
停電と送電線のトリップとして
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Dynamic security analysis (DSA) evaluates the ability of the system to transition from one equilibrium to another postevent equilibrium within security criteria [23] by simulating on system dynamic models.
Adequacy analysis quantifies the system’s capacity for sustainable supply that accommodates load variation, renewable uncertainty and system component outages by several manually defined indices.
Wholesale markets comporise day-ahead and real-time energy markets, capacity markets, financial transmission right (FTR) markets and ancillary service markets.
Both day-ahead and Economic dispatchUnit commitmentMarket operationSystem operationSystem security analysisSystem adequacy analysisSupervisory control and data acquisitionReal-time controlShort-termLon g-termState estimationGeneration SubstationLoadLoad and renewable forecastingDay-ahead energyReal-time energySCADA dataMarket participantsBidsLong /mid/short-term planningLMPCapacityF inancial transmission rightAncillary serviceControl signalsSchedulesSecu rity constraintsLoad and renewable forecasting
日頭・日頭ともに economic dispatchunit commitmentmarket operationsystem security analysis system adequacy analysis supervisory control and data acquisition real-termstate estimationgeneration substationloadload and renewable forecastingday-ahead energyscada datamarket participantsbidslong /mid/short-term planninglmpcapacityf inancial transmission rightancillary servicecontrol signalsschedulessecu rity constraintsload and renewable forecasting 訳抜け防止モード: 日頭・日頭ともに システム セキュリティ分析 システム セキュリティ分析 システム システム セキュリティ分析 システム セキュリティ分析 スーパーバイザリコントロールとデータ取得 ショート - termLong - termState EstimationGeneration SubstationLoadLoad and renewable forecastingDay - ahead EnergySCADA dataMarket participantsBidsLong / mid / short - term planningLMPCapacityF inancial transmission rightAncillary serviceControl signalSchedulesSecur ity constraintsLoad 再生可能エネルギーの予測は
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PROCEEDINGS OF THE IEEE 5 real-time energy markets determine clearing prices based on bids from market participants, incorporating physical constraints and potential restrictions.
Ancillary service markets provide regulation and reserve.
補助サービス市場は規制と予備権を提供している。
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Unit commitment (UC) and economic dispatch (ED) are two major security-constrained , bid-based mechanisms to handle the scheduling of generation and the management of system congestion.
Both UC and ED are typically formulated as large-scale nonlinear/linear programming problems, known as optimal power flow (OPF).
UC と ED は、通常、オプティマイトパワーフロー (OPF) として知られる大規模非線形/線形プログラミング問題として定式化されている。
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Providing forecasted load and renewable as input, the UC function determines when and which generation units start up and shut down in day-ahead markets.
The ED function calculates the power output of each committed generation unit and associated local marginal prices (LMPs).
ED関数は、各コミット生成ユニットの出力と関連する局所限界価格(LMP)を算出する。
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ED is performed to meet the day-ahead hourly forecasted load in day-ahead energy marketsas well as to meet the minute-ahead forecasted load every 5 to 10 minutes in real-time energy markets [42].
Given the proliferation of DERs in distribution grids such as distributed generation, interruptible load, and electricity storage, the retail market will involve system upgrades and reforms in the future to accommodate DER market participation, and to establish an appropriate mechanism of scheduling and compensation [43].
Renewable integration and transportation electrification at scale impose challenges on the paradigm of protection and control.
再生可能エネルギーの統合と輸送の電化は、保護と制御のパラダイムに課題を課す。
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The emergence of massive grid-following and gridforming inverted-based resources (IBRs) may challenge the effectiveness and efficiency of the current central control frame due to the unknown impacts of electromagnetic dynamics and low inertia.
Besides, typical methods for adequacy and security analysis are numerical simulations that highly rely on grid models of multiple time scales, including electromagnetic dynamic (very fast), electromechanical dynamic (fast), and steady state (slow).
However, system characteristics are being changed due to the proliferation of inverter-interfaced renewable resources and EVs in modern power grids, such as low inertia and deeper integration of transmission and distribution systems.
These emerging system characteristics create a need for new requirements on the existing models to determine whether the system is within critical security criteria.
(iii) cross-domain electricity-transpor tation models to incorporate the impacts of transportation networks on EVs.
三 輸送網のEVへの影響を考慮したクロスドメイン電気輸送モデル
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Market operation also faces the challenge of managing potential market risks resulting from the variability and stochasticity of renewable generation [44].
Strong uncertainty is a key obstacle to economic dispatch to
強い不確実性は経済の派遣にとって重要な障害である
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(i) maintain system stability as tertiary frequency control and
二 第三周波数制御としてシステムの安定性を維持すること。
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(ii) avoid unexpected renewable curtailment to the greatest possible extent to achieve decarbonization.
(ii)脱炭素化を実現するために、最も可能な範囲まで、予期せぬ再生可能減量を避けること。
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Current wholesale markets may not be sufficiently prepared to accommodate increasingly frequent extreme weather events such as the 2021 Texas power outage event [45] to prevent spiking price and mitigate energy scarcity.
Specifically, strong uncertainty regarding system net load and intermittent renewables generation in future grids will raise severe challenges for the accuracy and robustness of short-term load and renewable prediction.
Deepening transportation electrification may also undermine the existing enduse and econometric models for medium and long-term load forecasting [46].
輸送電化の深化は、中長期負荷予測のための既存の耐久・計量モデルも損なう可能性がある[46]。
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The distribution system also faces a growing number of facility challenges.
流通システムもまた、多くの施設の課題に直面している。
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Aging power lines may limit maximum use of renewable energy sources, such as wind farms and utility scale solar, especially in less populated areas where large renewable energy installations are located.
The utilization and availability of DERs installed in densely populated areas can be affected by frequent localized outages intermittently that may be recognized by the control center.
Given stronger integration and correlation between transmission and distribution grids, facility outages, such as transformer failures, may cause wider impacts.
Furthermore, in aiming to establish a competitive retail market in the distribution system, there multiple critical problems remain unsolved, such as LMP calculation and demand response modelling; however, these are beyond the scope of the paper.
Overall, the profound changes by decarbonization are posing and will continue to pose numerous challenges to all aspects of physical reliability and economics.
Given massive data acquisition as the “fuel” and high computing power as the “engine,” applying advanced data-driven AI-based approaches as an “autopilot” have the potential to steer the vehicle forward in a flexible and risk-aware manner.
III. DATA ACQUISITION IN DIGITIZED POWER GRIDS In broad industry sectors, large-volume and heterogeneously structured data have been generated at an unprecedented rate by diverse resources since 2010 [24]–[26], such as Internet of Things (IoT) records, social media, smart devices, and healthcare systems.
III。 DIGITIZED POWER GRIDS 幅広い産業分野において、2010年[24]-[26]以降、インターネット・オブ・モノ(IoT)レコード、ソーシャルメディア、スマートデバイス、医療システムなど、さまざまなリソースによって、大容量および均一に構造化されたデータが前例のない速度で生成される。
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The availability of such tremendous volumes of data has facilitated numerous applications of valuable knowledge extraction in sectors [47] such as spanning manufacturing [48], healthcare [49], government [50], retail [51], infrastructure [52]–[54].
In particular, numerous high-quality open-source training datasets [55] have been created to boost AI research in the aspects of model training, testing, calibration, and benchmarking.
the explosive growth of data resources has also created massive volumes of data in heterogeneous formats, including electrical measurements that span across grids vertically, such as sensors
PROCEEDINGS OF THE IEEE 6 installed on grid-level components, smart meters and smart appliances as well as non-electrical measurements, such as weather, social media, traffic and geographic information [56].
It is worth noting that these basic functionalities have distinct requirements for data quality in perspectives of data accuracy, latency, and sampling rate [58].
This section will review data acquisition approaches of electrical measurements in the power grids.
本項では電力網における電気測定データ取得手法について概説する。
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A. Real-world Measurements in Power Systems
a. 電力システムにおける実世界の計測
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1) Sensors in Transmission Systems: SCADA systems, which have played an important role in transmission system operation, are capable of collecting facility information and sending control signals, which are implemented by the critical component(i.e. RTUs).
SCADA systems collect asynchronous data on bus voltage magnitude as well as active and reactive power flows; the typical reporting rate is merely 1 sample per 2 to 6 seconds.
The wide-range acquisition of SCADA data has facilitated remote monitoring and system operation automation.
SCADAデータの広範な取得により、遠隔監視やシステム操作の自動化が容易になった。
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For example, the EMS at the control center is capable of estimating physical state variables that are not directly observable based on SCADA data alone.
PMUs are able to measure the voltage phasors5 at the installed bus (typically substations) and current phasors of the lines connected, along with synchronized time stamps, for which the typical reporting rate is 30 or 60 samples per second.
Compared to SCADA, PMUs’ high accuracy of time stamps and sensing, low latency, and high sampling rate of PMU benefit basic functionalities to different degrees [60]:
(i) more real-time control and protection applications become potentially implementable due to all of these advantages, such as remedial action schemes including grid islanding and shortterm stability control;
(ii) online system security analysis, such as disturbance detection and situational awareness, can be significantly improved due to low latency;
(ii) 障害検出や状況把握などのオンラインシステムセキュリティ分析は、低レイテンシのため大幅に改善することができる。
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(iii) system adequacy analysis for long-term planning, such as model calibration, can be improved due to high accuracy.
(iii)モデル校正等の長期計画におけるシステム適性解析は、精度が高いため改善することができる。
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However, it is worth noting that, due to several factors such as high costs and time consumption of installation, only around 2,500 production-grade PMUs have been installed across the North America transmission power grid [61], [62].
Digital fault recorders (DFRs) capture and store transient data and sequence of events (SOE) data that can be used for various purposes such as protection scheme monitoring and fault diagnosis, which tend to be implemented offline.
DFRs have three typical recording mechanisms: steady-state, lowspeed and high-speed disturbance recording modes.
dfrは定常モード、低速モード、高速外乱記録モードの3つの典型的な記録機構を有する。
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The disturbance recording modes are usually triggered by signals from
障害記録モードは通常、信号によってトリガーされる
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5Phasors contain magnitude A and phase angle φ of sinusoidal waveforms that can be expressed as Asin(ωt + φ), where ω is 2π × 60 rad/s in a 60-Hz system.
protection relays. The steady-state recording mode captures the min, max and mean values of phasors at a low sampling rate of 1 sample per 10 seconds to 1 hour.
The low-speed disturbance mode aims to provide phasor-domain information of long-term and short-term disturbances at a sampling rate of 1 sample per 1 to 10 cycles.
The high-speed disturbance mode aims to record instantaneous time-domain voltage and current measurements of transient faults at a sampling rate of hundreds of samples per cycle.
2) Sensors in Distribution Systems: The rapid expansion of advanced metering infrastructure (AMI) meters at grid edge has created massive amounts of residential electricity consumption data, typically at a rate of 1 sample every 1 or 5 minutes.
For example, the Pacific Gas and Electric Company collects more than 3 terabytes of power data from 9 million smart meters across the grid in the territory, and the State Grid Corporation of China collects 200 terabytes of data per year [63].
SCADA in distribution systems has facilitated remote monitoring and automated operation in multiple aspects, such as substation, feeder, and end-user load control.
In substation systems, SCADA gathers data including voltage magnitude, current magnitude and binary status of facilities such as switches, breakers, and transformers.
In typical feeder systems, SCADA facilitates the collection of historical data from feeder status of devices such as controlled load break switch and reclosers.
In end-user load, SCADA collects all meter data from the end users.
エンドユーザの負荷では、SCADAはエンドユーザからすべてのメーターデータを収集します。
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The frequency disturbance recorder (FDR), one of representative PMU applications in distribution systems, is a GPS-synchronized single-phase PMU at ordinary 120-volt wall outlets.
周波数障害レコーダ(英: frequency disturbance Recorder、FDR)は、通常の120ボルトの壁面出口におけるGPS同期単相PMUである。
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FDRs have the advantages of low cost and high deployability; they can be deployed even at residential households and campuses [64].
Using hundreds of FDRs that have been strategically placed across the U.S., the frequency monitoring network FNET/GridEye [65] is able to provide visualized nation-wide frequency monitoring.
Artificially generated data are commonly used for power system research for two major reasons:
人工的に生成されたデータは、主に2つの主な理由から電力システム研究に使用される。
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(i) most real-world operational data are protected by policies such as Critical Energy/Electric Infrastructure Information (CEII) owing to confidentiality, and
(ii) real-world measurement datasets of high-impact events are usually insufficient for data-driven model training, due to the reliability of real-world power grids, which ensures that high-impact events are rare.
Alternatively, artificial data generation methods facilitate the gathering of arbitrary numbers of data samples under varying scenarios and conditions, including voltage, current, frequency, and even machine inner state measurements across grid models.
Simulation models of transmission and distribution systems can be categorized into two major types:
送電・配電システムのシミュレーションモデルは次の2つのタイプに分類できる。
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(i) small-scale standard systems and
(i)小規模標準システム及び
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(ii) large-scale synthetic systems, which are available at [66].
(ii)大規模合成システムで, [66]で利用可能である。
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IEEE standard
IEEE標準
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英語（論文から抽出）
日本語訳
スコア
PROCEEDINGS OF THE IEEE 7 there are several key challenges regarding the data for AI algorithms.
IEEEの成果 7 AIアルゴリズムのデータには、いくつかの重要な課題がある。
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First, in contrast to numerous datasets that have benefited broad AI communities, the lack of publicly accessible high-quality power datasets may be impeding the advancement of AI research in power systems.
For example, insufficient data representativeness is one of the decisive factors for data-hungry AI methods.
例えば、不十分なデータ代表性は、データハングリーAIメソッドの決定的な要素のひとつです。
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Real-world measurements cannot provide a sufficient volume of publicly available data due to confidentiality rules and strong grid reliability.
実世界の計測は、機密性ルールと強力なグリッド信頼性のため、十分な量の公開データを提供できない。
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Randomly sampled scenarios in simulation can generate massive amounts of data, but they do not necessarily guarantee representativeness;therefore they likely lead to unexpected training biases, which was demonstrated by the example of ACOPF scenario generation [75].
Second, the feasibility of the proposed AI algorithms may be constrained by the current data acquisition system, as indicated by the data quality requirements of major power system applications [58].
For example, limited and inappropriate placement of high-sampling sensors that determine situational awareness for a specific task may confine advanced analysis and control including but not limited to practical applications of AI methods.
Third, although AI methods may offer unique creativity given cross-domain datasets, they require deep interdisciplinary knowledge and collaboration to identify useful combinations of heterogeneous datasets, which has been demonstrated by few AI-based canonical studies, such as automatic classification of distribution grid phases by camera imaging [76] and comprehension COVID impacts on power sectors by mobile phone location data [77].
Given sufficient available data resources, the implementation of data-driven applications in modern power grids faces computational burdens derived from large-volume, heterogeneous data.
This section will give an overview of state-of-the-art computing that has facilitated general AI, and will then introduce data streaming management systems and data processing platforms [63], [78] in power systems.
The remarkable improvement of computing performance is the key factor in the proliferation of AI, which is attributable to advances in hardware, software, and generic algorithms [27].
Quantum leaps in computing performance have yielded a variety of practical large-scale AI models, among which the amount of computation for model training has been increasing exponentially with a 3.4-month doubling period [22], [79].
The rapid progress of hardware computing resources has been the main driver behind the development of AI models.
ハードウェアコンピューティングリソースの急速な進歩は、AIモデルの開発の主要な要因となっている。
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Of particular note, the emergence of general purpose graphics processing units (GPUs) [80] and AI accelerator applicationspecific integrated circuits (ASICs), such as [81]–[84], are capable of dramatically accelerating AI model training.
For instance, basic linear algebra subroutine (BLAS) libraries, which were created
例えば、基本線型代数サブルーチン(BLAS)ライブラリは、作成されました。
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Fig. 4. Number of AI/ML/DL papers per simulation model of various system scales.
図4。 各種システムスケールのシミュレーションモデル当たりのAI/ML/DL論文の数。
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Note that we only count typical open-source simulation models, including IEEE standard test cases and large-scale synthetic grids using Google Scholar advanced search among IEEE transaction papers from 2016 to 2021.
test systems are typically used for investigations such as algorithm assessment and power system analysis.
テストシステムは通常、アルゴリズムアセスメントやパワーシステム分析などの調査に使用される。
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Researchers have recently contributed to the creation of large-scale synthetic grid models [67] that possess realistic system characteristics.
研究者たちは最近、現実的なシステム特性を持つ大規模合成グリッドモデル[67]の作成に貢献した。
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These large-scale synthetic grids have been used for analysis such as macro-scope energy portfolio transition [68], [69] and quantitative assessment of measures against extreme events [70].
For intuitive impression, we show the “popularity” of simulation models in Fig 4 by counting the number of corresponding IEEE transaction papers,6 which are used for both machine learning model training and testing.
It is clear that the most commonly used models for AI algorithm training, testing and calibration are the IEEE 39-bus and 118-bus systems, whereas the large-scale models are rarely adopted.
Please refer to Tables II-V for other simulation models that are not included in Fig 4.
図4に含まれていない他のシミュレーションモデルについて、テーブルII-Vを参照してください。
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2) Hardware Test Bed: The development of hardware-inloop (HIL) simulators has been used to support various types of research, including event detection, situational awareness, wide area monitoring and control, and cyber security [71].
HIL may play an important role in electromagnetic transient simulation of electronics-rich power grids because of its ability to represent realistic very-fast dynamics.
This section gives an overview of data acquisition approaches in today’s electric power grids.
この記事では、今日の電力グリッドにおけるデータ取得アプローチの概要を紹介する。
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The rapid expansion of advanced sensors across systems and the development of simulation have facilitated massive data acquisition spanning multiple spatial and temporal scales and have further accelerated practical data-driven applications.
Efforts to explore datadriven innovation, such as big data hubs [73], [74], have also promoted data-intensive research in the power system industry as well as academia and education.
6We use the Google Scholar advanced search to count IEEE transaction papers from 2016 to 2021, using several keywords such as “IEEE Transactions on”, “machine learning”, “#-bus system”, “power flow” and “transient”.
PROCEEDINGS OF THE IEEE 8 decades ago [86]–[88], have been used to optimize common linear algebra operations that are recursively executed in deep neural networks [89]–[92].
In particular, Nvidia GPUs, which are widely supported by mainstream deep learning framework [93]–[95], have a highly optimized library cuDNN [96] enabling high-performance GPU acceleration.
The progress of generic algorithms has also improved computing performance, exhibiting enormous heterogeneity on problems of different types and sizes [97].
It is worth noting that some large-size problems benefit just as much or even more from algorithmic improvement than from Moore’s law.
大規模な問題のいくつかは、ムーアの法則よりもアルゴリズムの改善の恩恵を受けている点に注意が必要だ。
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For instance, the total speedup of solving mixed integer optimizations (MIO) was 2.2 trillion times during the 25 years between 1991 and 2016 [21], of which a factor of 1.6 million is due to hardware speedup from 59.7 GFlop/s in 1993 to 93.0 PFlop/s in 2016; another factor of 1.4 million is due to software and algorithmic speedup from CPLEX 1.2 in 1991 to Gurobi 6.5 in 2015.
Because power system security highly relies on real-time system operation and control, it is challenging to store and process real-time data streaming effectively and efficiently.
Therefore, the building of real-time data streaming systems that mainly influence data latency is critical for the subsequent online data-driven applications including but not limited to AI-based methods.
In contrast to traditional database management systems that use statistical data storage, data stream management systems usually store synopsis data (instead of the entire dataset) via processing in order to handle frequent queries and data update.
We illustrate several of the most popular data stream management systems summarized in [63]: Aurora [98] has a good balance of accuracy, response time and resource utilization; TelegraphCQ [99] is mainly used for sensor networks, which involves a front end, a sharing storage, and a back end; STREAM [100] has the advantage in situations of limited resources in that it can execute queries with high efficiency.
In particular, big data management platforms are being developed to accommodate multi-modal data storage and processing of unstructured heterogeneous data.
Hadoop is able to process massive heterogeneous data efficiently and economically by taking advantages of a programming model [103], a distributed file system [104], and a distributed data storage system [105].
Spark, on the other hand, leverages the technology of resilient distributed datasets [106], which is more suitable for recursive computational operations in machine learning-based applications.
In terms of data management platforms that are suitable for power systems, several cases of solutions have been successful in facilitating energy efficiency.
For example, CenterPoint Energy has handle streaming messages from intelligent grid devices and smart meters using an IBMdeveloped platform to improve system reliability [107].
For its part, Oncor Energy Delivery has developed AMI databased predictive maintenance to reduce outages and guarantee sustainable supply enabled data platforms [108].
Oncor Energy Deliveryは、障害を低減し、持続可能なサプライ可能なデータプラットフォーム [108] を保証するために、AMIデータベースによる予測メンテナンスを開発した。 訳抜け防止モード: Oncor Energy DeliveryがAMIデータベースによる予測保守を開発 障害を減らし、持続可能なサプライ可能なデータプラットフォームを保証します [108 ]。
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CLASSIFICATION OF GRID SOLUTIONS BASED ON AI METHODS
Advanced computing power, along with massive data acquisition, has enabled many time-sensitive operations, such as real-time monitoring and security analysis.
However, with increasing complexity of power grids, such computing paradigm may face several challenges, such as privacy concern and communication bandwidth limit.
In contrast, edge computing that leverages computing resources at edge has the potential to improve computation efficiency and protect data privacy by performing data analytic close to customers [109].
V. AI SOLUTIONS TO POWER GRID DECISION MAKING This section surveys recent AI solutions to the core decision making processes in power grid operations.
V. AI SolutionTIONs to POWER GRID DECISION MAKING この節では、電力グリッド運用における中核的な意思決定プロセスに対する最近のAIソリューションを調査します。
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We report 85 papers, most of which were published in the IEEE transactions of the Power and Energy Society (e g , IEEE Transactions on Power Systems, and IEEE Transactions on Smart Grid) from 2019 to 2021.
我々は、2019年から2021年にかけて、パワー・アンド・エナジーのIEEEトランザクション(例えば、IEEE Transactions on Power Systems、IEEE Transactions on Smart Grid)で発表された85の論文を報告します。
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For earlier works about AI algorithms for grid operations, we refer readers to previous survey papers [23], [111]–[113].
Table I classifies the approaches used in these 85 papers according to the category to which these approaches belong, (i.e., supervised, unsupervised, and reinforcement learning).
In addition, for each decision making process, we provide not only an overview of the state-of-theart, AI-powered grid solutions, but also illustrative examples that give readers a sense of how specific AI techniques can be leveraged to solve grid challenges.
Data Sources (Availability) Solar irradiance and weather data (N) Actual wind power data from Glens of Foudland wind farm (N) Wind generation data from Irish transmission system operator (N) Residential smart meter data (N) Three PV arrays’ measured datasets in Australia (N) Wind Integration National Dataset (WIND) Toolkit [119] (Y) Desert Knowledge Australia Solar Centre (DKASC) (N) DKASC (N) Actual data of Tianjin power Grid in China (N) Hourly load data of the University of Texas at Dallas (UTD) (Y) Real-world utility data (N) 18-node real utility feeder (N) Data from three wind farm data in China (N) Datasets from ERCOT, PJM, CAISO (Y) Wind farm data from NREL (Y) 5-MW PV power plant (Y) Real-world smart meter data (N) CIGRE benchmark low voltage network (N) PV plant dataset (N) Real data set from residential Irish customers (N)
Data Sources (Availability) Solar irradiance and weather data (N) Actual wind power data from Glens of Foudland wind farm (N) Wind generation data from Irish transmission system operator (N) Residential smart meter data (N) Three PV arrays’ measured datasets in Australia (N) Wind Integration National Dataset (WIND) Toolkit [119] (Y) Desert Knowledge Australia Solar Centre (DKASC) (N) DKASC (N) Actual data of Tianjin power Grid in China (N) Hourly load data of the University of Texas at Dallas (UTD) (Y) Real-world utility data (N) 18-node real utility feeder (N) Data from three wind farm data in China (N) Datasets from ERCOT, PJM, CAISO (Y) Wind farm data from NREL (Y) 5-MW PV power plant (Y) Real-world smart meter data (N) CIGRE benchmark low voltage network (N) PV plant dataset (N) Real data set from residential Irish customers (N)
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Computation Resources i7-7700 CPU
計算リソース i7-7700 CPU
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i7-7700 CPU, 16GB RAM
i7-7700 cpu、16gb ram
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AI Method Neural network (NN), Extreme learning machine (ELM) ELM
- - i7-8550U CPU 16GB RAM i7-7700 CPU 16GB RAM i7-2600 CPU
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- Spectral clustering; regression Spectral clustering; Recursive Baysian Learning Multi-source and temporal attention network Long short term memory (LSTM) NNs ELM Deep belief network Regression; Gaussian mixture model Decision trees; Ant colony optimization Graph neural network Random forest
- スペクトルクラスタリング、回帰スペクトルクラスタリング、再帰的ベイズ学習 複数ソースおよび時間的注意ネットワーク long short term memory (lstm) nns elm deep belief network regression; gaussian mixed model decision tree; ant colony optimization graph neural network random forest
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Next we provide an example to elaborate on how AI can be leveraged to solve PV forecasting tasks in the grid.
次に、グリッド内のPV予測タスクを解決するためにAIをどのように活用できるかを詳しく説明する。
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The technical details are reported in [199].
技術的な詳細は[199]で報告されている。
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Figure 5 shows the geographic locations of a target solar site C6 and its neighboring N solar sites.
図5は、対象の太陽点C6とその近隣の太陽点の地理的位置を示しています。
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Let us suppose that we want to predict the solar irradiance of the target solar site C6 at time step (k + 1).
Reference [199] formulates the forecasting problem into one of estimating the parameters of the following autoregressive with exogenous input (ARX) model [199]:
(1) where x[k] is the solar irradiance at the target solar site at time step k; wi is the solar irradiance at the neighboring solar site i; f (·) is an ARX-structured function; and positive integers n, di, and mi are user-defined parameters that can be determined at training stages [199].
(1) x[k] が目標の太陽点における日射量である場合、wi は隣接する太陽点 i における日射量であり、f (·) はarx 構造関数であり、正の整数 n, di, mi は訓練段階 [199] で決定できるユーザ定義のパラメータである。
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The intuition of the formulation (1) is that the next-step solar irradiance x[k + 1] at the target solar site depends not only on the local solar irradiance, but also on the solar irradiance at its neighboring solar sites.
However, the algorithm proposed in [199] dose not provide a probability description for the forecast quality.
しかし,[199]線量で提案したアルゴリズムは,予測品質の確率記述を提供していない。
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One potential avenue for future work is to investigate such a description [199].
将来の研究の1つの潜在的な道は、そのような記述[199]を調べることである。
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B. Grid Economic Operation
B.グリッド経済活動
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The large-scale deployment of renewables poses unprecedented challenges to the electricity market operation.
再生可能エネルギーの大規模展開は、電力市場運営に前例のない課題をもたらす。
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Conventional deterministic tools may not be able to support the electricity market operation of the electricity infrastructure with a significant amount of uncertain renewables.
Reference [200] proposes a scenario-based approach that unlocks the potential of data in order to incorporate renewables’ uncertainties into the dispatch of grid resources.
Table IV summarizes the state-of-the-art AI adoption in these analyses.
テーブルIVは、これらの分析で最先端のAIの採用を要約している。
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Next, we will present a learning-based approach to networked microgrid security analysis [164], in order to show how an AI technique can be adopted in this specific topic area.
The dynamics of the networked microgrids can be described by ˙x = f (x), where state vector x is related to voltage magnitudes and phase angles at the points of common coupling (PCCs).
ネットワーク化されたマイクログリッドの力学は、状態ベクトル x が共通結合点(PCCs)における電圧の大きさと位相角に関係しているとき、x = f (x) で記述できる。
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In the networked microgrids, large disturbances may come from
ネットワーク化されたマイクログリッドでは、大きな乱れが生じる可能性がある
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(i) the microgrid operating mode change, e g , one microgrid enters an islanded mode; and
(i)マイクログリッドの動作モードの変化、例えば1つのマイクログリッドが島モードに入ること、
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(ii) the distribution network, e.g, distribution line tripping.
(ii)配電網、例えば配電線トリッピング。
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The security analysis attempts to quantify the disturbance magnitude that the networked microgrids can tolerate [164].
In [164], Huang et al formulate the security analysis problem as one of searching for a legitimate Lyapunov function, i.e., a system-behavior summary function for a dynamic system.
A Lyapunov function V (x) satisfies two conditions: (i) V (x) is a positive-definite function in a region R around the system equilibrium point; and (ii) the time derivative ˙V is a negative-definite function in R. In [164], the Lyapunov function is assumed to possess a neural network (NN) structure with parameter vector θ.
リアプノフ函数 V (x) は2つの条件を満たす: (i) V (x) は系平衡点の周りの領域 R における正定値関数であり、 (ii) 時間微分 yV は R における負定値関数である。 訳抜け防止モード: リャプノフ函数 V ( x ) は2つの条件を満たす: ( i ) V ( x ) は系の平衡点 ; の周りの領域 R における正定値函数である。 そして (ii ) の時間微分は R の負-定値関数である。 リャプノフ函数は仮定される パラメータベクトルθを持つニューラルネットワーク(NN)構造を持つ。
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To make the NN-structured function satisfy the two conditions of a Lyapunov function, a cost function c(θ) is designed.
NN構造関数をリアプノフ関数の2つの条件を満たすように、コスト関数c(θ)を設計する。
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The cost function incurs a positive penalty if the NN with θ violates one or both of the two Lyapunov function conditions.
コスト関数が θ を持つ NN が 2 つのリャプノフ函数条件の 1 つまたは両方に違反している場合、正のペナルティを生じる。 訳抜け防止モード: 費用関数が正のペナルティを引き起こす場合 θ を持つ NN は2つのリャプノフ関数条件の1つまたは両方に違反する。
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Vector θ is tuned by the following procedure:
ベクトルθは以下の手順で調整される。
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1) Create a sample pool by randomly drawing a large
1)大図をランダムに描画してサンプルプールを作成する
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number of states x within the region R;
領域 R 内の状態 x の数;
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2) Update θ n times based on the cost function c(θ) and
2)コスト関数 c(θ) と θ n に基づいて θ n を更新
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the gradient descent algorithm [164];
勾配降下アルゴリズム[164];
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Fig. 5. The target solar farm site C6 (in the red circle) and its neighboring solar farms.
where vector p concerns the power generation of all generators in all intervals during a planning horizon; vector c collects cost coefficients associated with generators; (2b) represents the scenario-independent constraints [200], such as ramp and capacity constraints of generators; and (2c) represents the scenario-dependent constraints [200], such as generation-load balance constraints.
Suppose that p∗ N is the solution to the optimization (2) given N historical samples.
p∗ N を N の歴史的標本の最適化 (2) の解とする。
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Because ∆N is a subset of all possible scenarios ∆, it is possible that there exists a scenario δ that causes the scenario-dependent constraints to be violated, i.e., g2(p∗ N , δ) > 0.
n はすべての可能なシナリオのサブセットであるから、シナリオ依存の制約に違反するシナリオ δ が存在する可能性、すなわち g2(p∗ n , δ) > 0 が存在する。
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The probability that such an event may occur is termed the “risk” in [200].
そのような事象が起こる確率は[200]において“リスク”と呼ばれる。
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Formally, the risk v(p∗
正式には、リスク v(p∗)
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N ) for the solution p∗
解 p∗ に対する N )
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N is defined by
N は定義されている
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N ) = Prob.
n ) = prob である。
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(δ ∈ ∆ : g2(p∗
(δ ∈ ) : g2(p∗)
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(3) where Prob.
3) プロブがある場合。
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(·) denotes the probability that event “·” occurs.
(·)は事象「·」が起こる確率を表す。
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We expect that the probability that the risk v(p∗ N ) of solution p∗ N exceeds a small number will be small, i.e.,
我々は、解 p∗ N のリスク v(p∗ N ) が小数 t を超える確率が小さいことを期待する。 訳抜け防止モード: 私たちはその確率を期待する。 解 p∗ N のリスク v(p∗ N ) は、小さな数 ～ 小さめ、I.E.
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N , δ) > 0)
n , δ) > 0) である。
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v(p∗ Prob.
v(p∗) Prob
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(v(p∗ N ) > ) < γ
(v(p∗) N ) > ) < γ
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(4) where 0 < , γ (cid:28) 1.
(4) ここで 0 < ..., γ (cid:28) 1 である。
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With the risk preference parameters and γ, a natural question is how to determine the size of ∆N , i.e., N, to achieve the risk preference (4).
Such a lower bound can help system operators determine how many scenarios must be drawn from the historical observations based on their risk preference.
For example, in an opensource, 2000-bus synthetic Texas grid, if we suppose that the risk preference parameters of the system operators are γ = 10−6 and = 0.0083, then 2000 historical scenarios are needed to be embedded into the ED formulation (2) [200].
A rigorous investigation of the relationship between the quantity of support constraints and the design parameters (γ and ) is still needed to further refine the algorithm in [200].
Data Resources (Availability) A real-world 1881-bus system in China (N) Polish 2383-bus system (N) 129-node feeder (N) A 33 bus distribution system from MATPOWER A 6700-bus French system (N); a 9000-bus system (N) 500-bus synthetic South Carolina system (N) 118-IEEE, 1354-PEG; 1888-RTE system (N) 30-, 57-, 118-, 300-IEEE (N) 261,489-consumer real dataset from a Brazilian utility (N) 13-IEEE (N) Real-world price data (Y) IEEE RTS-96; 2000-bus Texas synthetic grid (N) 821-day data (N) 24-, 118- IEEE (N) Market data from PJM, CAISO, and ISO-NE (N) Dutch market prices (N) Historic price data (N)
Data Resources (Availability) A real-world 1881-bus system in China (N) Polish 2383-bus system (N) 129-node feeder (N) A 33 bus distribution system from MATPOWER A 6700-bus French system (N); a 9000-bus system (N) 500-bus synthetic South Carolina system (N) 118-IEEE, 1354-PEG; 1888-RTE system (N) 30-, 57-, 118-, 300-IEEE (N) 261,489-consumer real dataset from a Brazilian utility (N) 13-IEEE (N) Real-world price data (Y) IEEE RTS-96; 2000-bus Texas synthetic grid (N) 821-day data (N) 24-, 118- IEEE (N) Market data from PJM, CAISO, and ISO-NE (N) Dutch market prices (N) Historic price data (N) 訳抜け防止モード: データリソース(可用性)) 中国の実世界1881バスシステム(N) ポーランド2383バスシステム(N) 19ノード供給システム(N) MATPOWER A 6700バスフランスシステム(N) 9000バスシステム(N) 500バス合成サウスカロライナシステム(N) 118-IEEE, 1354-PEG ; 1888-RTEシステム(N) 30- 57-, 118-, 300-IEEE ( N ) 261,489-consumer real data from a Brazil utility (N ) 13-IEEE (N ) Real - world price data (Y ) IEEE RTS-96 ; 2000-bus Texas synthetic grid (N ) 821-day data (N ) 24-, 118-IEEE (N ) Market data from PJM, CAISO, ISO - NE (N ) オランダの市場価格 (N ) 歴史的価格データ (N )
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Computation Resources i7-1065G7 CPU, 16GB RAM i7-8700K CPU, 32GB RAMi7-7820HQ CPU, 31.9GB RAM i7 CPU 16GB RAM; Nvidia Tesla V100 GPUs, 16GB RAM NN 24-core CPU, 128GB RAM
計算リソース i7-1065G7 CPU、16GB RAM i7-8700K CPU、32GB RAM i7-7820HQ CPU、31.9GB RAM i7 CPU 16GB RAM、Nvidia Tesla V100 GPU、16GB RAM NN 24コアCPU、128GB RAM
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AI Methods NN and oblique decision trees Stacked ELM Regression Copula function
AIメソッドNNと斜め決定木を積み重ねたERM回帰コプラ関数
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Bagged trees Nvidia Tesla V100 GPUs 4-core i7-3770 CPU, 16GB RAM2.6 GHz CPU, 2GB RAM
Deep learning Deep learning Rotation forest; XGBoost Regression Risk-averse learning KNN XGBoost ELM XGBoost XGBoost LSTM; recurrent neural network ELM
深層学習 深部学習 深部学習 深部学習 深部学習 深部学習 深部学習 リスク-逆学習 KNN XGBoost ELM XGBoost XGBoost LSTM; リカレントニューラルネットワーク ELM 訳抜け防止モード: 深層学習の深部回転林 : XGBoost回帰リスク-逆 KNN XGBoost ELM XGBoost XGBoost LSTMの学習 ; リカレントニューラルネットワークERM
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3) For the NN with the latest θ, search for samples that violated one or both of the two Lyapunov conditions via the satisfiability modulo theories (SMT) tool.
Fig. 7 visualizes a Lyapunov function learned from a state space for a grid-tied microgrid [164].
図7は、格子状マイクログリッド[164]の状態空間から学んだリャプノフ関数を視覚化する。
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The parameters of the system are reported in [164].
システムのパラメータは[164]で報告されている。
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It take 32.18 seconds to learn the Lyapunov function [164].
リアプノフ関数[164]を学ぶのに32.18秒かかる。
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Having learned the Lyapunov function shown in Fig 8, a security region can be estimated, which is visualized in Fig 9.
図8で示されるリャプノフ関数を学習すると、セキュリティ領域が推定され、図9で視覚化される。
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If a disturbance leads the state vector to deviate from the equilibrium (the origin of Fig 9) while also remaining within the solid red circle in Fig 9, one can conclude immediately that the system trajectory will converge to the equilibrium without conducting any simulations.
The region in the solid blue circle is the security region estimated by a conventional approach.
固体の青い円の領域は、従来のアプローチで推定されるセキュリティ領域である。
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It can be observed in Fig 9 that the learning-based approach is much less conservative than the conventional approach, since the red-solid circle is larger than the blue circle.
Although the approach in [164] can address heterogeneous interface dynamics and can provide less conservative results than the conventional approach, it incurs large computational costs when analyzing large-scale systems.
As a result, the power grids are becoming increasingly sensitive to disturbances and impact anomalies may become more frequently observable.
その結果、電力網は乱れに敏感になり、衝突異常はより頻繁に観測されるようになる。
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Effectively monitoring and correcting these anomalies in real-time
リアルタイムにこれらの異常を効果的に監視し修正する
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Fig. 7. A neural network-structured Lyapunov function: the tunable parameter vector θ is related to weights W1 and W2 and biases b1 and b2 in the hidden and output layers.
Table V summarizes these recent works from the perspectives of data sources, methods and computation resources.
表Vは、これらの最近の研究を、データソース、メソッド、計算資源の観点から要約します。
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The following are two specific examples that address online operational challenges in the grid.
以下の2つの具体例は、グリッドにおけるオンライン運用上の課題に対処するものである。
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1) Forced oscillation localization based on robust principal component analysis (RPCA): Forced oscillations are one type of the critical phenomena that concern system operators, because these oscillations may cause large-scale blackouts and decrease the lifespans of power grid components [201].
Fig 10 illustrates the mechanism of forced oscillations.
図10は強制振動のメカニズムを示しています。
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Let us consider a power grid as a blackbox with some inputs and outputs, as shown in Fig 10.
図10に示すように、電力網を入力と出力のあるブラックボックスと考えてみましょう。
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The inputs can be thought of as setpoints of generators, while the outputs are PMU mea-
入力はジェネレータのセットポイントと考えることができ、出力はPMU meaである。
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英語（論文から抽出）
日本語訳
スコア
PROCEEDINGS OF THE IEEE 12
IEEEの成果 12
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TRI-FACTORS OF AI SOLUTIONS TO GRID SECURITY AND RESOURCE ADEQUACY ANALYSIS
グリッドセキュリティと資源充実分析のためのaiソリューションのトライファクター
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TABLE IV Task [Ref.] Reliability study for power-gas systems [151] Reliability study w/ rich PE [152] Energy Loss estimation [153] Distribution system phase identification [154] Outage scheduling [155]
Data Sources (Availability) 73-bus power system w/ 40-node gas system (N) IEEE RTS-24 bus w/ 40-node gas system (N) 33-bus distribution system w/ 40-node gas system (N) 25-bus, 123-bus, 450-bus systems (N) IEEE-RTS79; IEEE- RTS96 (N) Ind-Solar dataset; EnerNOC GreenButton Data; Solar generation from National Renewable Energy Laboratory (N) 8-, 123- IEEE; 362-node utility distribution network (N) 33-node system (Y) 123-IEEE (N)
Data Sources (Availability) 73-bus power system w/ 40-node gas system (N) IEEE RTS-24 bus w/ 40-node gas system (N) 33-bus distribution system w/ 40-node gas system (N) 25-bus, 123-bus, 450-bus systems (N) IEEE-RTS79; IEEE-RSTS96 (N) Ind-Solar dataset; EnerNOC GreenButton Data; Solar generation from National Renewable Energy Laboratory (N) 8-, 123-IEEE; 362-node utility distribution network (N) 33-node system (Y) 123-IEEE (N)
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Umass Smart data (Y) Systems from SCE, PGEC, and FortisBC (Y) ISO-NE area data (Y) 24-IEEE (N) 4,000 circuits from U.K. utilities (N)
umassスマートデータ (y) sce, pgec, fortisbc (y) iso-ne領域データ (y) 24-ieee (n) 4,000 英国ユーティリティ (n) 訳抜け防止モード: sce, pgec, umassスマートデータ(y)システム and fortisbc (y) iso - ne area data (y) 24-ieee (n) 4,000 circuits from u.k. utilities (n)
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Computation Resources - Intel CPU, 16GB RAM
計算資源 - Intel CPU、16GB RAM
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PC, 8GB RAM
PC 8GB RAM
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300-core Xeon CPUs, 2GB RAM for each
300コアのXeon CPUと2GBのRAM
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AI Method Random Forest; XGBoost Support vector regression (SVR); random forests (RF) Regression trees
ベイズ語辞書学習 i5 CPU 8GB RAM Xeon CPU E5-2687W v4、64GB RAM
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Support Matrix Regression Deep learning Neural Lyapunov method Autoencoder; semi-supervised learning Information theoretic machine learning Regression Bayes Decision Theory Decision tree
support matrix regression deep learning neural lyapunov method autoencoder; semi-supervised learning information theoretic machine learning regression bayes decision theory decision tree
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Fig. 8. A Lyapunov function learned for a grid-tied microgrid.
第9話。 セキュリティ領域(SR)と有効領域(VR):ニューラルネットワーク(NN)アプローチと従来の(cvt.)アプローチ(出典: Fig 7-a of reference [164] >IEEE 2021)
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surements. If one of the inputs varies periodically, oscillations can be observed in the PMU measurements.
確かだ 入力の1つが周期的に変化すると、PMU測定で振動が観測される。
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These oscillations are termed “the forced oscillations,” and the periodic input is called the source of the forced oscillations.
これらの振動は「強制振動」と呼ばれ、周期的な入力は強制振動の源と呼ばれる。
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Different PMU measurements have different geographical distances from the oscillation source.
異なるpmu測定では、振動源からの距離が異なる。
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The objective of the forced oscillation localization is to pinpoint which PMU measurements are close to the oscillation source, based only on the PMU data without information on the inputs and the power grid models.
Locating the oscillation source is a challenging task, because the measurement closest to the source may not exhibit the largest oscillations.
発振源の配置は、発振源に最も近い測定値が最大の発振を示さないため、難しい作業である。
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Fig 11 shows such a counter-intuitive case in which the measurement (the red curve) closest to the oscillation source does not exhibit the largest oscillation magnitude.
図11は、振動源に最も近い測定値(赤曲線)が最大の振動強度を示さないような直観的な逆の場合を示す。
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Reference [202] reports a real-world, counterintuitive case in which the distance between the source and the measurement exhibiting large oscillations is more than 1100 miles [201].
In reference [201], Huang et al formulate the forced oscillation localization as decomposing the measurement matrix Yt into a low-rank matrix Lt and a sparse matrix
Split expectation maximization Regression Task [Ref.] Cyberattack detection [165] Actuator placement [194] Frequency prediction assessment and control [166] Emergency control [196] Under-voltage load shedding [167] Transient stability prediction [168] Transient stability prediction and control [169] Generator dynamic behavior prediction [170] Voltage stability margin prediction [171] Training data preparation for transient stability prediction [172] Representative state selection for security prediction [173] Hydrostatic tidal turbine (HTT) control [174] Cyber-physical anomaly detection [175] Inverter control [176]
分割予測最大化回帰 Task [Ref.] Cyberattack detection [165] Actuator placement [194] Frequency prediction assessment and control [166] Emergency control [196] Under-voltage load shedding [167] Transient stability prediction [168] Transient stability prediction and control [169] Generator dynamic behavior prediction [170] Voltage stability margin prediction [171] Training data preparation for transient stability prediction [172] Representative state selection for security prediction [173] Hydrostatic tidal turbine (HTT) control [174] Cyber-physical anomaly detection [175] Inverter control [176] 訳抜け防止モード: 分割予測最大化回帰 タスク [リフ ] サイバー攻撃検出 [165 ] アクチュエータ配置 [194 ] 周波数予測評価 and control [166 ] emergency control [196 ] Under- voltage load shedding [167 ] Transient stability prediction [168 ] Transient stability prediction [196 ] 制御[169 ] 発電機の動的挙動予測[170 ] 電圧安定率予測[171 ] 過渡安定度予測[172 ] セキュリティ予測[173 ] 静水圧潮流タービン(HTT)制御[174 ] サイバー-物理的異常検出[175 ] インバータ制御[176 ]
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Transient stability prediction [177] Line outage detection [178] Dynamic security prediction [179] Distribution system state estimation [180] Local control design for active distribution grids [181] Anomaly detection, localization and classification [195] Volt-VAR optimization [197] Cyber anomaly detection [182] Faulted line localization [183] Feature extraction for Security assessment [184] Building Energy Optimization [198] PV frequency control [185]
118-NREL; Taiwan Power Systems (N) 39-IEEE; 2417-bus GD Power Grid in South China (N) 118-IEEE (N)
118-NREL, 台湾電力システム(N)39-IEEE, 2417-bus GD Power Grid in South China(N)118-IEEE(N)
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HTT simscape model (N) HiL test of Kundur two area system (N) 123-IEEE (N) 39-IEEE; 2417-bus GD Power Grid in South China (N) 30-, 118-, 300- IEEE (N) 39-IEEE (N) 37-IEEE (N) Typical European radial LV grid (N)
HTT simscape model (N) HiL test of Kundur two area system (N) 123-IEEE (N) 39-IEEE; 2417-bus GD Power Grid in South China (N) 30-, 118-, 300-IEEE (N) 39-IEEE (N) 37-IEEE (N) typical European radial LV grid (N)
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14-, 39- IEEE (N) 13-, 123- IEEE (N) 39-IEEE (N) 39-, 68- IEEE (N) 118-IEEE (N) Real-world data from Pecan Street Inc. (N) 6,102-bus system; HiL tests (N)
14-,39-IEEE (N) 13-,123-IEEE (N) 39-,68-IEEE (N) 118-IEEE (N) Pecan Street Inc. (N) 6,102-bus system; HiL test (N)
min St (5) where (cid:107) · (cid:107)∗ and (cid:107) · (cid:107)1 denote the nuclear norm and l1 norm, respectively, Yt represents a measurement matrix up to time t where each row of the matrix represents a time series from one PMU, and St is the corresponding approximate sparse matrix.
However, when the RPCA can exactly locate the true source remains an open-ended question.
しかし、RPCAが真のソースを正確に見つけることができれば、未解決の疑問が残る。
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Fig. 11. Voltage deviations in a counter-intuitive case: the red curve is the voltage deviation closest to the oscillation source; the black curves are other voltage deviation measurements.
2) Reinforcement learning (RL)-based protection scheme for renewable-rich distribution systems: The conventional protection paradigm in distribution systems has been challenged by the increasing amount of DERs.
However, if a DER is installed nearby, it may decrease the fault current by injecting reverse power flow.
しかし、DERが近傍に設置されている場合、逆流を注入することで故障電流を低減することができる。
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As a consequence, the current under the faulty condition might be much less than the relay threshold.
その結果、故障状態の電流はリレー閾値よりもはるかに小さい可能性がある。
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In order to address the protection challenges in a renewablerich distribution system, reference [204] places the protection problem into a RL framework (Fig. 14) in which the protection scheme is learned by interacting with a distribution system simulator.
In the RL framework, the distribution system is modeled by a Markov decision process (MDP) described by states s ∈ S, actions a ∈ A, a reward function r(s, a), transition probability P , and a user-defined discount factor
RLフレームワークでは、分布系は状態 s ∈ S, アクション a ∈ A, 報酬関数 r(s, a), 遷移確率 P, ユーザ定義割引因子によって記述されたマルコフ決定過程(MDP)によってモデル化される。
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β ∈ (0, 1].
β ∈ (0, 1].
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The implication of the states, action, and reward function in the protection problem are annotated in Figure 13.
保護問題における状態、行動、報酬関数の影響を図13に記す。
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In particular, the state si,t and action ai,t of relay i at time t are defined by
特に、時刻 t におけるリレー i の状態 si,t とアクション ai,t は、
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i,t}, i,t, sd i,t }, i,t, areset
i,t}, i,t, sd i,t }, i,t, areset
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si,t = {sc ai,t = {aset
si,t = {sc ai,t = {aset} である。
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i,t, sb i,t, ad
I、T、SB、T、広告
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i,t represents local current measurements, sb
i,tは局所電流の測定値 sbを表します
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(6a) (6b) where sc i,t represents the status of the local breaker, sc i,t represents the value of the countdown timer, aset i,t represents the action of triggering the countdown timer, ad i,t represents the action of decreasing the value of the counter by one, and areset represents the action of i,t resetting the counter.
The reward function gives deterministic positive rewards to the tripping action under fault conditions and stay-in-silence action under normal condition, and it gives negative rewards to malfunctions.
The transition probability is determined by the distribution system; in practice it is unknown.
遷移確率は分布系によって決定されるが、実際には未知である。
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The optimal action a∗(s) at state s is obtained by
状態 s における最適作用 a∗(s) を得る。
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Q(s, a) = E a∗(s) = arg max
Q(s, a) = E a∗(s) = arg max
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(cid:18) a(cid:48)∈A Q(s, a(cid:48)),
(cid:18) a(cid:48)・A Q(s, a(cid:48))
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r(s, a) + β max
r(s, a) + β max
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(cid:19) a(cid:48)∈A Q(s(cid:48), a(cid:48))
(cid:19) a(cid:48) ajaxa q(s(cid:48), a(cid:48))
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, (7a) (7b) where E(·) is the expectation operator; a(cid:48) is the possible nextstep action; and s(cid:48) is the next-step state given the current state and action; it is determined by the distribution system.
In [204], the Q function in (7) is approximated by an NN.
204]では、(7)におけるQ関数はNNによって近似される。
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The NN’s parameters are learned by a sequence of {s, a, r, s(cid:48)} observations from the framework shown in Fig 14.
nnのパラメータは、図14に示すフレームワークから{s, a, r, s(cid:48)}の一連の観測によって学習される。
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The dataset reported in [205] can be used for training the algorithm.
205]で報告されたデータセットは、アルゴリズムのトレーニングに使用できる。
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The simulation results in [204] suggests that the failure rate of the RL-based relay is only 0.32% in a distribution system with 30% DER penetration, whereas the conventional overcurrent relay has a much higher failure rate, i.e., 15.46%, under the same condition.
その結果、[204]におけるシミュレーションの結果、RL系リレーの故障率は、30% DER の浸透を伴う分布系では0.32%に過ぎず、従来のオーバーカレントリレーは15.46%の故障率である。
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One future direction of this work is to investigate a rigorous convergence guarantee for the sequential reinforcement learning algorithm [204].
本研究の今後の方向性は,逐次強化学習アルゴリズム[204]の厳密な収束保証を検討することである。
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To summarize this section, we provide two-fold guidance on applying use-inspired AI methods in power systems.
本節を要約し,電力系統におけるai手法の適用について,2段階のガイダンスを提供する。
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First, it is critical to find appropriate application scenarios that take precedence over proposing innovative methodology.
まず、革新的な方法論の提案よりも優先する適切なアプリケーションシナリオを見つけることが重要です。
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With deep neural networks as representatives, current AI techniques that are essentially model-agnostic function approximators usually present outperforming performance in application scenarios where there is only heuristic experience with no clear firstprinciple physical model, such as in load and renewable prediction.
ディープニューラルネットワークを代表とする、基本的にモデルに依存しない関数近似器である現在のAI技術は、通常、負荷や再生可能予測のような明確な第一原理の物理モデルを持たないヒューリスティックな経験しか持たないアプリケーションシナリオにおいて、優れたパフォーマンスを示す。 訳抜け防止モード: ディープニューラルネットワークを代表とする現在のAI技術 is agnostic function approximator 通常、アプリケーションのシナリオではパフォーマンスが優れています。 過負荷や再生可能予測のような 明確な第一原理の物理モデルがない ヒューリスティックな経験があるだけだ
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The illustrated neural network-based Lyapunov function [164] is another example.
ニューラルネットワークに基づくリアプノフ関数 [164] も例である。
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Although a Lyapunov function itself has rigorous definition, there is no traditional cost-effective analytical or numerical way to construct such a function for a large-scale real-world dynamical system, in which neural networks can provide an alternative effective solution.
Fig. 12. Illustration of the RPCA-based source localization algorithm:
第12話。 RPCAに基づくソースローカライゼーションアルゴリズムの図解
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(a) the measurement matrix; (b) the low-rank matrix; and
a) 測定行列 (b)低ランク行列,及び
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(c) the sparse matrix.
(c)スパース行列。
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The (normalized) magnitudes of matrix entries are color-coded.The measurement closest to the source can be tracked by identifying the largest absolute entry in the sparse matrix, i.e., the entry with the brightest color.
. that enable it to meet and exceed customer expectations.
. 顧客からの期待を 越えることができます
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This section presents some industry use cases to illustrate the continuing adoption of machine learning techniques by Oncor, a regulated utility that operates operates the largest distribution and transmission system in Texas.
The following use cases were selected to show instances of AI adoption with relatively high maturity.
比較的成熟度の高いAI導入例を示すために、以下のユースケースが選択された。
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In addition, we illustrate use cases (e g , asset management) that are not considered in powersystems research, but that are essential for business operations with physical devices spread over large distances.
All use case development is based on business needs and the value of the investment must be justified before a use case is developed, even if data are readily available.
Moreover, the value-add of some high performance algorithms in many cases may not offset the maintenance cost required to keep such models operating properly (e g , due to model drift).
In many industry use-cases, the methods currently used may appear simplistic compared to the latest research; however, these use-cases are of high value, and large amounts of data are readily available.
Utilities usually have multiple databases for various systems, such as outage management, advanced metering, work orders, geographical and meteorological data, and financial info.
At Oncor, a datalake was created to consolidate the data needed for analytics.
oncorでは、分析に必要なデータを統合するためのデータレイクが開発された。
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The datalake replicates data from all of Oncor’s operational databases.
datalakeは、oncorのすべての運用データベースからデータを複製する。
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In addition to supporting uniformity, this approach also minimizes stress on operational databases because they are accessed only during each scheduled copy rather than whenever an analyst makes a query.
As the industry continues to adopt machine learning continues, and available platforms become more mature, advanced techniques will be more feasible at lower cost; these will be necessary to address more complex problems in power systems.
Fig. 13. The conventional threshold-based protection scheme may fail due to low fault current.
第13話。 従来のしきい値に基づく保護スキームは、低電流のため失敗する可能性がある。
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(Modified from source: Fig 1 of [204])
(資料より変更:[204]第1図)
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Fig. 14. Obtaining the Q function for the RL-based relay: the optimal policy embedded in the RL-based relay is obtained by interacting with a distribution system simulator.
However, formulated as a matrix decomposition problem, this problem can be solved by RPCA that is commonly used for image processing, which has both outperforming accuracy and explainability.
As more measurement data and data-driven algorithms become available, the power industry continues to adapt and improve operations by leveraging new technology and systems
TimeCurrent magnitudeFaulty conditionOvercurrent pickup thresholdActual fault currentDelay counterEMTP simulatorDistributio n gridLine trippingRL-based relayActionCounter settingStatesCurrent measurements, breaker status, and counterdowntimer valueRewardAssessmen t based on the consequence by the relay’s action
時間電流 マグニチュード オーバーカレント ピックアップ しきい値 電流 デレイ カウンターemtp シミュレータ 分散グリッドライン trippingrl-based relayactioncounter settingstatescurrent measurement, breaker status, counterdowntimer valuerewardassesment s based on the result by the relay's action
For all utility companies, monitoring and maintaining their assets is critical to realizing system reliability and providing the highest quality service to their customers.
For assets where digital measurements are not available, health monitoring may be possible by analyzing asset images using advanced image processing techniques.
Several Oncor use cases are presented below to illustrate how asset health can be monitored by utilizing machine learning methods.
以下に、機械学習手法を用いて資産の健全性を監視する方法について説明する。
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As the largest utility company in the state of Texas, Oncor provides power to nearly 4 million customers through more than 1 million distribution class transformers, which can fail from damaged coils or overload degradation.
The two horizontal lines are the upper and lower limits of the operating voltage ratings defined by the American National Standards Institute (ANSI C84.1-2020), which are ±5% of the nominal voltage.
On June 24th, 2018 the voltage suddenly rose above the upper limit due to a damaged coil on the primary side of the transformer.
2018年6月24日、変圧器の一次側にあるコイルの損傷により、電圧が突然上限を上回った。
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The sudden drop in voltage on July 18th, 2018 denotes the time of the replacement.
2018年7月18日の突然の電圧低下は、交換時期を示している。
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Typically, a transformer will not fail immediately after a coil is damaged.
通常、コイルが損傷した直後に変圧器は故障しない。
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Therefore, proactive replacement is realistic and valuable if a change in voltage can be detected soon enough.
したがって、電圧の変化を十分早く検出できれば、積極的に置換することは現実的かつ価値がある。
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After examining the pre-outage voltage profiles of all transformers replaced in Oncor’s system during an 18-month period, a change point detection algorithm was designed to detect over/under voltage issues.
Several post-processing steps were implemented to remove change points due to outages or temporary voltage changes.
停止や一時的な電圧変更による変更点を取り除くために、いくつかの後処理ステップが実施された。
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The thresholds for these steps were selected from the ground truth data.
これらのステップのしきい値は、真理データから選択された。
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Based on the number of issues seen on the same feeder, the detected issues were then categorized into various types, such as meter, transformer, or regulation issues, to enhance
Proactive transformer maintenance has saved Oncor approximately $3.25 million in equipment, labor and expenses as well as 5.5 million customer interruption minutes.
Defective insulators are hazardous to the operation of power lines and pose a risk to system reliability.
欠陥絶縁体は電力線の運転に危険であり、システムの信頼性に危険をもたらす。
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Oncor has more than 18, 000 circuit-miles of transmission lines with over 500, 000 transmission insulators.
Oncorには18,000の伝送路があり、50,000以上の絶縁体がある。
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Rapid identification of damaged insulators, especially after a storm, is therefore a critical task in asset management.
損傷した絶縁体、特に嵐後の迅速な識別は、資産管理において重要な課題である。
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Due to the scale of Oncor’s transmission system, manual inspection is infeasible.
oncorのトランスミッションシステムの規模のため、手動での検査は不可能である。
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An automated inspection method was developed that use aerial/drone images of transmission lines and convolutional neural networks.
送電線の空中/ドローン画像と畳み込みニューラルネットワークを用いた自動検査手法を開発した。
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The insulator defect detection method employs YOLOv3 (You Only Look Once, Version 3 [9]), which is a real time object detection model that uses Darknet-53 [207] as the backbone feature extractor in a deep convolutional neural network.
The model was initialized with YOLO’s pre-trained weights using the Microsoft COCO (Common Objects in Context) dataset [208] and insulator images, provided by Electric Power
モデルは、Microsoft COCO(Common Objects in Context)データセット[208]とElectric Powerが提供する絶縁体イメージを使用して、YOLOのトレーニング済み重量で初期化された。
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Research Institute (EPRI), were used for transfer learning and validation (confidential data).
研究機関(EPRI)は、転送学習と検証(機密データ)に使用された。
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The defect detector successfully recognized the insulators in an image, pinpointed those issues of each damaged insulator, and classified the issues as either “broken” or “flashed.”
For the 50 testing images, each containing multiple flashed/broken locations, 100% of the broken points were detected correctly and 90% of the flashed points were detected.
The recent Texas House Bill 4150, also known as the “William Thomas Heath Power Line Safety Act,” which was passed through the Legislature in May 2019, requires all utilities to make regular inspections of their power lines to ensure that they comply with state and federal safety regulations.
2019年5月に議会を通過した「ウィリアム・トーマス・ヒース・パワーライン安全法」(william thomas heath power line safety act)としても知られるテキサス州下院法案4150は、州や連邦政府の安全規則に準拠するように電力線を定期的な検査を義務付けている。
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Although Oncor completes routine inspections of all transmission power lines, detailed manual inspections of all structures are time consuming, impactful to land owners and costly.
The first stage of this model requires Oncor to verify all structure asset information in the Oncor Transmission Information System.
このモデルの最初の段階では、Oncor は Oncor Transmission Information System のすべての構造資産情報を検証する必要がある。
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Because many transmission lines are 40+ years old, information in historical records may be inaccurate for structures where components were replaced or added after the initial installation.
These attributes include • Composition: wood, steel, concrete • Design: H-frame, A-frame,
これらの属性には • 構成:木、鋼、コンクリート • 設計:hフレーム、aフレーム、
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lattice tower, multi-pole,
格子塔 マルチポール
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single-pole • Cross arm: beam, double-plank • Brace: V, X, knee The effort to classify transmission line attributes made use of YOLOv3; the initial results were promising, with accuracy rates of 89% for braces and 87% for cross arms.
Fig 16 and Fig 17 show several examples of successful classification results.
図16と図17は、成功した分類結果のいくつかの例を示している。
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As more images are labeled to augment training data, the model’s performance is expected to improve; furthermore, by including images with defective structures, the system can be used to inventory components as well as their degradation levels.
B. Load Forecasting Load forecasting is an essential building block in operating and planning tasks in both the power industry [209] and commercial building energy [210].
It is needed in many decision making processes for electric energy generation, DERs management, transmission, distribution, markets, and demandresponse.
電力発電、DER管理、送電、流通、市場、需要に対する多くの意思決定プロセスにおいて必要である。 訳抜け防止モード: エネルギー発生のための多くの意思決定プロセスにおいて必要である。 DERs Management, Transmission, Distribution, market, and demandresponse.
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The pursuit of models that can achieve accurate load forecasts for short-, mid-, and/or long-term purposes is a long standing research area with a large body of literature [211], [212].
In some cases, a contingency plan will be made ahead of these peak seasons for feeders that are at risk of overload based on historical load data leveraged by analytics.
Switching operations, however, are a major challenge for feeder load forecasting because a feeder’s load can change significantly due to a load switching event (e g feeder reconfiguration due to an outage or planned maintenance).
With a large quantity of distribution transformers (e g , more that 1 million in Oncor’s system), if computational power is limited, cluster analysis can be used to group transformers with similar load behaviors.
Normalization (re-scaling each load profile to range [0, 1]) is needed before clustering so that the clustering results are affected mainly by the shape of the load profiles.
After the transformers have been assigned into clusters, load forecasts for each cluster center (the representative of all transformers in that cluster) can be
This increase in load is referred to as ”cold load”.
この負荷の増加は、"コールドロード"と呼ばれる。
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After some time period passes, the diversity will be restored because the unit run times will vary depending on factors such as HVAC rating, home size, and temperature setpoints.
Cold load peak values are affected by pre-outage load behavior, season (winter/summer), time of day, ambient temperature, and load composition (customer types).
Predicting these values at feeder breakers or other downstream protective devices enables optimal sequencing of operations to restore power quickly while minimizing the likelihood of damaging equipment.
In addition, EMS typically has a load shed/restoration tool that can automatically conduct outage rotations among all feeders in the system during a short supply situation such as the recent Texas power crisis [70].
With predictions of each feeder’s postoutage load peaks, the EMS can automatically and accurately follow ISO’s load-shed requirements to protect the entire power grid.
Oncor is currently testing a linear regression model
Oncorは現在、線形回帰モデルをテスト中
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to predict the ratio of the peak cold-load (post-outage) and preoutage load of a feeder.
給油機のピーク冷負荷(停電後)とプレアウト負荷の比率を予測する。
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The data used are outage duration, preand post-outage temperatures, and the fraction of residential customers on the feeder.
使用したデータは、停止期間、前および後温度、およびフィーダー上の住宅顧客の割合である。
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The residential load fraction is a good proxy for feeder load diversity (i.e., the independently controlled cyclic loads such as HVAC systems that may be energized at any given time during normal operating conditions).
Since feeder breaker level outages are relatively rare, feeders are grouped by their residential fractions and a model is learned for each feeder-group.
A total of 1127 breakers were evaluated and training data were collected for fitting the regression model.
計1127名のブレーカーが評価され,回帰モデルに適合するトレーニングデータが収集された。
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To accurately capture the cold load behavior, switch operation logs and fuse level events were reviewed to ensure that the cold load peaks were neither overestimated due to switching operations nor underestimated due to fuse level events behind the breakers.
The predicted value is marked at the same location as the post-outage load peak only for better visualization and easier comparison.
予測値は、より良い視覚化と簡単な比較のためにのみ、障害後の負荷ピークと同じ位置にマークされる。
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C. Residential/Industri al/Commercial (RIC) Categorization
C.住宅・産業・商業(RIC)分類
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For many transmission and distribution planning models, RIC percentages at each substation transformer bank are used to allocate load in the base-case models.
These percentages are also used to derive the number of various motor types
これらの割合は、各種モータの数を導出するためにも用いられる。
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Fig. 18. An example of transformer load forecast results.
第18話。 変圧器負荷予測結果の例。
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Blue dots: actual measurements; red dots: predicted values.
青い点:実測値、赤い点:予測値。
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Top figure: predicted and actual loads; bottom figure: predicted and actual temperatures.
トップフィギュア: 予測と実際の負荷、ボトムフィギュア: 予測と実際の温度。
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obtained; they are then scaled back to each transformer’s load level by undoing the normalization steps.
次に、正規化ステップを解除することで、各トランスの負荷レベルにスケールバックされる。
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If distributed computing platforms are available, transformer load forecasting can be conducted by directly training individual models for every transformer, which will introduce fewer errors.
The load of a transformer is affected by both numerical and categorical factors.
変圧器の荷重は数値的要因とカテゴリー的要因の両方に影響される。
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The most important numerical factors include temperature, wind speed, humidity, and solar radiation, whereas categorical factors include time of day, day of week, month, etc.
To avoid over-fitting, the maximum numbers of layers and leaves were tuned based on model performance.
過剰フィッティングを避けるために,モデル性能に基づいて最大層数と葉数を調整した。
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Fig. 18 shows an example of the hourly load forecasting results for one distribution transformer over the course of 3 days.
第18図は、1つの配電変圧器の3日間の時間負荷予測結果の例である。
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The blue and red curves on the top plot give the actual and predicted load based on the predicted temperatures in the bottom plot (blue curve) using a regression tree model trained for a particular transformer.
There is a trade-off between model performance (error level) and computing time, which can be calibrated to suit shifting business needs at any given time.
The uncertainty in the exogenous factors must be accounted for in the final forecast, and because several of those factors are forecasts themselves, errors can be large.
The accuracy will be reduced during time of extreme cold or heat due to the lack of historical meter data.
過去のメータデータの欠如により、極端な寒さや暑さの時期に精度が低下する。
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A special case in load forecasting is cold load characterization.
負荷予測における特別なケースは、コールドロード特性である。
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During steady state, the heating or cooling load on a feeder is typically a smaller percentage of the total heating or cooling load.
定常状態では、供給装置の加熱または冷却負荷は、通常、全加熱または冷却負荷のより小さい割合である。
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This reduced load results from the diversity of HVAC (heating, ventilation, and air conditioning) units simultaneously running due to normal cycling between on and off.
After an extended outage the temperature in the residence
住宅の温度を延ばした後
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were needed for a cluster with 2 namenodes (dual Xeon-4208, 768 GB RAM per node) and 8 datanodes (dual Xeon-5218, 768 GB RAM per node) as shown in Table VI.
2つのネームノード(dual xeon-4208, 768 gb ram per node)と8つのデータノード(dual xeon-5218, 768 gb ram per node)を持つクラスタが必要だった。
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The analysis will be repeated annually to capture any premises with changes in load type.
Many companies in the power industry have been developing data-driven methods for their business needs.
電力業界の多くの企業は、ビジネスニーズに対してデータ駆動の手法を開発してきた。
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Exelon Utility and ComEd applied classification methods to aerial/satellite images as well as light detection and ranging (LiDAR) data for vegetation management to better understand the system’s tree trimming workload in the system seeking to cut rimming costs while reducing the number of tree-related outages [216], [217].
ISO New England proposed a prediction method based on decision tree to instruct interface limit values for different operating conditions [218].
ISO New Englandは、異なる動作条件のインタフェース制限値を指示する決定木に基づく予測手法を提案した[218]。
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Researchers in Hitachi proposed a three-layer wind power prediction model based on the data from historical power measurements and numerical weather prediction tools [219].
VII. OUTLOOK In this paper, we have briefly reviewed the structure of today’s AI power system physical and market operation, infrastructure of data acquisition and computation in power systems, state-of-the-art AI-based approaches for multiple critical functions, and industrial use cases of AI methods.
In the following, we propose several research directions from the aspects of data, computing and AI algorithms.
以下に、データ、コンピューティング、AIアルゴリズムの各側面から、いくつかの研究方向を提案する。
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A. High-quality Open-source Datasets
A.高品質オープンソースデータセット
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in contrast Despite the advances in data acquisition,
対照的に データ取得の進歩にもかかわらず
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to numerous datasets that have benefited broad AI communities, the lack of publicly accessible high-quality power datasets may be impeding the advancement of AI research in power systems.
There are several reasons for the limited public access to power datasets.
電力データセットへのパブリックアクセスが制限されている理由はいくつかある。
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First, most real-world operational data are protected by policies such as CEII in the interest of confidentiality.
第一に、現実世界の運用データは機密性に関心のあるCEIIのようなポリシーによって保護されている。
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Second, due to the reliability of real-world power grids, the rairty of opportunities to observe high-impact events may produce an insufficiently robust real-world measurement dataset.
There have been few open-source datasets [220], [221] and online contests dedicated to topics such as forced oscillation localization [222] and power system operation [223], [224].
However, far more will be needed to build a standard library of open-source benchmark datasets along with critical tasks in clear mathematical formulation that can be used to train, calibrate, test and benchmark data-driven models.
One critical challenge is that commonly used random sampling and data generation methods do not guarantee representativeness [44] and may introduce unexpected biases into subsequent data-drive methods.
Fig. 19. An example of the cold load peek forecasting result for one feeder.
第19話。 1つのフィードのコールドロードピーク予測結果の例。
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Orange curve: SCADA current time series data before and after a feeder level outage; grey points: pre-outage current reading and predicted post-outage current.
Likewise, distribution planners must sometimes perform weather corrections for load projections.
同様に、分配プランナーは時として負荷予測のための天気補正を行う必要がある。
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In these cases, industrial and other nonweather-sensitive loads (such as water pumping and/or oil field pumping loads) are not weather-corrected, because these load types are rarely weather-sensitive or weather-dependent.
Traditionally, the customer category of a premise is established at the creation of the premise and may not get updated when the customer type changes.
伝統的に、前提の顧客カテゴリは前提の作成時に確立され、顧客タイプが変化しても更新されない。
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For example, a commercial building can be leased to a new business that has a completely different load profile from that of the previous business, but the utility may not be aware of the change.
Before system-wide installation of advanced meters, the RIC process used typical summer and winter hourly load profiles for each category of the building distribution feeder models.
With the availability of AMI interval data and distributed computing, the process can be improved by directly analyzing the load profile of each premise.
Domain experts selected 12 weeks (non-contiguous) over a 1-year period that adequately covered different seasonal and holiday effects (e g , extended hours during holidays).
The 15 minute interval load data was collected from each week and the timeseries for each meter were stacked into 8064-dimensional vectors (12×7×24×4).
K-means clustering was applied to the data with, initially, k = 100.
k-meansクラスタリングは、まず、k = 100でデータに適用された。
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The initial parameter values were chosen as subject matter experts’ estimation.
初期パラメータ値は課題の専門家の推定値として選択された。
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Subsequently, large clusters were checked by comparison of random samples within the cluster to the cluster center (i.e., comparing the average load profile with the other profiles within the cluster).
If a large deviation was found, then the cluster was split.
大きな偏差が見つかった場合、クラスターは分裂した。
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A less heuristic approach would be to use the V -measure or silhouette-coefficient to determine an optimal number of clusters [214], [215]; however, cluster-splitting was found to be effective for this use case.
The rapid expansion of sensors has enabled massive data acquisition; however, although this data is necessary for realizing a digitized power grid, using all of it is beyond current computing capacity for centralized methods.
Therefore, to explore and exploit advanced algorithms and massive streaming data, hybrid edge and cloud computing are necessary to dynamically balance the computational-load and escalate computing power as needed.
For example, edge devices can compute partial results across several hundred sensors (e g , half of a neural network’s layers) and forward the results to the control center for final computations, effectively distributing computational load.
Furthermore, new ASIC devices, dedicated to power system computations could be used in edge devices for real-time data processing and to accelerate simulations.
In addition, communications between edge and cloud may contain sensitive information, requiring privacy preserving methods such as federated learning [110].
Besides accelerating computation, platforms are needed to manage the complexity introduced by digitization.
計算の高速化に加えて、デジタル化によってもたらされる複雑さを管理するプラットフォームも必要である。
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The software development industry uses a set of (automation) practices called “DevOps” to manage development, integration, testing, deployment, and monitoring of distributed software systems.
In sectors where data-driven and machine learning algorithms are used, another layer is added to DevOps [225], [226] that encompasses automated training, testing, deployment, and monitoring of models—this is called “MLOps” [227], [228].
For efficient digitization of the power grid, both DevOps and MLOps will be necessary; however, there are unique aspects of power systems that require investigation.
The instrumentation and sensors being deployed into modern grids also bring cyber-security challenges.
現代のグリッドに配置される機器とセンサーは、サイバーセキュリティの課題も引き起こす。
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If the data and contols are transmitted over the internet (e g , cloud computing), the grid is vulnerable to the same cyber-attacks as a website, except the stakes are much higher: outages, energy theft, and loss of private data.
Monitoring and detecting cyberthreats to the grid is an important area for cross disciplinary research combining power systems, cyber-security, and AI.
C. Use-inspired AI Methods for Practical Applications
c. 実用的利用のためのai手法
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Because power grids are large-scale critical infrastructure systems for human society, future research efforts ought to use-inspired AI algorithms that possess three key properties, namely interpretability, robustness, and scalability, aiming to
First, AI algorithms ought to be explainable by first-principle-based physical models, because only interpretable algorithms are acceptable for participation in the human-in-the-loop decision making process.
まず第一に、AIアルゴリズムは第一原理に基づく物理モデルによって説明可能である必要がある。
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In particular, interpretable AI approaches should provide clear causal inference for the purposes of real-time monitoring, control and diagnosis, such as identifying root cause of complex observations.
Preliminary efforts have been devoted to physicsinformed ML as summarized in [229].
[229]で要約したように、物理インフォームドMLに予備的な取り組みが注がれている。
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The principle is to steer the learning process towards identifying physically consistent solutions, of which instructive guidance contains three aspects, namely data processing, loss function modification, and model architecture design.
For example, incorporating ordinary different equation (ODE) formats into loss function as regularization terms can improve the performance of system identification algorithms based on transient data or improve the fidelity of transient data generation methods.
Particularly, the robustness to perturbation is critically important for reinforcement learningbased algorithms for decision making.
特に、摂動に対する堅牢性は、意思決定のための強化学習に基づくアルゴリズムにとって極めて重要である。
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Meta reinforcement learning [230], [231] and transfer learning can potentially accommodate the gap between reality and simulation environment, thereby rendering the decision making adaptive to varying conditions and scenarios.
Third, another highly desirable feature of AI algorithms is scalability, which refers to adequate effectiveness and efficiency in large-scale real-world systems.
The concern regarding scalability arises from the aforementioned observation that the performance of existing AI algorithms in the power system domain is mostly demonstrated by small-scale grids without validation in large-scale cases.
As high-dimensional measurements in power systems empirically have properties such as approximate low-rankness and sparsity, they may be potentially efficacious to discover intrinsic low-dimensional manifolds and linear coordinates in data structure [232].
In summary, digitization of the power grid will play a major role in transforming the electricity sector into a decarbonized system while simultaneously improving grid reliability.
The synergy of high-dimensional dynamic data, increased computing power, and use-inspired AI algorithms, will enable improvements to the reliability and operational efficiency of the power grid at multiple scales.
Strong collaboration between industry and academia will be crucial for the successful adoption of use-inspired AI methods in a decarbonized power system.
[4] J. Rogelj, D. Shindell, K. Jiang, S. Fifita, P. Forster, V. Ginzburg, C. Handa, H. Kheshgi, S. Kobayashi, E. Kriegler et al , “Mitigation pathways compatible with 1.5 c in the context of sustainable development,” in Global warming of 1.5 C. Intergovernmental Panel on Climate Change, 2018, pp. 93–174.
4]j. rogelj, d. shindell, k. jiang, s. fifita, p. forster, v. ginzburg, c. handa, h. kheshgi, s. kobayashi, e. kriegler et al , “持続可能な発展の文脈において1.5cと互換性のある緩和経路” 1.5 c. 気候変動に関する政府間パネル, 2018, pp. 93–174。
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英語（論文から抽出）
日本語訳
スコア
PROCEEDINGS OF THE IEEE 24
IEEEの成果 24
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36, no. 3, pp. 1725–1735, 2021.
36, no. 3, pp. 1725–1735, 2021。
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[145] S. Pineda, J. M. Morales, and A. Jim´enez-Cordero, “Data-driven screening of network constraints for unit commitment,” IEEE Transactions on Power Systems, vol.
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35, no. 5, pp. 3695–3705, 2020.
35, No. 5, pp. 3695–3705, 2020。
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[146] L. Liu and Z. Hu, “Data-driven regulation reserve capacity determination based on bayes theorem,” IEEE Transactions on Power Systems, vol.
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35, no. 2, pp. 1646–1649, 2020.
35, No. 2, pp. 1646–1649, 2020。
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[147] F. Safdarian, A. Kargarian, and F. Hasan, “Multiclass learning-aided temporal decomposition and distributed optimization for power systems,” IEEE Transactions on Power Systems, vol.
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36, no. 6, pp. 4941– 4952, 2021.
36, no. 6, pp. 4941– 4952, 2021。
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35, no. 4, pp. 3040-3050, 2020。
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36, no. 3, pp. 1998–2009, 2021.
36, No. 3, pp. 1998-2009, 2021。
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10, no. 3, pp. 1025–1033, 2019.
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[154] Z. S. Hosseini, A. Khodaei, and A. Paaso, “Machine learning-enabled distribution network phase identification,” IEEE Transactions on Power Systems, vol.
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36, no. 2, pp. 842–850, 2021.
36, no. 2, pp. 842–850, 2021。
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34, no. 4, pp. 2528–2540, 2019.
34, no. 4, pp. 2528-2540, 2019。
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[157] J. Yuan and Y. Weng, “Support matrix regression for learning power flow in distribution grid with unobservability,” IEEE Transactions on Power Systems, pp. 1–1, 2021.
ieee transactions on power systems, pp. 1–1, 2021. [157] j. yuan, y. weng, and y. weng, “観測不能な分散グリッドにおける電力の流れを学習するための行列回帰をサポートする。 訳抜け防止モード: [157 ]J. Yuan, Y. Weng, “可観測性のない配電系統における電力フロー学習のための行列回帰支援” IEEE Transactions on Power Systems, pp. 1-1, 2021。
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36, no. 2, pp. 1239–1249, 2021.
36, no. 2, pp. 1239–1249, 2021。
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[160] B. Foggo and N. Yu, “Improving supervised phase identification through the theory of information losses,” IEEE Transactions on Smart Grid, vol.
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11, no. 3, pp. 2337–2346, 2020.
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[161] W. Gao and D. Gorinevsky, “Probabilistic modeling for optimization of resource mix with variable generation and storage,” IEEE Transactions on Power Systems, vol.
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35, no. 5, pp. 4036–4045, 2020.
35, no. 5, pp. 4036-4045, 2020。
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36, no. 6, pp. 4906-4914, 2021。
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[163] M. Mokhtar, V. Robu, D. Flynn, C. Higgins, J. Whyte, C. Loughran, and F. Fulton, “Automating the verification of the low voltage network cables and topologies,” IEEE Transactions on Smart Grid, vol.
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11, no. 2, pp. 1657–1666, 2020.
11、no. 2, pp. 1657-1666、2020。
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[164] T. Huang, S. Gao, and L. Xie, “A neural Lyapunov approach to transient stability assessment of power electronics-interfac ed networked microgrids,” IEEE Transactions on Smart Grid, pp. 1–1, 2021.
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11, No. 2, pp. 1805– 1808, 2020。
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34, no. 6, pp. 4557–4568, 2019.
34, no. 6, pp. 4557-4568, 2019。
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[167] L. Zhu and Y. Luo, “Deep feedback learning based predictive control for power system undervoltage load shedding,” IEEE Transactions on Power Systems, vol.
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36, no. 4, pp. 3349–3361, 2021.
36, no. 4, pp. 3349-3361, 2021。
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[168] L. Zhu, D. J. Hill, and C. Lu, “Hierarchical deep learning machine for power system online transient stability prediction,” IEEE Transactions on Power Systems, vol.
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35, no. 3, pp. 2399–2411, 2020.
35, No. 3, pp. 2399–2411, 2020。
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[169] J. L. Cremer, I. Konstantelos, S. H. Tindemans, and G. Strbac, “Datadriven power system operation: Exploring the balance between cost and risk,” IEEE Transactions on Power Systems, vol.
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34, no. 1, pp. 791–801, 2019.
34, no. 1, pp. 791-801, 2019。
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[170] S. M. Mazhari, B. Khorramdel, C. Y. Chung,
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I. Kamwa, and D. Novosel, “A simulation-based classification approach for online prediction of generator dynamic behavior under multiple large disturbances,” IEEE Transactions on Power Systems, vol.
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36, no. 4, pp. 3790-3793, 2021。
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[173] I. Konstantelos, M. Sun, S. H. Tindemans, S. Issad, P. Panciatici, and G. Strbac, “Using vine copulas to generate representative system states for machine learning,” IEEE Transactions on Power Systems, vol.
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34, no. 1, pp. 225–235, 2019.
34, No. 1, pp. 225-235, 2019。
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PDF 2021年 J. Zhao氏、S. Maslennikov氏、E. Litvinov氏、X. Geng氏は、2018年のIEEE Power & Energy Society General Meeting (PESGM)で、"機械学習技術を使用した高度なトランスミッション操作ガイド作成フレームワーク"について述べている。 訳抜け防止モード: PDF 2021年 [218 ]J. Zhao, S. Maslennikov, E. Litvinov, そしてX. Gengは、“機械学習技術を使った高度なトランスミッション操作ガイド作成フレームワーク”だ。 2018年 IEEE Power & Energy Society General Meeting (PESGM) に参加。
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IEEE, 2018, pp. 1–5.
ieee、2018年、p.1-5。
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[219] J. Gao, P. Chongfuangprinya, Y. Ye, and B. Yang, “A three-layer hybrid model for wind power prediction,” in 2020 IEEE Power & Energy Society General Meeting (PESGM).
J. Gao, P. Chongfuangprinya, Y. Ye, and B. Yang, “A three-layer hybrid model for wind power prediction” in 2020 IEEE Power & Energy Society General Meeting (PESGM) 訳抜け防止モード: [219 ]J. Gao, P. Chongfuangprinya, Y. B. Yangは曰く、“風力発電予測のための3層ハイブリッドモデル”だ。 2020年 IEEE Power & Energy Society General Meeting (PESGM) に参加。
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IEEE, 2020, pp. 1–5.
橋本、2020年、p.1-5。
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[220] S. Maslennikov, B. Wang, Q. Zhang, E. Litvinov et al , “A test cases library for methods locating the sources of sustained oscillations,” in 2016 IEEE Power and Energy Society General Meeting (PESGM).
2016年のIEEE Power and Energy Society General Meeting (PESGM)では、[220]S. Maslennikov, B. Wang, Q. Zhang, E. Litvinov et al , “A test case library for locating the sources of sustained oscillations” と題されている。
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[221] X. Zheng, N. Xu, L. Trinh, D. Wu, T. Huang, S. Sivaranjani, Y. Liu, and L. Xie, “PSML: A multi-scale time-series dataset for machine learning in decarbonized energy grids,” arXiv preprint arXiv:2110.06324, 2021.
X. Zheng, N. Xu, L. Trinh, D. Wu, T. Huang, S. Sivaranjani, Y. Liu, L. Xie, “PSML: A multi-scale time-series dataset for machine learning in decarbonized energy grids”, arXiv preprint arXiv:2110.06324, 2021”。 訳抜け防止モード: [221 ]X. Xheng, N. Xu, L. Trinh, D. Wu, T. Huang, S. Sivaranjani, Y. Liu, L. Xie 脱炭エネルギーグリッドにおける機械学習のためのマルチスケール時系列データセット「PSML」 arXiv preprint arXiv:2110.06324 , 2021。
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英語（論文から抽出）
日本語訳
スコア
PROCEEDINGS OF THE IEEE 26
IEEEの成果 26
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Tong Huang (Member, IEEE) is a postdoctoral researcher at Massachusetts Institute of Technology (MIT).
Before joining MIT, he was a postdoctoral researcher at Texas A&M University in 2021.
MITに入る前、2021年にテキサスA&M大学の博士研究員だった。
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He received his B.E. degree in Electrical Engineering from North China Electric Power University in 2013 and an M.S. and a Ph.D. degree in Electrical Engineering from Texas A&M University in 2017 and 2021, respectively.
He received the Best Paper Award at the 2020 IEEE PES General Meeting, the Best Paper Award at the 54-th Hawaii International Conference on System Sciences, Thomas W. Powell’62 and Powell Industries Inc.
2020年のIEEE PES General Meetingで最優秀論文賞、第54回ハワイ国際システム科学会議、Thomas W. Powell'62、パウエル・インダストリーズで最優秀論文賞を受賞した。
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Fellowship, and Texas A&M Graduate Teaching Fellowship.
奨学金、テキサスa&m卒業奨学金。
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3,6, pp. 422-440, 2021。
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〔230〕A.長坂井
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I. Clavera, S. Liu, R. S. Fearing, P. Abbeel, S. Levine, C. Finn, “動的で現実世界の学習に適応するために学習する”。 訳抜け防止モード: I. Clavera, S. Liu, R. S. Fearing, P. Abbeel S. Levine, C. Finn, “ダイナミックでリアルな世界学習に適応するための学習”。 arXiv メタ-強化 arXiv:1803.11347 , 2018。
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IEEE, 2020, pp. 2725–2731.
IEEE, 2020, pp. 2725-2731。
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S. L. Brunton, J. L. Proctor, J. N. Kutz, “データから非線形力学系のスパース同定による支配方程式の発見”、とNational Academy of SciencesのProceedings, vol。
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113, no. 15, pp. 3932–3937, 2016.
113, no. 15 pp. 3932-3937, 2016年。
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AUTHORS Le Xie (Fellow, IEEE) received his B.E. degree in Electrical Engineering from Tsinghua University, Beijing, China, in 2004, an M.S. degree in Engineering Sciences from Harvard University, Cambridge, MA, USA, in 2005, and a Ph.D. degree from Carnegie Mellon University, Pittsburgh, PA, USA, in 2009.
He also managed the routing and acquisition of right-of-way for new Transmission lines in west Texas.
テキサス州西部の新しいトランスミッション線のための経路と道路の買収も担当した。
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Mr. Bruton’s current role as Director of T&D Services involves ensuring an accurate system that monitors and controls Oncor’s Transmission and Distribution systems, EMS and ADMS, as well as data analytics and operator training.
Yannan Sun (Senior member, IEEE) received her B.S. degree in Mathematics from the University of Science and Technology of China, Hefei, China, in 2004, and an M.S. degree in Statistics and a Ph.D. degree in Mathematics from Washington State University, Pullman, WA, USA, in 2007 and 2010, respectively.
She was a Scientist/Senior Scientist in the Electricity Infrastructure group at Pacific Northwest National Laboratory, Richland, WA, USA, from 2010 to 2017.
She is currently a data scientist at Oncor Electric Delivery.
彼女は現在、oncor electric deliveryのデータサイエンティストである。
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Her expertise lies in data analytics and machine learning using power system data, which she has employed to develop many data-driven algorithms for load forecasting, anomaly detection, connectivity correction and equipment preventive maintenance.