Ensemble LVQ Model for Photovoltaic Line-to-Line Fault Diagnosis Using K-Means Clustering and AdaGrad

Abstract

Line-to-line (LL) faults are one of the most frequent short-circuit conditions in photovoltaic (PV) arrays which are conventionally detected and cleared by overcurrent protection devices (OCPDs). However, OCPDs are shown to face challenges when detecting LL faults under critical detection conditions, i.e., low mismatch levels and/or high fault impedance values. This occurs due to insufficient fault current, thus leaving the LL faults undetected and leading to power losses and even catastrophic fire hazards. To compensate for OCPD deficiencies, recent studies have proposed modern artificial intelligence (AI)-based methods. However, various limitations can still be witnessed even in AI-based methods, such as (i) most of the models requiring a massive training dataset, (ii) critical fault detection conditions not being taken into consideration, (iii) models not being accurate enough when dealing with critical conditions, etc. To this end, the present paper proposes a learning vector quantization (LVQ)-based ensemble learning model in which three LVQs are individually trained to detect and classify LL faults in PV arrays. The initial LVQ vectors are determined using the k-means clustering method, and the learning rate is optimized by the adaptive gradient (AdaGrad) optimizer. The training and testing datasets are collected according to the PV array’s current–voltage (I–V) characteristic curve, and several features are extracted based on the Canberra and chi-squared distance techniques. The model utilizes a small training dataset, considers various critical detection conditions for LL faults—such as different mismatch levels and fault impedance values—and the final experimental results show that the model achieves an impressive average accuracy of 99.26%.