Transformer winding looseness diagnosis method based on multiple feature extraction and sparrow search algorithm optimized XGBoost
In order to solve the problem of overlap and insufficient anti-interference ability under different load conditions in diagnosing transformer winding looseness using a single feature quantity,a vibration signal diagnosis method for transformer winding looseness based on kernel principal component analysis(KPCA)and extreme gradient boosting(XGBoost)optimized by improved sparrow search algorithm(SSA)was proposed.Firstly,feature quantities in vibration signals were extracted from three dimen-sions:time domain,frequency domain,and entropy;Then,the feature quantity was dimensionally re-duced through grid search optimized KPCA;Finally,a fault diagnosis model based on XGBoost was con-structed and sparrow search algorithm was used to optimize the parameters for achieving accurate identifi-cation of transformer winding looseness faults under different currents.The experimental verification was conducted on a 110 kV transformer.The diagnosis results show that the extracted feature quantities can accurately reflect the fault characteristics,have stronger anti-interference ability,and the diagnostic accu-racy rate of the diagnostic model is 99.00%.Compared with other diagnostic algorithms,the accuracy and stability are higher,and have good recognition effects under different load conditions.
transformer vibrationwinding loosenesskernel principal component analysisXGBoostsparrow search algorithmfault diagnosis
NETL
NSTL
万方数据
