On-load tap-changer fault identification method based on vibration signal characteristic extraction

Abstract

An on-load tap-changer fault identification method based on vibration signal characteristic extraction is disclosed. The method is characterized by using a simulated annealing algorithm to carry out parameter selection; and adaptively selecting a modal number K and a second penalty factor alpha according to a vibration signal to obtain an optimal parameter combination. The effect of vibration signal modal decomposition is improved, which is good for extracting more significant characteristics and is convenient for subsequent state monitoring and fault diagnosis.

Description

technical field [0001] The invention belongs to the technical field of power equipment, relates to the field of mechanical vibration signal processing of power equipment, and in particular relates to a signal processing method for mechanical vibration signals of an on-load tap changer of a power transformer. Background technique [0002] The on-load tap-changer is the only moving key component in a power transformer. The feature recognition and fault diagnosis of the mechanical vibration signal of the on-load tap-changer can judge the operating status of the switch in time, which is conducive to ensuring the safety of the equipment and the reliable operation of the power grid. [0003] Since the mechanical vibration signal of the on-load tap-changer is nonlinear and non-stationary, the time domain or frequency characteristics extracted by wavelet transform or fast Fourier transform are not a stable and effective index, and cannot accurately reflect the vibration signal. int...

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Problems solved by technology

However, since the EMD method takes the envelope of the vibration signal, the extreme points of the signal are unevenly distributed, which will lead to the occurrence of mode aliasing, so that the physical meaning of the decomposed eigenmode components is not clear. The effect of extracting the effect

At the same time, the EMD algorithm lacks a strict mathematical foundation, the algorithm has low efficiency, and the anti-noise performance is poor. Therefore, it is necessary to study a new mode decomposition algorithm to further avoid the mode aliasing phenomenon, improve the operation efficiency, and optimize the effect of feature extraction.

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