Fault Identification of Hybrid Distribution Transformer Based on Wavelet Transform and Logistic Regression
Zhang Lishi1,2, Liang Deliang1,2, Liu Hua1,2, Liu Yibin1,2, Li Dawei1,2
1. State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong University Xi’an 710049 China; 2. Shaanxi Key Laboratory of Smart Grid Xi’an Jiaotong University Xi’an 710049 China
Abstract：Hybrid distribution transformer (HDT) can replace traditional distribution transformers in intelligent distribution networks to achieve reactive power compensation, harmonic control, voltage regulation and other functions. In order to distinguish between the internal fault of the transformer and the power electronic fault when the HDT fails, this paper first obtains a large amount of current characteristic data of primary side, secondary side, tertiary side and quaternary side under different fault conditions of HDT through simulation. Then, with the help of wavelet transform theory, four-layer discrete wavelet transform is performed on the obtained data and the normalized energy, the normalized energy moment and sample entropy of the data in the wavelet domain are used as the eigenvalues of the current characteristic data. Using machine learning technology, a logistic regression classifier is constructed, and the feature matrix composed of feature values is used as the input of the classifier, and the model is trained to obtain a classifier model with good receiver operating characteristic (ROC) curve and confusion matrix performance. Finally, the data was randomly extracted many times, and the accuracy of the HDT fault recognition of the machine learning model obtained by training was about 90%.
2021, Vol. 36 
