Fault Diagnosis of Commutation Failure Using Wavelet Transform and Wavelet Neural Network in HVDC Transmission System

Abstract

Commutation failure is the most common fault on the inverter side of the high voltage direct current (HVDC) transmission system. In the actual engineering operation, there are many reasons for the commutation failure. In order to diagnose the causes in real-time and effectively, this article proposes a new method for the fault diagnosis of the commutation failure based on the combination of the wavelet transform and wavelet neural network (WNN). First, by analyzing the changes in electrical characteristics caused by the failure of commutation, the voltage signal of the inverter side commutation bus is finally selected as the feature signal for diagnosis. Then, the wavelet transform is used as a tool for extracting fault feature quantities, and the root mean square (rms) value of the wavelet detail coefficients, wavelet energy skewness, and wavelet energy spectrum information entropy of the inverter side commutation bus voltage signal after wavelet decomposition are used as three feature parameters. At the same time, wavelet packet decomposition is performed on the inverter side commutation bus voltage signal, and the wavelet packet subband energy, energy entropy, and energy skewness are extracted as the other three feature vectors. Finally, the simulation test data shows that the proposed fault diagnosis method can accurately identify the specific fault causes and has high reference value and specific engineering guiding significance for improving the reliability of the HVDC transmission system. Compared with the traditional BP neural network method for fault diagnosis of commutation failure, the proposed diagnosis method considers the fault type more comprehensively, the diagnosis speed is faster, and the recognition accuracy rate is higher in the case of a small number of samples.