Detection and Separation of On-Load Tap Changer Vibration Signals Based on PSO-VMD

To provide data support for the operating condition monitoring of on-load tap changers (OLTC) in converter transformers, this paper proposes a method for detecting and separating OLTC vibration signals based on measured OLTC vibration signals from the in-service converter transformers and the standby converter transformer. At first, vibration signals with OLTC actions are detected from a large number of vibration signals of the operating converter transformer. Subsequently, the minimum orthogonal coefficients among the decomposed modes generated by Variational Mode Decomposition (VMD) are used as the fitness function for Particle Swarm Optimization (PSO) algorithm. PSO is applied to optimize the initial parameters, including the decomposition components and the penalty parameter. Finally, the vibration signals with OLTC actions are decomposed into multi-scale components. The ineffective components are removed and the remaining components are denoised by using the Dual-Tree Complex Wavelet Transform (DT-CWT) thresholding method. The proposed method effectively detects and separates the OLTC vibration signals from a considerable quantity of vibration signals generated by the in-service converter transformer, laying the foundation for subsequent research on the state monitoring and fault diagnosis of the OLTC in in-service converter transformers.