Fault Identification in Solar PV Panels Using Machine Learning. GLCM, HOG, Naive-Bayes

      Аннотация: Among the renewable forms of energy, Solar energy is a convincing, clean energy and acceptable worldwide. Solar photovoltaic plants, both ground mounting and the rooftop, are mushrooming throughout the world. One of the significant challenges is the fault identification of the solar photovoltaic module, since a vast power plant condition monitoring of individual panels is cumbersome.This project aims to identify the panel using a thermal imaging system and processes the thermal images using the image processing technique. Similarly, the new and aged solar photovoltaic panels were compared in the image processing technique to identify any fault in the panel. The image of the aged panels containing faults will be recorded and performance will be analyzed using MATLAB software. This book is the work of students B. Akhila, S. Keerthana, G.Meghana, K Meghana.

Ключевые слова: Hotspots, Photovoltaic (PV) modules, Naive Bayes classifier, Histogram of gradients (HOG), GLCM