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[2602.14939] Fault Detection in Electrical Distribution System using Autoencoders
Summary
This paper presents an anomaly-based approach for fault detection in electrical distribution systems using deep autoencoders, achieving high accuracy on simulated datasets.
Why It Matters
Fault detection in electrical systems is critical for ensuring reliability and safety. This research leverages advanced machine learning techniques to improve detection accuracy, addressing a significant challenge in the field and potentially benefiting both academic research and industry applications.
Key Takeaways
- The proposed method uses deep autoencoders for effective fault detection.
- Achieves accuracy rates of 97.62% and 99.92% on different datasets.
- Utilizes Convolutional Autoencoders for reduced training time and complexity.
- Addresses the scarcity of reliable data in training fault detection systems.
- Enhances existing fault detection methods with a probabilistic approach.
Electrical Engineering and Systems Science > Systems and Control arXiv:2602.14939 (eess) [Submitted on 16 Feb 2026] Title:Fault Detection in Electrical Distribution System using Autoencoders Authors:Sidharthenee Nayak, Victor Sam Moses Babu, Chandrashekhar Narayan Bhende, Pratyush Chakraborty, Mayukha Pal View a PDF of the paper titled Fault Detection in Electrical Distribution System using Autoencoders, by Sidharthenee Nayak and 4 other authors View PDF HTML (experimental) 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...
