Mechanical Fault Diagnosis of On-load Tap-changer Based on ANHGA-VMD and Coupled Hidden Markov Model

Mechanical fault is the main fault type of on-load tap-changer (OLTC). To solve the problem that it is difficult to determine the influence of variational mode decomposition (VMD) parameter settings on the decomposition quality and to improve the mechanical fault diagnosis accuracy of OLTC, a mechanical fault diagnosis method of OLTC based on multichannel vibration signal is presented. Firstly, the adaptive niched hierarchical genetic algorithm (ANHGA) with stable optimization capability is adopted to optimize the VMD parameters, in which the quality factor are used as the criterion to measure the signal decomposition quality. After that, the improved VMD method is used to decompose the pretreated multichannel vibration signals, and the energy entropy and fuzzy entropy of VMD are extracted as eigenvalues. Finally, a coupled hidden Markov model (CHMM) is established for fault diagnosis of multichannel data. The case study results show that the optimized VMD can effectively improve the decomposition quality of VMD signals. The diagnostic accuracy of the seven typical states of OLTC based on the CHMM is 100%, which is higher than the correct rate of fault diagnosis methods of hidden Markov model (HMM) and support vector machine (SVM). A new idea for the diagnosis of complex electromechanical equipment with few samples, which is difficult to sample, is provided by the proposed method. © 2021, High Voltage Engineering Editorial Department of CEPRI. All right reserved.