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Journal of System Simulation

Abstract

Abstract: To address the problem of frequent PV system faults, a multimodal fusion fault diagnosis model based on the optimization of the improved lemming algorithm was proposed. The one-dimensional time series signals of PV currents and voltages were converted into two-dimensional images by Markov transformation field, and the spatial features of the original waveforms were mined by using multiscale CNN (MCCNN); BiGRU was used to extract the temporal dynamic features of the original waveforms, and complementary enhancement of the temporal and spatial features was realized by the feature fusion layer. The improved lemming algorithm was innovatively introduced to adaptively optimize parameters such as the number of neurons in the hidden layer of BiGRU and the learning rate of the model, and the weight assignment of fault-sensitive features was enhanced by combining the assistive technology. The results of simulation experiments have shown that the diagnostic accuracies of the proposed model simulation and the measured data reach 97.9% and 95.4%, respectively. Compared with the comparative models, the diagnostic accuracy is improved by up to 4.1%. The proposed model provides a new technical path for intelligent operation and maintenance of PV systems.

First Page

3018

Last Page

3032

CLC

TP391.9; TM615

Recommended Citation

Li Bin, Wang Yuchuo. Fault Diagnosis Method for Photovoltaic Systems Based on Multi-strategy Fusion[J]. Journal of System Simulation, 2025, 37(12): 3018-3032.

Corresponding Author

Wang Yuchuo

DOI

10.16182/j.issn1004731x.joss.25-0471

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