ML-Based Fault Detection Strategies for Power Electronics

Power electronics plays a pivotal role in modern energy systems, contributing to improved efficiency, reduced emissions, and enhanced control in various applications such as renewable energy integration, electric vehicles, and smart grids. This vital domain within electrical engineering, has undergone a remarkable transformation through the integration of Machine Learning, as the power electronics industry has witnessed significant advancements in optimizing operations, improving reliability, and enabling sustainable energy solutions. This research paper presents various strategies of Machine learning used in power electronics. It focuses on using Machine Learning based models to examine and train various voltage and current record units obtained by simulated malfunctioning transmission line model created with Matlab /Simulink. This yielded a dataset consisting of 12,001 fault data samples. Machine Learning models SVM, KNN and Decision Tree were trained on the same data and were employed to detect the faults. Next, the model was chosen for its strong fault detection capabilities and potential, which were evaluated based on accuracy, precision, and the F1- score. Effective fault detection techniques play a crucial role in optimizing power system control, reducing energy wastage, and ensuring top-notch performance.