改進(jìn)蜣螂優(yōu)化XGBoost的變壓器故障診斷研究
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中圖分類號(hào)：TM411 文獻(xiàn)標(biāo)志碼：A 文章編號(hào):1672-1098(2025)02-0001-09
引文格式：,,.改進(jìn)蜣螂優(yōu)化 XGBoost 的變壓器故障診斷研究［J].安徽理工大學(xué)學(xué)報(bào)（自然科學(xué)版），2025,45(2) :1-9.
Transformer Fault Diagnosis Based on Idbo-optimized XGBoost LI Hongyue,LI Chunyu ,LI Tao
(Scholof Electricaland Information Engineering,Anhui UniversityofScienceandTechnology,Huainan Anhui 232Ool,Chna) Abstract:Objective To address the problems of difficulty in selecting key parameters for extreme gradient boosting（XGBoost）,and the dung beetle optimizer（DBO）being prone to local optima,which lead to low accuracy in transformer fault diagnosis. Methods By using 14- dimensional gas feature vectors as input,a transformer fault diagnosis model based on IDBO-optimized XGBoost was proposed.Firstly,to improve the algorithm's optimization capability,the four stages of the DBO algorithm were improved by Tent chaos mapping,adaptive spiral strategy, Cauchy-Gaussian mutation,and T-disturbance.Then,by comparative optimization tests with the original DBO, grey wolf optimizer（GWO）,sparrow search algorithm （SSA） and whale optimization algorithm（WOA）,the superiority of the IDBO algorithm was demonstrated.Finally,in fault diagnosis comparative experiments,the IDBOXGBoost model was compared with XGBoost models optimized byusing DBO,SSA and GWO,as well as with multiple machine learning methods optimized by using DBO and the IEC standard method.Results The results showed that the IDBO-XGBoost model achieved an accuracy of 91.76% and a Kappa coefficient of O. 900 8, which demonstrated better fault diagnosis effectiveness.Conclusion The IDBO-XGBoost model can effectively improve fault diagnosis accuracy,providing an effective solution for transformer fault diagnosis.
Key words : transformer;fault diagnosis;dung beetle optimizer; XGBoost
電力變壓器作為電力系統(tǒng)中重要的一環(huán)，其穩(wěn)定運(yùn)行對(duì)系統(tǒng)正常工作具有關(guān)鍵性作用。（剩余14122字）