Research on photovoltaic system malfunction identification based on deep convolutional neural network

Fei Wu; Moquan Liu; Linwei Zhao

doi:10.1117/12.2686334

10 August 2023 Research on photovoltaic system malfunction identification based on deep convolutional neural network

Fei Wu, Moquan Liu, Linwei Zhao

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127592N (2023) https://doi.org/10.1117/12.2686334
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

At present, photovoltaic system transfers solar energy to electricity and has become a fundamental component in electronic power supply module. However, the occurrence of system faults extremely influence the stabilization and effectiveness of photovoltaic system. Additionally, existing researches on photovoltaic system faults detection are primarily concentrated on the voltages signals or mathematical analysis models, which leaks the detection accuracy and costs extraordinary computation times. In this paper, we utilize deep learning method to obtain the features from input system data, which contains system fault information. Indeed, the trained model can distinguish the faults nodes from these features and response faults information. Subsequently, we devise loss function to improve the model learning rate and enhance the detection accuracy. From our extensive simulation results, we can conclude that our devised method can successfully identify the faults nodes in photovoltaic system with reasonable computation costs and precise detection results through comparing with traditional detection methods.

Citation Download Citation

Fei Wu, Moquan Liu, and Linwei Zhao "Research on photovoltaic system malfunction identification based on deep convolutional neural network", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127592N (10 August 2023); https://doi.org/10.1117/12.2686334

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