Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)                   IJEEE 2025, 21(2): 3617-3617 | Back to browse issues page

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Zahalan A, Mat Zali S, Che Mid E, Fadzail N F. Analysis of Fault Detection and Classification in Photovoltaic Arrays Using Neural Network-Based Methods. IJEEE 2025; 21 (2) :3617-3617
URL: http://ijeee.iust.ac.ir/article-1-3617-en.html
Analysis of Fault Detection and Classification in Photovoltaic Arrays Using Neural Network-Based Methods
Arizadayana Zahalan , Samila Mat Zali , Ernie Che Mid , Noor Fazliana Fadzail
Abstract:   (189 Views)
Photovoltaic (PV) systems are vital in the global renewable energy landscape because of their capability to harness solar energy efficiently. Ensuring the continuous and efficient operation of PV systems is crucial in maximizing their energy contribution. However, these systems' reliability and safety remain critical because they are prone to various faults, mainly when operating in harsh environmental conditions. This study addresses these issues by exploring fault detection and classification in PV arrays using neural network (NN) -based techniques. A PV array model, consisting of 3x6 PV modules, was simulated using MATLAB Simulink to replicate real-world conditions and analyse various fault scenarios. An open circuit, a short circuit, and a degrading fault are the three types of faults considered in this study. The NN was trained on a dataset generated from the MATLAB Simulink model, encompassing normal operating and fault conditions. This training enables the network to learn the distinctive patterns associated with each fault type, enhancing its detection accuracy and classification capabilities. Simulation results demonstrate that the NN-based approach effectively identifies and classifies the three types of faults.
Keywords: Photovoltaic Arrays, Fault Detection, Fault Classification, Neural Network
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Type of Study: Only For Articles of ELECRiS 2024 | Subject: Artificial Intelligence Techniques
Received: 2024/12/19 | Revised: 2025/03/19 | Accepted: 2025/02/22
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.