Fault diagnosis method for new energy electrical equipment referring to KMFCC-BOA-CNN-1D

Ran Ma

International Journal of Low-Carbon Technologies, 2025, vol. 20, 1697-1705

Abstract: The development of battery energy storage is a significant initiative in support of the construction of new power systems. However, frequent switching of the energy storage converter can lead to component degradation and failures. To address the challenges in diagnosing various fault types in current converters, a new fault diagnosis method based on KMFCC-BOA-CNN-1D is proposed. The Matlab/Simulink platform is used to simulate the open-circuit fault dataset, and the accuracy of the model is 97.88% under noise-free conditions. The accuracy of the model in this paper compared with the empirical mode decomposition feature extraction method and the support vector machine model increased by 2.74% and 6% respectively, which verified the effectiveness of the model in this paper.

Keywords: energy storage converter; deep learning; electrical equipment; Mel-frequency cepstral coefficients; fault diagnosis (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:1697-1705.

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