Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids

2026-02-11

AI Summary

This study addresses the challenge of detecting anomalous events in large-scale, high-voltage power grid operational data by systematically evaluating the performance of neural networks, k-nearest neighbors, support vector machines, and unsupervised learning methods under complex contextual conditions. The findings reveal that grid anomalies exhibit strong context dependency. Among the evaluated approaches, neural networks significantly outperform traditional methods in overall detection accuracy, while unsupervised learning algorithms demonstrate superior robustness and efficiency in scenarios involving concurrent multiple anomalies. This work not only validates the advantages of deep learning for anomaly detection in power systems but also highlights the practical value of unsupervised methods when labeled data are scarce and fault patterns are intricately coupled.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Evaluation and Analysis
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

We apply several machine learning algorithms to the problem of anomaly detection in operational data for large-scale, high-voltage electric power grids. We observe important differences in the performance of the algorithms. Neural networks typically outperform classical algorithms such as k-nearest neighbors and support vector machines, which we explain by the strong contextual nature of the anomalies. We show that unsupervised learning algorithm work remarkably well and that their predictions are robust against simultaneous, concurring anomalies.

Problem

• Anomaly Detection
• Power Grids
• Machine Learning
• Operational Data
• Unsupervised Learning

Innovation

• anomaly detection
• machine learning
• power grids
• neural networks
• unsupervised learning