Neural Network-Based Fault Learning Using Artificial Intelligence and Kalman's Filter

This paper presents a novel learning algorithm for fault detection in power systems, leveraging real-time data from Phasor Measurement Units (PMUs) and comparing it with pre-existing smoothed data stored in a database. Unlike traditional fault detection methods, which rely on predetermined thresholds or rule-based systems, our approach utilizes machine learning techniques to dynamically analyze and interpret complex patterns within the power system data. By harnessing the power of AI, specifically tailored for fault detection, our algorithm offers enhanced accuracy and efficiency in identifying faults, thereby enabling quicker response times and improved system resilience. The significance of this work lies in its practical application within power system operations, where timely fault detection is critical for maintaining grid stability and minimizing disruptions. Through empirical evaluation and case studies, we demonstrate the effectiveness of our algorithm in enhancing the reliability and security of power systems, thus contributing to the advancement of smart grid technologies.