Defining the best-fit machine learning classifier to early diagnose photovoltaic solar cells hot-spots. (June 2021)

• Record Type: Journal Article
• Authors: Dhimish, Mahmoud
• Is Part Of: Case studies in thermal engineering. Volume 25(2021)
• Publication Date: 2021-06
• Subjects: Photovoltaics -- Hot-spots -- Machine learning -- Artificial intelligence -- Classification; Heat engineering -- Case studies -- Periodicals; 621.40205
• DOI: 10.1016/j.csite.2021.100980
• Languages: English
• ISSNs: 2214-157X

Abstract

Abstract: Photovoltaic (PV) hot-spots is a reliability problem in PV modules, where a cell or group of cells heats up significantly, dissipating rather than producing power, and resulting in a loss and further degradation for the PV modules' performance. Therefore, in this article, we present the development of a novel machine learning-based (ML) tool to diagnose early-stage PV hot-spots. To achieve the best-fit ML structure, we compared four distinct machine learning classifiers, including decision tree (DT), support vector machine (SVM), K-nearest neighbour (KNN), and the discriminant classifiers (DC). Results confirm that the DC classifiers attain the best detection accuracy of 98%, while the least detection accuracy of 84% was observed for the decision tree. Furthermore, the examined four classifiers were also compared in terms of their performance using the confusion matrix and the receiver operating characteristics (ROC).