Year: 2025
Type: article
Abstract: Early detection of performance degradation and prevention of critical failures in photovoltaic (PV) arrays are essential for ensuring system reliability and efficiency. This study presents an intelligent fault detection and classification framework based on a Multi-Layer Neural Network (MLNN). The model was developed and validated using a simulated 250 kW grid-connected PV system tested under five operating scenarios: normal operation, open-circuit fault, partial short-circuit, partial shading, and string-to-string fault. Unlike conventional diagnostic approaches, the proposed model directly processes raw electrical measurements (current, voltage, power, irradiance, and temperature) under varying environmental conditions, thus emulating real-world operational variability. The MLNN achieved 98% test accuracy and outperformed benchmark classifiers Support Vector Machine (SVM) and Random Forest (RF) across multiple metrics. Performance was evaluated using the confusion matrix, precision, recall (sensitivity) and F1-score. The framework is designed for scalability and can be integrated into predictive maintenance platforms to enable early fault detection and improve long-term PV system availability and efficiency.
Source: Processes
DOI: 10.3390/pr13123831
Authors: Aliaa Freej, Asmaa Sobhy Sabik, Ibrahim A. Nassar
Institution: Al-Azhar University
Language: English
Field: Energy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Funder: Al-Azhar University
OpenAlex ID: W7106800117