Noise Reduction Method for Fiber Optical Current Transformer Based on Mode Decomposition-Sample Entropy

Abstract

When a fiber optic current transformer (FOCT) malfunctions, its output signal is usually accompanied by a large noise, which affects the subsequent fault diagnosis results of the transformer. In response to the above noise issues, this paper constructs a new noise reduction algorithm by combining Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) and Sample Entropy (SE). Above all, the output signal of FOCT is decomposed into multiple fault components using the CEEMDAN algorithm. Then, by comprehensively considering the frequency domain characteristics and sample entropy calculation value of each component, the characteristics of each component are determined, so the power frequency and low-frequency components are selected as the feature vectors representing the fault mode. Finally, the noise components with high sample entropy are eliminated, and the power frequency current components with medium sample entropy and the low-frequency fault feature components with low sample entropy are recombined and superposed to build a fault signal dataset after noise reduction.