Identification of gradual failure time-frequency feature in fiber optical current transformer

Abstract

Decomposing the output signal of fiber optical current transformer (FOCT) with time-frequency conversion is the key step to obtain the gradual fault characteristics. Aiming at the characteristics of FOCT gradual fault signal with large time domain span and random deterioration process,the output signal is sampled across intervals,and the wavelet packet decomposition algorithm is used to extract fault signal features according to the frequency band of the fault signal. The characteristic parameters are screened to obtain the optimal characteristic parameters that characterize the FOCT degradation trend.The principal component analysis method is proposed to reduce the dimensionality of high-dimensional features,the problem of high signal feature dimensionality is solved,and the need for fast fault feature identification is met. Experiment results show that the wavelet algorithm can decompose the signal into various frequency bands and obtain 64 sub-sequences containing signals of different frequency bands. The operating status of the transformer is determined by the energy ratio of each frequency band of the wavelet signal,and the gradual fault identification is realized.

Keywords

• fiber optical current transformer (foct)
• fault diagnosis
• wavelet transform
• frequency domain feature
• time domain feature
• smart decomposition algorithm