Feature Extraction Technique for Fault Detection in Microgrid Using Principal Component Analysis

With the increasing integration of distributed generation into traditional distribution grids, microgrids (MGs) are becoming more susceptible to various types of faults, such as open‐circuit, short‐circuit, symmetric, and asymmetric faults. These faults can arise from equipment failures, abnormal operating conditions, human error, and environmental factors, often leading to substantial financial losses and blackouts. Traditional methods of fault analysis struggle to cope with the complexity, diversity, and large volumes of data involved in the detection and diagnosis processes. In this context, the application of machine learning techniques has shown promise in enhancing the accuracy of fault detection and classification in MGs. A critical component of this success is the feature extraction process, which significantly influences the performance of machine learning models. This study proposes the use of principal component analysis (PCA) for effective feature extraction, improving the accuracy and efficiency of fault detection in MGs. The proposed method demonstrates how PCA can simplify the feature space while preserving essential information, thereby enhancing the overall diagnostic capability of the system. Experimental results demonstrate that the PCA‐based feature extraction method significantly improves the performance of the fault detection classifier by achieving a higher accuracy of 99.7% and faster processing times of 102.43 s compared to other classifier methods.