Solar energy paper index
Development of an Integrated DBN-ELM, CNN-SVM and CNN-BiGRU Photovoltaic Array Fault Diagnosis Model Based on Weighted Probability Averaging
One-line summary
A solar energy research paper on Development of an Integrated DBN-ELM, CNN-SVM and CNN-BiGRU Photovoltaic Array Fault Diagnosis Model Based on Weighted Probability Averaging.
Engineering notes
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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
Photovoltaic arrays are continuously exposed to complex environmental conditions over long periods, making them susceptible to seven types of single and multiple failures such as shadow blocking and module aging. Fault diagnosis of photovoltaic arrays is essential to prevent failures that may lead to reduced power generation efficiency and potential safety hazards. This paper proposes a photovoltaic array fault diagnosis model based on weighted probability averaging, integrating DBN-ELM, CNN-SVM, and CNN-BiGRU methods. The model is calculated and experimentally verified. The results demonstrate that the integrated model achieves an overall accuracy, recall, precision, and F1-score of 99.0% across the four evaluation metrics, indicating a highly effective fault recognition capability.
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