Statistical Pattern Recognition based Technique for PV Array Fault Diagnosis

Abstract

A reliable operation of photovoltaic (PV) systems should be guaranteed by implementing an effective fault detection program so that the maintenance schedule can be arranged to repair or replace the faulty component in time. This paper presents a PV array fault diagnosis method by applying the statistical pattern recognition technique. Proper feature parameters are selected first to characterize the operation condition of a PV array. The fault data set is then collected under varying operating conditions. Once the fault categories are defined, the genetic algorithm-based fuzzy C-means (GAFCM) clustering algorithm is employed to obtain the clustering center of each fault category so that the fuzzy mapping relationship between chosen feature parameters and its corresponding fault type is established. In this way, the fault type of a PV array can be judged immediately once the values of characteristic parameters are given. Finally, a Gaussian distribution-based membership function is developed to calculate the similarity between test samples and the defined fault categories. The fault type of the test sample can be recognized by judging from the largest similarity.