Cover
Vol. 21 No. 1 (2025)

Published: September 19, 2025

Pages: 126-133

Original Article

Enhancing PV Fault Detection Using Machine Learning: Insights from a Simulated PV System

Halah Sabah Muttashar 1 Amina Mahmoud Shakir
1Department of Electronic and Communications Engineering, Al-Nahrain University, Iraq
History
  • Received: July 21, 2023
  • Revised: September 2, 2023
  • Accepted: December 22, 2023
  • Available Online: December 5, 2024
DOI

https://doi.org/10.37917/ijeee.21.1.12

Abstract

Recently, numerous researches have emphasized the importance of professional inspection and repair in case of suspected faults in Photovoltaic (PV) systems. By leveraging electrical and environmental features, many machine learning models can provide valuable insights into the operational status of PV systems. In this study, different machine learning models for PV fault detection using a simulated 0.25MW PV power system were developed and evaluated. The training and testing datasets encompassed normal operation and various fault scenarios, including string-to-string, on-string, and string-to-ground faults. Multiple electrical and environmental variables were measured and exploited as features, such as current, voltage, power, temperature, and irradiance. Four algorithms (Tree, LDA, SVM, and ANN) were tested using 5-fold cross-validation to identify errors in the PV system. The performance evaluation of the models revealed promising results, with all algorithms demonstrating high accuracy. The Tree and LDA algorithms exhibited the best performance, achieving accuracies of 99.544% on the training data and 98.058% on the testing data. LDA achieved perfect accuracy (100%) on the testing data, while SVM and ANN achieved 95.145% and 89.320% accuracy, respectively. These findings underscore the potential of machine learning algorithms in accurately detecting and classifying various types of PV faults. .

Keywords

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