Research on Improving Leapfrog Algorithm to Optimize Transformer Fault Diagnosis with Support Vector Machine

The transformer fault diagnosis model based on the improved leapfrog algorithm support vector machine is significantly higher in prediction accuracy than other intelligent optimization algorithms, and the transformer equipment fault diagnosis model is more feasible. Therefore, the algorithm studied in this paper will also have potential application value in the daily operation and maintenance management of substation power grid.

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Published
2025-04-11
DOI
https://doi.org/10.1109/icmtim65484.2025.11040792
Primary Topic
Advanced Decision-Making Techniques
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article
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article

Research on Improving Leapfrog Algorithm to Optimize Transformer Fault Diagnosis with Support Vector Machine

Qian Zhang, Yunhai Hou
Advanced Decision-Making Techniques
article

Research on Improving Leapfrog Algorithm to Optimize Transformer Fault Diagnosis with Support Vector Machine

Qian Zhang, Yunhai Hou
article en

Abstract

The transformer fault diagnosis model based on the improved leapfrog algorithm support vector machine is significantly higher in prediction accuracy than other intelligent optimization algorithms, and the transformer equipment fault diagnosis model is more feasible. Therefore, the algorithm studied in this paper will also have potential application value in the daily operation and maintenance management of substation power grid.

Changchun University of Technology (CN)
Openalex Percentile: Top 78%
Advanced Decision-Making Techniques
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Research on Improving Leapfrog Algorithm to Optimize Transformer Fault Diagnosis with Support Vector Machine — Qian Zhang, Yunhai Hou · (2025) | TGRS Research Map | TGRS