In transformer fault diagnosis models based on machine learning

the imbalance of class distribution among fault categories will lead to low diagnosis accuracy. In order to improve the accuracy and the efficiency of the models

a fault diagnosis model based on k-nearest neighbors and neighborhood component analysis with correction factors was proposed. Firstly a correction factor to NCA algorithm’s objective function

was introduced to reduce the impact of sample imbalance on model training. Also

association rules to obtain a parameter correlation quantization matrix according to the fault data

was used as the initial value of NCA algorithm’s training metric matrix. Secondly

the result matrix of NCA was used to map the input data of k NN classifier

which could reduce the distance between samples of the same class and improve the classification performance of the classifier. Finally

the Bayesian optimization algorithm was applied to tune the hyper-parameters of the model to obtain the model parameter set that maximizes the accuracy of test set. The practical value of our model was verified through the data from dataset. The experimental results show that

compared with the traditional machine learning diagnosis models

the proposed method can save nearly half of time

and the diagnostic accuracy of minority classes is improved by at least 15%. The research can provide references for the fault diagnosis of power transformers.