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MF-LSTM for fault diagnosis of new energy electrical equipment

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  • Xiaoxin Yu

Abstract

To enhance the accuracy and reliability of fault diagnosis in new energy electrical equipment, we present a fault diagnosis method based on MF-LSTM (multidimensional fuzzy long short-term memory). It incorporates thermal imaging feature distance optimization for the separation of thermal map feature patterns, then introduces a logic sequence optimization structure to enhance the adaptability of the LSTM model. Additionally, a multifuzzy algorithm is introduced to improve the model’s global understanding of fault features. Results demonstrate that it enhances fault diagnosis accuracy, achieving a precision of 92.31%, surpassing other algorithms and exhibiting superior recognition performance across various fault categories.

Suggested Citation

  • Xiaoxin Yu, 2025. "MF-LSTM for fault diagnosis of new energy electrical equipment," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 1163-1172.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:1163-1172.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf056
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