Dhimish, Mahmoud (2021) Defining the best-fit machine learning classifier to early diagnose photovoltaic solar cells hot-spots. Case Studies in Thermal Engineering. 100980. ISSN 2214-157X
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).
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2021 The Author(s) |
Dates: |
|
Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 02 Sep 2021 14:40 |
Last Modified: | 16 Oct 2024 17:50 |
Published Version: | https://doi.org/10.1016/j.csite.2021.100980 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.csite.2021.100980 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177739 |
Download
Filename: 1_s2.0_S2214157X2100143X_main.pdf
Description: 1-s2.0-S2214157X2100143X-main
Licence: CC-BY 2.5