en Artificial Intelligence Techniques Artificial Intelligence Techniques Only For Articles of ELECRiS 2024 Only For Articles of ELECRiS 2024 <span style="font-size:10.0pt"><span new="" roman="" style="font-family:" times="">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&#39; 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.</span></span> Photovoltaic Arrays, Fault Detection, Fault Classification, Neural Network 107 115 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-5493-1&slc_lang=en&sid=1 Arizadayana Zahalan 1800319475328460016489 1800319475328460016489 No Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia Samila Mat Zali samila@unimap.edu.my 1800319475328460016490 1800319475328460016490 Yes Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia Ernie Che Mid 1800319475328460016491 1800319475328460016491 No Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia Noor Fazliana Fadzail 1800319475328460016492 1800319475328460016492 No Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia