Integrating AI-driven Fault Detection and Protection Technique for Electric Power Components and Systems

Abstract

Electric power systems are critical infrastructures that require continuous monitoring and protection to ensure reliable operation. Fault detection and protection play a crucial role in maintaining the stability and integrity of power components and systems. This research paper presents a comprehensive approach to enhancing fault detection and protection techniques in electric power systems by integrating Artificial Intelligence (AI). The proposed model leverages AI-driven techniques, including Deep Forest, Support Vector Machines (SVM), and Neural Networks (NN), for effective fault detection and protection. Deep Forest serves as a feature extractor, capturing informative representations of fault data, while SVM and NN classifiers ensure accurate fault type classification and decision-making. Extensive experiments and evaluations demonstrate the hybrid model's superior performance, achieving 98.57% accuracy and highlighting its potential to advance fault detection and protection in electric power systems.