A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Grid

• URL: http://arxiv.org/abs/2309.09921v1
• Date: Mon, 18 Sep 2023 16:35:30 GMT
• Title: A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Grid
• Authors: Dibaloke Chanda, Nasim Yahya Soltani
• Abstract summary: We propose a heterogeneous multi-task learning graph neural network (MTL-GNN) capable of detecting, locating and classifying faults. Using a graph neural network (GNN) allows for learning the topological representation of the distribution system. This work also proposes a novel GNN-based explainability method to identify key nodes in the distribution system.
• Score: 1.8416014644193066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Precise and timely fault diagnosis is a prerequisite for a distribution system to ensure minimum downtime and maintain reliable operation. This necessitates access to a comprehensive procedure that can provide the grid operators with insightful information in the case of a fault event. In this paper, we propose a heterogeneous multi-task learning graph neural network (MTL-GNN) capable of detecting, locating and classifying faults in addition to providing an estimate of the fault resistance and current. Using a graph neural network (GNN) allows for learning the topological representation of the distribution system as well as feature learning through a message-passing scheme. We investigate the robustness of our proposed model using the IEEE-123 test feeder system. This work also proposes a novel GNN-based explainability method to identify key nodes in the distribution system which then facilitates informed sparse measurements. Numerical tests validate the performance of the model across all tasks.

Related papers

• DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment [57.62885438406724]
  Graph neural networks are recognized for their strong performance across various applications. BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks. We propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning.
  arXiv  Detail & Related papers  (2024-06-04T07:24:51Z)
• Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network [8.318363497010969]
  This paper studies the application of GNNs to identify the specific types of network flows in an unsupervised manner. To the best of our knowledge, it is the first GNN-based self-supervised method for the multiclass classification of network flows in NIDS.
  arXiv  Detail & Related papers  (2024-03-03T12:34:13Z)
• 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)
• Dynamic Fault Characteristics Evaluation in Power Grid [7.791487134360031]
  The proposed GNN-based approach first identifies fault nodes through a specialized feature extraction method coupled with a knowledge graph. The results from experiments show that this method can accurately locate fault nodes in simulation scenarios with a remarkable accuracy.
  arXiv  Detail & Related papers  (2023-11-28T05:00:27Z)
• A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids [0.3093890460224435]
  This paper presents a Temporal Graph Neural Network (TGNN) framework for detection and localization of false data injection and ramp attacks on the system state in smart grids. The sensitivity of the model to intensity and location of the attacks and model's detection delay versus detection accuracy have been evaluated.
  arXiv  Detail & Related papers  (2022-12-07T00:56:02Z)
• Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks [0.0]
  This paper investigates Graph Neural Networks (GNNs) application for self-supervised network intrusion and anomaly detection. GNNs are a deep learning approach for graph-based data that incorporate graph structures into learning. We present Anomal-E, a GNN approach to intrusion and anomaly detection that leverages edge features and graph topological structure in a self-supervised process.
  arXiv  Detail & Related papers  (2022-07-14T10:59:39Z)
• Distributionally Robust Semi-Supervised Learning Over Graphs [68.29280230284712]
  Semi-supervised learning (SSL) over graph-structured data emerges in many network science applications. To efficiently manage learning over graphs, variants of graph neural networks (GNNs) have been developed recently. Despite their success in practice, most of existing methods are unable to handle graphs with uncertain nodal attributes. Challenges also arise due to distributional uncertainties associated with data acquired by noisy measurements. A distributionally robust learning framework is developed, where the objective is to train models that exhibit quantifiable robustness against perturbations.
  arXiv  Detail & Related papers  (2021-10-20T14:23:54Z)
• An Introduction to Robust Graph Convolutional Networks [71.68610791161355]
  We propose a novel Robust Graph Convolutional Neural Networks for possible erroneous single-view or multi-view data. By incorporating an extra layers via Autoencoders into traditional graph convolutional networks, we characterize and handle typical error models explicitly.
  arXiv  Detail & Related papers  (2021-03-27T04:47:59Z)
• Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation [21.297461316329453]
  We propose to capture the structure of a complex equipment in the form of a graph, and use graph neural networks (GNNs) to model multi-sensor time-series data. We observe that the proposed GNN-based RUL estimation model compares favorably to several strong baselines from literature such as those based on RNNs and CNNs.
  arXiv  Detail & Related papers  (2020-06-30T06:38:08Z)
• Graph Neural Networks for Motion Planning [108.51253840181677]
  We present two techniques, GNNs over dense fixed graphs for low-dimensional problems and sampling-based GNNs for high-dimensional problems. We examine the ability of a GNN to tackle planning problems such as identifying critical nodes or learning the sampling distribution in Rapidly-exploring Random Trees (RRT) Experiments with critical sampling, a pendulum and a six DoF robot arm show GNNs improve on traditional analytic methods as well as learning approaches using fully-connected or convolutional neural networks.
  arXiv  Detail & Related papers  (2020-06-11T08:19:06Z)
• Stochastic Graph Neural Networks [123.39024384275054]
  Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning. Current GNN architectures assume ideal scenarios and ignore link fluctuations that occur due to environment, human factors, or external attacks. In these situations, the GNN fails to address its distributed task if the topological randomness is not considered accordingly.
  arXiv  Detail & Related papers  (2020-06-04T08:00:00Z)

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.