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, the panel degradation makes the fault inevitable. Thus, the quick detection and classification of panel degradation is pivotal. Among various problems that promote pane...
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2021-01-01
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| author | David Prince Winston Madhu Shobini Murugan Rajvikram Madurai Elavarasan Rishi Pugazhendhi O. Jeba Singh Pravin Murugesan M. Gurudhachanamoorthy Eklas Hossain |
| author_facet | David Prince Winston Madhu Shobini Murugan Rajvikram Madurai Elavarasan Rishi Pugazhendhi O. Jeba Singh Pravin Murugesan M. Gurudhachanamoorthy Eklas Hossain |
| author_sort | David Prince Winston |
| collection | DOAJ |
| description | 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<sub>OC</sub>), Short circuit current (I<sub>SC</sub>), Irradiance (I<sub>RR</sub>), 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. |
| format | Article |
| id | doaj-art-b97e560d9746499fb5e39aaa631fad89 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b97e560d9746499fb5e39aaa631fad892025-08-25T23:05:53ZengIEEEIEEE Access2169-35362021-01-01912725912726910.1109/ACCESS.2021.31119049535505Solar PV’s Micro Crack and Hotspots Detection Technique Using NN and SVMDavid Prince Winston0https://orcid.org/0000-0003-4701-8024Madhu Shobini Murugan1Rajvikram Madurai Elavarasan2https://orcid.org/0000-0002-7744-6102Rishi Pugazhendhi3https://orcid.org/0000-0001-6831-6288O. Jeba Singh4Pravin Murugesan5M. Gurudhachanamoorthy6Eklas Hossain7https://orcid.org/0000-0003-2332-8095Department of Electrical and Electronics Engineering (EEE), Kamaraj College of Engineering & Technology, Virudhunagar, Tamil Nadu, IndiaDepartment of Electrical and Electronics Engineering (EEE), Sri Vidya College of Engineering & Technology, Virudhunagar, Tamil Nadu, IndiaDepartment of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, IndiaResearch and Development Division (Power & Energy), Nestlives Private Ltd., Chennai, IndiaDepartment of Electrical and Electronics Engineering (EEE), Arunachala College of Engineering for Women, Nagercoil, Tamil Nadu, IndiaDepartment of Electrical and Electronics Engineering (EEE), Kamaraj College of Engineering & Technology, Virudhunagar, Tamil Nadu, IndiaDepartment of Electrical and Electronics Engineering (EEE), Kamaraj College of Engineering & Technology, Virudhunagar, Tamil Nadu, IndiaDepartment of Electrical Engineering and Renewable Energy, Oregon Renewable Energy Center (OREC), Oregon Institute of Technology, Klamath Falls, OR, USAFor 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<sub>OC</sub>), Short circuit current (I<sub>SC</sub>), Irradiance (I<sub>RR</sub>), 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.https://ieeexplore.ieee.org/document/9535505/Binary treefeed forward back propagation neural networkhot-spottingmicro crackPV modulesupport vector machine |
| spellingShingle | David Prince Winston Madhu Shobini Murugan Rajvikram Madurai Elavarasan Rishi Pugazhendhi O. Jeba Singh Pravin Murugesan M. Gurudhachanamoorthy Eklas Hossain Solar PV’s Micro Crack and Hotspots Detection Technique Using NN and SVM IEEE Access Binary tree feed forward back propagation neural network hot-spotting micro crack PV module support vector machine |
| title | Solar PV’s Micro Crack and Hotspots Detection Technique Using NN and SVM |
| title_full | Solar PV’s Micro Crack and Hotspots Detection Technique Using NN and SVM |
| title_fullStr | Solar PV’s Micro Crack and Hotspots Detection Technique Using NN and SVM |
| title_full_unstemmed | Solar PV’s Micro Crack and Hotspots Detection Technique Using NN and SVM |
| title_short | Solar PV’s Micro Crack and Hotspots Detection Technique Using NN and SVM |
| title_sort | solar pv x2019 s micro crack and hotspots detection technique using nn and svm |
| topic | Binary tree feed forward back propagation neural network hot-spotting micro crack PV module support vector machine |
| url | https://ieeexplore.ieee.org/document/9535505/ |
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