GRU-BAYESIAN BASED METHOD FOR DISTRIBUTED PHOTOVOLTAIC POWER ANOMALY DETECTION

Wang Yaolong; Wu Yuzhou; Liu Yunyi; Li Bin; Su Sheng

doi:10.19912/j.0254-0096.tynxb.2023-0465

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (7) : 494-501. DOI: 10.19912/j.0254-0096.tynxb.2023-0465

GRU-BAYESIAN BASED METHOD FOR DISTRIBUTED PHOTOVOLTAIC POWER ANOMALY DETECTION

Wang Yaolong¹, Wu Yuzhou², Liu Yunyi², Li Bin¹, Su Sheng¹

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Abstract

For the effective identification of faults in distributed photovoltaic（PV） systems, this study proposes a GRU-Bayesian neural network-based method for anomaly detection in the power output of distributed PV systems.Firstly, sunny days are selected as the detection scene to reduce the interference of weather factors. Then, the gray absolute correlation degree algorithm is introduced to screen out unqualified PV output data by utilizing the similarity of PV system output in the same region and construct a dataset of normal PV output for users. The GRU-Bayesian neural network is used to train and obtain the normal PV power interval for detection. Finally, actual data is used for case analysis, demonstrating the feasibility and effectiveness of the proposed method.

Key words

distributed power generation / PV / Bayesian networks / anomaly detection / grey correlation analysis / GRU

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Wang Yaolong, Wu Yuzhou, Liu Yunyi, Li Bin, Su Sheng. GRU-BAYESIAN BASED METHOD FOR DISTRIBUTED PHOTOVOLTAIC POWER ANOMALY DETECTION[J]. Acta Energiae Solaris Sinica. 2024, 45(7): 494-501 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0465

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