September 10, 2025Frontiers in Energy ResearchOpen Access

Early fault diagnosis of transformer windings based on the improved MVMD-ELM

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The method achieves diagnostic accuracy rates of 98.33% for axial compression deformation and 96.67% for inter-turn short circuits.
Optimization of extreme learning machine parameters through the Dream Optimization Algorithm significantly enhances model performance.
Utilizing multivariable mode decomposition allows for better signal quality by efficiently denoising noises in the fault characteristics.
Compared to traditional methods like BPNN and SVM, the proposed approach significantly improves classification accuracy and computational efficiency.

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Implication

This paper demonstrates an innovative method to enhance fault diagnosis accuracy in transformer windings, suggesting improved classification efficiency.

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Lin et al. (2025) studied this question.

synapsesocial.com/papers/68c23d3ab210217d6478cd59 https://doi.org/10.3389/fenrg.2025.1645135

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Implication

This paper demonstrates an innovative method to enhance fault diagnosis accuracy in transformer windings, suggesting improved classification efficiency.

Early fault diagnosis of transformer windings based on the improved MVMD-ELM

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Implication

Cite This Study

Early fault diagnosis of transformer windings based on the improved MVMD-ELM

Key Points

Discussion

Implication

Cite This Study

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