Deep Learning based Fault Detection and Classification in Electric Circuits.

Abstract

The reliability and efficiency of electric circuits are paramount in power systems, necessitating advanced methods for fault detection and classification. Traditional techniques, such as rule-based systems and statistical analysis, often struggle with the complex and nonlinear data patterns prevalent in electrical networks. This paper explores the application of deep learning to address these challenges, presenting a novel approach for fault detection and classification in electric circuits. We employ convolutional neural networks (CNNs) to extract spatial features and recurrent neural networks (RNNs) to capture temporal dependencies, creating a hybrid model that enhances fault diagnosis accuracy. The proposed model is trained and validated on a comprehensive dataset, encompassing various fault types and conditions. Furthermore, the robustness of the model is tested against noise and variations in operating conditions, proving its reliability in real-world applications. This study underscores the importance of integrating advanced machine learning techniques into power system monitoring and control, paving the way for more resilient and intelligent electric infrastructures. The findings highlight deep learning's promise in enhancing fault detection and classification, ultimately contributing to improved system reliability and reduced downtime in electric power networks.