Based on PCA and SSA-LightGBM oil-immersed transformer fault diagnosis method

• Published in: PloS one Vol. 20; no. 2; p. e0314481
• Main Authors: Wang, Jizhong, Chi, Jianfei, Ding, Yeqiang, Yao, Haiyan, Guo, Qiang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.02.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Biology and Life Sciences; Boosting Machine Learning Algorithms; Classification; Computer and Information Sciences; Diagnostic systems; Dimensional analysis; Dissolved gases; Electric transformers; Electrical engineering; Engineering and Technology; Fault diagnosis; Gases; Hydrocarbons; Medical diagnosis; Methods; Models, Theoretical; Neural networks; Oils & fats; Oils - chemistry; Optimization; Physical Sciences; Principal Component Analysis; Principal components analysis; Redundancy; Research and Analysis Methods; Standard scores; Standardization; Support vector machines; Testing; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0314481

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 is collected, and a 17 dimensional fault feature matrix is constructed using the uncoded ratio method. The feature matrix is then standardized to obtain joint features. Secondly, principal component analysis is used for feature fusion to eliminate information redundancy between variables and construct fused features. Finally, a transformer diagnostic model based on SSA-LightGBM was constructed, and the ten fold cross validation method was used to verify the classification ability of the model. The experimental results show that the SSA-LightGBM model proposed in this paper has an average fault diagnosis accuracy of 93.6% after SSA algorithm optimization, which is 3.6% higher than before optimization. At the same time, compared with the GA-LightGBM and GWO-LightGBM fault diagnosis models, SSA-LightGBM has improved the diagnostic accuracy by 8.1% and 5.7% respectively, verifying that this method can effectively improve the fault diagnosis performance of oil immersed transformers and is superior to other similar methods.