Deep Learning-Based Fault Detection and Classification in Power System Protection: A Comparative Study

Power systems are susceptible to faults during electricity transmission, which can compromise system stability and reliability. Accurate fault detection and classification are crucial for implementing effective protection and mitigation strategies. While deep learning models have demonstrated considerable potential for automating these tasks, their performance can vary depending on the specific circumstances. This research provides a comparative analysis of various deep learning approaches for fault detection and classification in power system protection. The models are assessed using a relevant dataset and multiple performance metrics to gauge their effectiveness. The aim is to offer a structured evaluation, providing insights into which approaches are best suited for different fault scenarios.