Fault Diagnosis in PV system using DWT and Ensembled k-NN Machine Learning Classifier

Abstract

The output power of a photovoltaic (PV) system is dependent on the perfect operation of the PV array. PV arrays may experience various fault conditions as a result of open circuit, short circuit, and partial shading. A fault is an aberrant situation that can interfere with the effective execution of a PV system. To reduce power loss and harm to the PV system, design engineers must identify faults. The most common problems in PV array systems are open circuit, partial shading, hotspots, cracks and short circuit faults. Several scientists suggested fault diagnosis methods to recognize faults in PV arrays, however the majority failed to detect problems accurately and quickly. In this paper, a two-stage algorithm based on wavelet transform and ensemble KNN method is used to detect and classify faults in PV array. A 6X6 series parallel PV array is used to test the proposed methodology. MATLAB Simulink is used to model the 6X6 PV array. Wavelet Analysis tool bar is used for signal analysis.