Machine Learning-Based Fault Diagnosis for Rooftop Photovoltaic Systems
DOI:
https://doi.org/10.46604/ijeti.2026.16514Keywords:
rooftop photovoltaic, fault diagnosis, hybrid CNN–LSTM, data-driven, deep learningAbstract
This study aims to develop a deep learning framework for intelligent fault detection, classification, and multi-level localization in rooftop photovoltaic (PV) systems. The proposed approach uses a hybrid CNN–LSTM architecture that combines a one-dimensional convolutional neural network (1D-CNN) to identify local spatial patterns with long short-term memory (LSTM) units to capture long-term temporal dependencies in time-series data. The framework was evaluated on a multivariate dataset collected from a grid-connected experimental rooftop PV prototype under various fault scenarios. Experimental results demonstrate that the hybrid model significantly outperforms standalone 1D-CNN and LSTM architectures across all evaluation criteria. Specifically, the proposed framework achieved classification accuracies of 99.96% for fault type identification, 98.76% for string-level fault localization, and 99.21% for module-level fault localization. These findings highlight the effectiveness and robustness of the hybrid framework for real-time monitoring and reliable fault diagnosis in photovoltaic installations.
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Copyright (c) 2026 Mi Sa Nguyen Thi, Xuan-Duy Do, Dinh-Nguyen Tran, Dinh-Nhon Truong, Van-Phuong Ta
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