FAULT DIAGNOSIS OF PHOTOVOLTAIC ARRAY BASED ON DIGITAL TWIN AND FUSION NEURAL NETWORK

Liu Weiliang; Jiang Kaiyue; Xu Zhisheng; Liu Shuai; Liu Changliang; Wang Xin

doi:10.19912/j.0254-0096.tynxb.2023-1124

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (11) : 303-312. DOI: 10.19912/j.0254-0096.tynxb.2023-1124

FAULT DIAGNOSIS OF PHOTOVOLTAIC ARRAY BASED ON DIGITAL TWIN AND FUSION NEURAL NETWORK

Liu Weiliang^1,2, Jiang Kaiyue¹, Xu Zhisheng¹, Liu Shuai^1,2, Liu Changliang^1,2, Wang Xin³

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Abstract

Photovoltaic power stations are mostly located in harsh environments, so timely and accurate diagnosis of various faults is particularly important. A fault diagnosis method of photovoltaic array based on digital twin model and fusion neural network is proposed. Firstly, the overall framework of digital twin system of PV power station is designed and implemented, which includes twin model, data acquisition and transmission module and service application system. Secondly, combining the mechanism modeling method and particle swarm optimization （PSO） algorithm, the digital twin model of photovoltaic array is established. Thirdly, the fault is detected by evaluating the residual difference between the output of digital twin model and the output of physical entity. Finally, the fault diagnosis of photovoltaic array is carried out by combining temporal convolution network （TCN） with bidirectional gated recurrent unit （BIGRU）. The experimental results show that the proposed fault diagnosis method has higher accuracy than other methods, and the accuracy rate reaches 97.8%.

Key words

digital twin / photovoltaic arrays / particle swarm optimization algorithm / temporal convolution network / bidirectional gated recurrent unit / fault diagnosis

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Liu Weiliang, Jiang Kaiyue, Xu Zhisheng, Liu Shuai, Liu Changliang, Wang Xin. FAULT DIAGNOSIS OF PHOTOVOLTAIC ARRAY BASED ON DIGITAL TWIN AND FUSION NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2024, 45(11): 303-312 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1124

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