Key parameters of LightGBM.

<div><p>A fault diagnosis method for oil immersed transformers based on principal component analysis and SSA LightGBM is proposed to address the problem of low diagnostic accuracy caused by the complexity of current oil immersed transformer faults. Firstly, data on dissolved gases in oil...

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Bibliographic Details
Main Author:	Jizhong Wang (7441697) (author)
Other Authors:	Jianfei Chi (20752518) (author), Yeqiang Ding (20752521) (author), Haiyan Yao (18501084) (author), Qiang Guo (218747) (author)
Published:	2025
Subjects:	Molecular Biology Biotechnology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified principal component analysis obtain joint features fault diagnosis performance experimental results show eliminate information redundancy construct fused features uncoded ratio method fault diagnosis method oil immersed transformers ssa algorithm optimization lightgbm model proposed xlink "> ssa lightgbm lightgbm oil feature matrix feature fusion effectively improve dissolved gases diagnostic accuracy classification ability
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Key parameters of LightGBM.

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