Nonlinear Optimization for Optimal Fault Detection and Classification in Power Systems Using Novel Semi-Supervised KNN Approach

Power system reliability is paramount for ensuring uninterrupted energy supply. This paper addresses the critical challenge of accurate fault detection and classification in power systems, particularly in the context of increasing renewable energy integration. Traditional protection systems, while effective, often struggle to adapt to evolving grid conditions and emerging fault types. To overcome the limitations of traditional supervised learning methods, which require large amounts of labelled data, we propose a novel semi-supervised learning approach based on the K-Nearest Neighbor (KNN) algorithm for robust fault detection and classification in power systems. The proposed method leverages the strengths of semi-supervised learning, effectively utilizing both labelled and unlabelled data to improve classification accuracy of transmission line faults. By iteratively training the KNN model on an expanding dataset, the system can adapt to changing fault patterns and enhance its performance over time. To enhance the robustness and accuracy of fault classification, we formulate the fault detection and classification problem as a nonlinear variational inequality (NVI)-based problem. This NVI framework captures the inherent nonlinearity of power system dynamics and the variability of fault events, enabling the KNN classifier to accurately identify faults under diverse operating conditions. The proposed method is evaluated through extensive simulations in MATLAB. The effectiveness of the proposed approach is demonstrated through simulations on a 200 km transmission line under various fault conditions. The results indicate that the KNN classifier can accurately identify and categorize different fault types, contributing to improved system reliability and operational efficiency. This research provides a promising solution for enhancing power system protection, enabling timely fault detection and response, and ultimately minimizing system disruptions.