Solar energy paper index
Enhancing the Reliability of PV Fault Diagnosis through a Hybrid CNN–DT Approach
One-line summary
A solar energy research paper on Enhancing the Reliability of PV Fault Diagnosis through a Hybrid CNN–DT Approach.
Engineering notes
Engineering notes will be added by the Power for Solar editorial team.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。
Original abstract
This paper presents a hybrid diagnostic approach combining a one-dimensional convolutional neural network (1D-CNN) and a Decision Tree (DT) for automatic fault detection in photovoltaic (PV) systems. The CNN performs feature extraction and preliminary classification using normalized electrical and environmental data, while the DT refines predictions based on confidence scores, irradiance, and temperature. Experimental results show that the proposed CNN–DT framework improves diagnostic accuracy, interpretability, and robustness compared to conventional methods, ensuring reliable operation of PV installations under varying conditions.
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