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

IEEE Access

Madhu Shobini Murugan¹  Rishi Pugazhendhi¹  M. Gurudhachanamoorthy²  David Prince Winston³  Eklas Hossain³  Rajvikram Madurai Elavarasan⁴  O. Jeba Singh⁵  Pravin Murugesan⁶ 

[1] Technology, Virudhunagar, Tamil Nadu, India;Department of Electrical and Electronics Engineering (EEE), Arunachala College of Engineering for Women, Nagercoil, Tamil Nadu, India;Department of Electrical and Electronics Engineering (EEE), Kamaraj College of Engineering &x0026;Department of Electrical and Electronics Engineering (EEE), Sri Vidya College of Engineering &x0026;Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, India;Research and Development Division (Power & Energy), Nestlives Private Ltd., Chennai, India;

关键词: Binary tree;    feed forward back propagation neural network;    hot-spotting;    micro crack;    PV module;    support vector machine;   

DOI  :  10.1109/ACCESS.2021.3111904

来源: DOAJ

【 摘 要 】

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

【 授权许可】

Unknown