Fault Detection of Solar PV System Using SVM and Thermal Image Processing

Abstract

Installation of photovoltaic plants across the globe increases, in the recent years, due to the energy demand across the world. Solar energy is free of cost, inexhaustible and a non-polluted source to the environment. The efficiency of any power plant depends on its fault free operation. Due to the occurrence of fault in PV system, the reliability and power output is reduced. In PV plants, the internal and external faults normally result in an increase in temperature which is easily sensed by different methods. In this paper, an algorithm based on thermal image processing, is proposed to extract the features of the PV cells in operation. These extracted features are then compared with the features of the healthy PV module using Support Vector Machine. SVM is a classifier tool which classifies whether the PV modules are defective or non-defective. An experimental set up is created and the performance of the algorithm is verified by testing it with faulty data sets which are obtained by creating different types of faults intentionally. The algorithm successfully identifies the defective PV module and its performance is validated experimentally.