Partial Discharge Pattern Recognition Based on FrFT Combined RVM
Rapid and accurate identification of partial discharge(PD)types is of great significance for ensuring the safe and stable operation of transformers.This paper proposes a PD pattern recognition method based on fractional Fourier transform(FrFT)and correlation vector machine(RVM)to address the problem of feature selection and classifier design in PD signal pattern recognition.Firstly,it introduces FrFT into the field of PD signal analysis,which is used to transform PD signals into fractional domains for multi-scale analysis.At the same of expanding information extraction dimensions,a feature vector consisting of 14 features that can describe the waveform differences of PD signals corresponding to different discharge types is extracted.Then,the feature vector is used as the input to establish an RVM classification model for joint optimization of feature selection and classification decision functions,so as to achieve optimal feature selection while obtaining optimal pattern recognition results.Finally,the paper establishes experimental models for corona discharge,surface discharge,and air gap discharge,and collects PD ultrasound signals for testing.The results indicate that the proposed method can achieve high recognition accuracy.Under the same conditions,compared to the conventional support vector machine(SVM),the promotion of average correct recognition rate of this method exceeds over 2.7%.
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