Solar PV’s Micro Crack and Hotspots Detection Technique Using NN and SVM

For lifelong and reliable operation, advanced solar photovoltaic (PV) equipment is designed to minimize the faults. Irrespectively, panel degradation makes fault inevitable. Thus, quick detection classification of pivotal. Among various problems that promote degradation, hot spots micro-cracks are prominent reliability which affect PV performance. When these types faults occur in a cell, gets heated up it reduces power generation hence its efficiency considerably. In this study, effect hotspot studied comparative method proposed detect different modules affected by hotspots. The process accomplished utilizing Feed Forward Back Propagation Neural Network technique Support Vector Machine (SVM) techniques. Six input parameters like percentage loss (PPL), Open-circuit voltage (VOC), Short circuit current (ISC), Irradiance (IRR), Panel temperature Internal impedance (Z) accounted Experimental investigation simulations using MATLAB carried out five categories faulty healthy panels. Both methods exhibited promising result with an average accuracy 87% for feed-forward back propagation neural network 99% SVM exposes potential technique.