Defining the best-fit machine learning classifier to early diagnose photovoltaic solar cells hot-spots

Research output: Contribution to journalArticlepeer-review

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).
Original languageEnglish
Article number100980
Number of pages10
JournalCase Studies in Thermal Engineering
Volume25
Early online date7 Apr 2021
DOIs
Publication statusPublished - 1 Jun 2021

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