Performance Analysis of SVM and CNN Networks for Defect Detection in Solar Panel

Vishakha Yadav; Om Dev Singh; Shubham Singh; Dr. Shailender Gupta

International Journal of Analog Integrated Circuits

Performance Analysis of SVM and CNN Networks for Defect Detection in Solar Panel

Abstract

Use of solar panel in generating electricity is increasing globally day by day to meet the increase in demand of power. Many developments have been made in order to achieve higher efficiency of the system. But efficiency of the system may still decrease due to harsh environmental conditions or poor handling of the. These factors highly contribute in the performance degradation of the system. For detection of these irregularities, human level inspections to advance level methods are available in order to enhance the performance metrics. This paper focuses on performance evaluation and comparison of Support Vector Machines (SVM) and Convolution Neural Network (CNN), to find their effectiveness in detection of the defect in Photo-Voltaic cell. To achieve this both SVM and CNN models are trained and tested with the electroluminescence dataset. The simulation results depicts that CNN achieves higher accuracy than SVM on grounds of accuracy (87.97%) and error rate (1.7%)

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How to cite

Vishakha Yadav, Om Dev Singh, Shubham Singh, Dr. Shailender Gupta. Performance Analysis of SVM and CNN Networks for Defect Detection in Solar Panel. International Journal of Analog Integrated Circuits. 2021;7(1):15–24.

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Performance Analysis of SVM and CNN Networks for Defect Detection in Solar Panel

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