Resilient Identification of Distribution Network Topology

• URL: http://arxiv.org/abs/2011.07981v1
• Date: Mon, 16 Nov 2020 14:23:56 GMT
• Title: Resilient Identification of Distribution Network Topology
• Authors: Mohammad Jafarian, Alireza Soroudi, Andrew Keane
• Abstract summary: This paper develops a network TI function that relies only on the measurements available to DERMS. The propounded method is able to identify the network switching configuration, as well as the status of protective devices. Having a low computational burden, this approach is fast-track and can be applied in real-time applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Network topology identification (TI) is an essential function for distributed energy resources management systems (DERMS) to organize and operate widespread distributed energy resources (DERs). In this paper, discriminant analysis (DA) is deployed to develop a network TI function that relies only on the measurements available to DERMS. The propounded method is able to identify the network switching configuration, as well as the status of protective devices. Following, to improve the TI resiliency against the interruption of communication channels, a quadratic programming optimization approach is proposed to recover the missing signals. By deploying the propounded data recovery approach and Bayes' theorem together, a benchmark is developed afterward to identify anomalous measurements. This benchmark can make the TI function resilient against cyber-attacks. Having a low computational burden, this approach is fast-track and can be applied in real-time applications. Sensitivity analysis is performed to assess the contribution of different measurements and the impact of the system load type and loading level on the performance of the proposed approach.

Related papers

• Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrolling [9.20186865054847]
  Anomaly detection (AD) is increasingly recognized as a key component for ensuring the resilience of future communication systems. This work considers AD in network flows using incomplete measurements. We propose a novel block-successive convex approximation algorithm based on a regularized model-fitting objective. Inspired by Bayesian approaches, we extend the model architecture to perform online adaptation to per-flow and per-time-step statistics.
  arXiv  Detail & Related papers  (2024-09-17T19:59:57Z)
• Federated PCA on Grassmann Manifold for IoT Anomaly Detection [23.340237814344384]
  Traditional machine learning-based intrusion detection systems (ML-IDS) possess limitations such as the requirement for labeled data. Recent unsupervised ML-IDS approaches such as AutoEncoders and Generative Adversarial Networks (GAN) offer alternative solutions. This paper proposes a novel federated unsupervised anomaly detection framework, FedPCA, that learns common representations of distributed non-i.i.d. datasets.
  arXiv  Detail & Related papers  (2024-07-10T07:23:21Z)
• Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks [94.2860766709971]
  We address the challenge of sampling and remote estimation for autoregressive Markovian processes in a wireless network with statistically-identical agents. Our goal is to minimize time-average estimation error and/or age of information with decentralized scalable sampling and transmission policies.
  arXiv  Detail & Related papers  (2024-04-04T06:24:11Z)
• Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks [52.96907334080273]
  As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions. We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
  arXiv  Detail & Related papers  (2023-09-16T11:37:23Z)
• Compressed Regression over Adaptive Networks [58.79251288443156]
  We derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem. We devise an optimized allocation strategy where the parameters necessary for the optimization can be learned online by the agents.
  arXiv  Detail & Related papers  (2023-04-07T13:41:08Z)
• Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach [67.06539298956854]
  fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs)
  arXiv  Detail & Related papers  (2022-06-13T02:19:20Z)
• Stability Analysis of Unfolded WMMSE for Power Allocation [80.71751088398209]
  Power allocation is one of the fundamental problems in wireless networks. It is essential that the output power allocation of these algorithms is stable with respect to input perturbations. In this paper, we focus on UWMMSE, a modern algorithm leveraging graph neural networks.
  arXiv  Detail & Related papers  (2021-10-14T15:44:19Z)
• Adaptive network reliability analysis: Methodology and applications to power grid [0.0]
  This study presents the first adaptive surrogate-based Network Reliability Analysis using Bayesian Additive Regression Trees (ANR-BART) Results indicate that ANR-BART is robust and yields accurate estimates of network failure probability, while significantly reducing the computational cost of reliability analysis.
  arXiv  Detail & Related papers  (2021-09-11T19:58:08Z)
• Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA [78.60275748518589]
  Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks. A novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.
  arXiv  Detail & Related papers  (2021-01-02T15:21:08Z)
• A Multi-step and Resilient Predictive Q-learning Algorithm for IoT with Human Operators in the Loop: A Case Study in Water Supply Networks [4.9550706407171585]
  We consider the problem of recommending resilient and predictive actions for an IoT network in the presence of faulty components. We use anonymized data from Arlington County, Virginia, to compute predictive and resilient scheduling policies for a smart water supply system.
  arXiv  Detail & Related papers  (2020-06-06T15:51:52Z)
• Identification of AC Networks via Online Learning [0.0]
  This paper proposes an online learning procedure to estimate the network admittance matrix capturing topological information and line parameters. Our approach improves on existing techniques, and its effectiveness is substantiated by numerical studies on realistic testbeds.
  arXiv  Detail & Related papers  (2020-03-13T11:40:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.