GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements

• URL: http://arxiv.org/abs/2205.13116v1
• Date: Thu, 26 May 2022 02:50:26 GMT
• Title: GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements
• Authors: Armin Aligholian and Hamed Mohsenian-Rad
• Abstract summary: This paper is concerned with the complex task of identifying the type and cause of the events that are captured by distribution-level phasor measurement units (D-PMUs) in order to enhance situational awareness in power distribution systems. We propose an unsupervised graph-representation learning method, called GraphPMU, to significantly improve the performance in event clustering under locationally-scarce data availability.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper is concerned with the complex task of identifying the type and cause of the events that are captured by distribution-level phasor measurement units (D-PMUs) in order to enhance situational awareness in power distribution systems. Our goal is to address two fundamental challenges in this field: a) scarcity in measurement locations due to the high cost of purchasing, installing, and streaming data from D-PMUs; b) limited prior knowledge about the event signatures due to the fact that the events are diverse, infrequent, and inherently unscheduled. To tackle these challenges, we propose an unsupervised graph-representation learning method, called GraphPMU, to significantly improve the performance in event clustering under locationally-scarce data availability by proposing the following two new directions: 1) using the topological information about the relative location of the few available phasor measurement units on the graph of the power distribution network; 2) utilizing not only the commonly used fundamental phasor measurements, bus also the less explored harmonic phasor measurements in the process of analyzing the signatures of various events. Through a detailed analysis of several case studies, we show that GraphPMU can highly outperform the prevalent methods in the literature.

Related papers

• Instance-Aware Graph Prompt Learning [71.26108600288308]
  We introduce Instance-Aware Graph Prompt Learning (IA-GPL) in this paper. The process involves generating intermediate prompts for each instance using a lightweight architecture. Experiments conducted on multiple datasets and settings showcase the superior performance of IA-GPL compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-11-26T18:38:38Z)
• Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning [53.81365215811222]
  We provide a review and summary of the latest approaches, strategies, and insights that address distribution shifts within the context of graph learning. We categorize existing graph learning methods into several essential scenarios, including graph domain adaptation learning, graph out-of-distribution learning, and graph continual learning. We discuss the potential applications and future directions for graph learning under distribution shifts with a systematic analysis of the current state in this field.
  arXiv  Detail & Related papers  (2024-02-26T07:52:40Z)
• Multitask Active Learning for Graph Anomaly Detection [48.690169078479116]
  We propose a novel MultItask acTIve Graph Anomaly deTEction framework, namely MITIGATE. By coupling node classification tasks, MITIGATE obtains the capability to detect out-of-distribution nodes without known anomalies. Empirical studies on four datasets demonstrate that MITIGATE significantly outperforms the state-of-the-art methods for anomaly detection.
  arXiv  Detail & Related papers  (2024-01-24T03:43:45Z)
• Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery [28.38130326794833]
  We introduce Dual-level Graph self-supervised Pretraining with Motif discovery (DGPM) DGPM orchestrates node-level and subgraph-level pretext tasks. Experiments on 15 datasets validate DGPM's effectiveness and generalizability.
  arXiv  Detail & Related papers  (2023-12-19T08:09:36Z)
• DynamoPMU: A Physics Informed Anomaly Detection and Prediction Methodology using non-linear dynamics from $\mu$PMU Measurement Data [0.0]
  We develop a physics dynamics-based approach to detect anomalies in the $mu$PMU streaming data and simultaneously predict the events using governing equations. We demonstrate the efficacy of our proposed framework through analysis of real $mu$PMU data taken from the LBNL distribution grid.
  arXiv  Detail & Related papers  (2023-03-31T19:32:24Z)
• DAGAD: Data Augmentation for Graph Anomaly Detection [57.92471847260541]
  This paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs. A series of experiments on three datasets prove that DAGAD outperforms ten state-of-the-art baseline detectors concerning various mostly-used metrics.
  arXiv  Detail & Related papers  (2022-10-18T11:28:21Z)
• Physics-Informed Graph Learning for Robust Fault Location in Distribution Systems [2.984934409689467]
  Rapid growth of distributed energy resources potentially increases power grid instability. One promising strategy is to employ data in power grids to efficiently respond to abnormal events (e.g., faults) by detection and location. We propose a physics-informed graph learning framework of two stages to handle these challenges when locating faults.
  arXiv  Detail & Related papers  (2021-07-05T21:18:37Z)
• Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning [64.98816284854067]
  Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. A novel GCN-based SSL algorithm is presented in this paper to enrich the supervision signals by utilizing both data similarities and graph structure.
  arXiv  Detail & Related papers  (2020-09-15T13:59:28Z)
• Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems [6.4332733596587115]
  We introduce a novel deep learning-based approach for fault detection and test it on a real data set, namely, the Kaggle platform for a partial discharge detection task. Our solution adopts a Long-Short Term Memory architecture with attention mechanism to extract time series features, and uses a 1D-Convolutional Neural Network structure to exploit frequency information of the signal for prediction.
  arXiv  Detail & Related papers  (2020-09-15T02:01:47Z)
• Unsupervised Event Detection, Clustering, and Use Case Exposition in Micro-PMU Measurements [0.0]
  We develop an unsupervised event detection method based on the concept of Generative Adversarial Networks (GAN) We also propose a two-step unsupervised clustering method, based on a novel linear mixed integer programming formulation. Results show that they can outperform the prevalent methods in the literature.
  arXiv  Detail & Related papers  (2020-07-30T05:20:29Z)
• Graph Representation Learning via Graphical Mutual Information Maximization [86.32278001019854]
  We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
  arXiv  Detail & Related papers  (2020-02-04T08:33:49Z)

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.