Development of Novel Algorithms for Fault Detection in Power Distribution Networks

Abstract

In modern power distribution networks, ensuring reliable and efficient operation is crucial, with fault detection being a key component. This paper introduces novel algorithms designed to advance fault detection capabilities in these networks. Current fault detection methods often face limitations in accuracy, response time, and scalability. To address these challenges, we propose three innovative algorithms: a hybrid machine learning approach combining supervised learning with ensemble techniques, a real-time data fusion algorithm integrating multiple data sources, and an adaptive thresholding technique for dynamic fault detection. Each algorithm aims to improve the precision and speed of fault identification and isolation. We evaluate the performance of these algorithms through extensive simulations and real-world case studies. Our results demonstrate significant improvements over traditional methods, with enhanced accuracy, reduced response times, and better scalability. The proposed algorithms offer promising solutions for modernizing fault detection in power distribution networks, contributing to increased reliability and efficiency. The paper concludes with recommendations for future research, including further refinement of the algorithms and exploration of their integration with emerging smart grid technologies.