AI ASSISTED ELECTRICAL FAULT CLASSIFICATION

Abstract

The AI Assisted Electrical Fault Classification project focuses on developing an intelligent system for detecting and classifying different types of electrical faults in power systems. Electrical faults such as single-line-to-ground, line-to-line, doubleline-to-ground, and three-phase faults can cause equipment damage, power interruptions, and safety hazards. Therefore, fast and accurate fault identification is essential for maintaining the reliability and stability of electrical networks. The proposed system uses artificial intelligence and machine learning techniques to analyze electrical parameters such as voltage, current, and other relevant signal characteristics. The collected data is processed and supplied to an AI-based classification model that learns the patterns associated with different fault conditions. Based on the extracted features, the system identifies the type of fault and provides a corresponding classification result. The methodology involves data collection, preprocessing, feature extraction, model training, testing, and fault classification. Machine learning algorithms can be trained using normal and fault-condition datasets to improve classification accuracy. The trained model can then classify new electrical conditions automatically without requiring extensive manual analysis. The system helps reduce the time required for fault identification and can support engineers in making faster decisions during abnormal operating conditions. It can also be integrated with monitoring and protection systems to provide real-time fault information. Overall, the AI Assisted Electrical Fault Classification project demonstrates the application of artificial intelligence, machine learning, electrical signal processing, and power-system analysis for automated fault identification. The system can be further enhanced using deep learning, real-time monitoring, IoT connectivity, and larger fault datasets to improve classification accuracy, reliability, and practical applicability.