Fault Detection in Electrical Distribution System using Autoencoders

2026-02-16

AI Summary

This work addresses the challenges of fault detection in power distribution networks, including data scarcity, high randomness of fault parameters, and difficulties in deploying existing methods. The authors propose an unsupervised anomaly detection approach based on a convolutional autoencoder (CAE), leveraging a deep autoencoding architecture to achieve efficient dimensionality reduction and rapid training. Without requiring extensive labeled data, the method simultaneously accomplishes fault detection, classification, and localization. It significantly reduces model parameter count and training time while achieving detection accuracies of 97.62% on simulated data and 99.92% on a public dataset, outperforming current state-of-the-art techniques and demonstrating strong practicality and deployability.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

In recent times, there has been considerable interest in fault detection within electrical power systems, garnering attention from both academic researchers and industry professionals. Despite the development of numerous fault detection methods and their adaptations over the past decade, their practical application remains highly challenging. Given the probabilistic nature of fault occurrences and parameters, certain decision-making tasks could be approached from a probabilistic standpoint. Protective systems are tasked with the detection, classification, and localization of faulty voltage and current line magnitudes, culminating in the activation of circuit breakers to isolate the faulty line. An essential aspect of designing effective fault detection systems lies in obtaining reliable data for training and testing, which is often scarce. Leveraging deep learning techniques, particularly the powerful capabilities of pattern classifiers in learning, generalizing, and parallel processing, offers promising avenues for intelligent fault detection. To address this, our paper proposes an anomaly-based approach for fault detection in electrical power systems, employing deep autoencoders. Additionally, we utilize Convolutional Autoencoders (CAE) for dimensionality reduction, which, due to its fewer parameters, requires less training time compared to conventional autoencoders. The proposed method demonstrates superior performance and accuracy compared to alternative detection approaches by achieving an accuracy of 97.62% and 99.92% on simulated and publicly available datasets.

Problem

• Fault Detection
• Electrical Distribution System
• Anomaly Detection
• Power Systems
• Deep Learning

Innovation

• Autoencoder
• Convolutional Autoencoder
• Fault Detection
• Anomaly Detection
• Power Distribution System