Abstract
The photovoltaic power generation is affected by many nonlinear variables so, it is very difficult to detect the faults. In order to detect such faults very easily and effectively, a photovoltaic hot spot detection method based on kernel entropy component analysis (KECA) and information gain is proposed in this paper. The method first uses the kernel entropy component analysis to extract the information characteristic data of the sample, then uses the normal sample and the information gain to determine the detection threshold, and finally calculates the detection variable of the faulty sample and compares it with the detection threshold to determine whether any hot spots fault exist. Experiments show that this method can make full use of the inherent information in the data, and exhibits a good hot spot detection effect.
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Jiang, S., Yi, H. (2022). Photovoltaic Hot Spots Detection Based on Kernel Entropy Component Analysis and Information Gain. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13605. Springer, Cham. https://doi.org/10.1007/978-3-031-20500-2_40
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DOI: https://doi.org/10.1007/978-3-031-20500-2_40
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