High Impedance Fault

High-impedance faults (HIFs) in power distribution systems pose a significant challenge due to their low current magnitudes, making them difficult to detect with traditional methods. Current research focuses on developing data-driven solutions, employing machine learning techniques such as support vector machines and autoencoders combined with principal component analysis, to identify HIFs based on voltage and current characteristics. These unsupervised and supervised learning approaches aim to improve the accuracy and speed of HIF detection, enhancing grid reliability and safety. The successful implementation of these advanced detection methods will have a substantial impact on power system operation and maintenance.