Efficient Fault Detection in Photovoltaic Systems Using Machine Learning: A Comparative Analysis of Tree-Based Models

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dc.contributor.authorAlqaraghuli, Omar
dc.contributor.authorIbrahim, Abdullahi
dc.date.accessioned2025-08-14T15:14:03Z
dc.date.available2025-08-14T15:14:03Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
dc.descriptionConference name : 15th International Conference on Electrical Engineering, ICEENG 2025. Conference city : Cairo Conference date : 12 May 2025 - 15 May 2025 Conference code : 209654
dc.description.abstractUsing the most recent machine learning techniques, this article examines several defect detection algorithms that may be utilised in photovoltaic (PV) systems. Maintaining energy efficiency and economic stability requires accurate defect detection, which is becoming increasingly important as the use of renewable energy and large-scale photovoltaic systems continue to expand. Faults that are common in photovoltaic systems, such as string, string-to-ground, and string-to-string faults, can have a significant negative influence on production and result in financial losses. The research presents a flaw detection technique that makes use of classifiers such as Decision Tree, Random Forest, XGBoost, Gradient Boosting, and Extra Trees. We used data from a simulated photovoltaic power plant with a capacity of 250 kW. This data included 600 training instances, 50 testing instances, and 30 characteristics. Among the defects that were examined were those that occurred during normal operation (16.67%), string faults (25.5%), string-to-ground faults (24.83%), and string-to-string mistakes (33%). Accuracy, F1 score, recall, precision, and training duration were the aspects that were considered while evaluating the models. Due to the fact that the Decision Tree model earned the maximum accuracy of 95%, an F1-score of 0.949, recall of 0.95, and precision of 0.958, all while requiring just 0.0083 seconds for training, it is an excellent choice for applications that need real-time processing. The next best model was Random Forest, which achieved an accuracy of 88%, an F1-score of 0.879, and a training time of 0.4856 seconds. The accuracies of XGBoost and Gradient Boosting were below average, coming in at 79% and 78%, respectively. Additionally, the training durations for Gradient Boosting were significantly longer, requiring 4.732 seconds. Extra Trees demonstrated the lowest accuracy, with a score of 74%, an F1-score of 0.723, and a training time of 0.3199 seconds.
dc.identifier.citationAlqaraghuli, O., Ibrahim, A. (2025). Efficient Fault Detection in Photovoltaic Systems Using Machine Learning: A Comparative Analysis of Tree-Based Models. 2025 15th International Conference on Electrical Engineering, ICEENG 2025. 10.1109/ICEENG64546.2025.11031355
dc.identifier.doi10.1109/ICEENG64546.2025.11031355
dc.identifier.isbn9798331519018
dc.identifier.scopus2-s2.0-105009458998
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5867
dc.indekslendigikaynakScopus
dc.institutionauthorAlqaraghuli, Omar
dc.institutionauthorIbrahim, Abdullahi
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2025 15th International Conference on Electrical Engineering, ICEENG 2025
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFault detection
dc.subjectMachine learning Classifiers
dc.subjectPerformance evaluation
dc.subjectPhotovoltaic (PV) systems
dc.titleEfficient Fault Detection in Photovoltaic Systems Using Machine Learning: A Comparative Analysis of Tree-Based Models
dc.typeConference Object

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