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

Abstract

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. Six input parameters like percentage of power loss (PPL), Open-circuit voltage (VOC), Short circuit current (ISC), Irradiance (IRR), 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.

Development of a machine-learning-based method for early fault detection in photovoltaic systems

“…Six methods [41-43, 46, 50, 53], can detect the three main types of faults on the DC side of a PVS (open-circuit fault, short-circuit fault, mismatch faults). One method [47] can detect two types of faults (short-circuit fault, mismatch faults), and six methods [44,45,48,49,51,52] can detect only one type of fault (open-circuit fault, arc fault, mismatch faults, short-circuit fault, arc fault, mismatch faults respectively). Regarding the accuracy of fault detection, nine methods [41,42,45,[47][48][49][50][51]53] show an accuracy of more than 95%, while one method [46] shows fluctuating accuracy depending on the type of the fault (89.6-98%).…”

“…Wang et al [47] employed a multi-class SVM to identify and categorize line-to-line faults and anomalous degradation faults in a PV module. Winston et al [48] utilized a feed forward back propagation neural network combined with an SVM in order to detect micro-cracks and hotspots. Yi and Etemadi [49] proposed a method for detecting line-to-line and line-to-ground faults, which was based primarily on the use of a multi-resolution signal decomposition (MSD) algorithm on a fuzzy inference system.…”

Lightweight Hot-Spot Fault Detection Model of Photovoltaic Panels in UAV Remote-Sensing Image

“…Photovoltaic panels that run outdoors for a long time are prone to aging; therefore, the simulation model needs to be constantly updated to reduce the output deviation from the actual photovoltaic panels. Winston et al [21] used the percentage of power loss (PPL), open-circuit voltage (V OC ), short-circuit current (I SC ), irradiance (I RR ), panel temperature, and internal impedance (Z) parameters. The hot spots of photovoltaic panels were detected by using a feedforward backpropagation neural network and support vector machine (SVM).…”

Towards Smart Monitoring Systems: Fault Detection and Diagnosis-Based Artificial Intelligence Algorithms in Solar PV Power Plants

“…Finally, the microcracks will impact the efficiency of all PV modules and systems, resulting in a performance loss. Changing the panel is usually advised in this situation [9].…”