Line-to-Line Fault Detection for Photovoltaic Arrays Based on Multiresolution Signal Decomposition and Two-Stage Support Vector Machine

Fault detection in photovoltaic (PV) arrays becomes difficult as the number of PV panels increases. Particularly, under low irradiance conditions with an active maximum power point tracking algorithm, line-to-line (L-L) faults may remain undetected because of low fault currents, resulting in loss of energy and potential fire hazards. This paper proposes a fault detection algorithm based on multiresolution signal decomposition for feature extraction, and two-stage support vector machine (SVM) classifiers for decision making. This detection method only requires data of the total voltage and current from a PV array and a limited amount of labeled data for training the SVM. Both simulation and experimental case studies verify the accuracy of the proposed method.

I. Introduction

Renewable energy sources (RES) have been significantly contributing to sustainable power generation, considering the increasing demand and global environmental degradation. Among various RES, solar energy is one of the most attractive power sources because of its technical, economical, and environmental benefits. Increasing deployment of PV systems is revealing various technical issues that used to be unknown to the PV industry. PV array L-L short-circuit fault is one such issues that can cause PV failure and lower system efficiency [1]. L-L faults are hardly detectable under low irradiance conditions or when the fault occurs between points with close electric potential, i.e., low mismatch faults. Moreover, maximum power point tracking (MPPT) schemes, while optimizing the power output of a PV array under different operating conditions, may further increase the difficulty of detecting such faults [2].