Faulty Line Selection Based on Modified CEEMDAN Optimal Denoising Smooth Model and Duffing Oscillator for Un-Effectively Grounded System

Abstract

As the un-effectively grounded system fails, the zero-sequence current contains strong noise and nonstationary features. This paper proposes a novel faulty line selection method based on modified complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) and Duffing oscillator. Here, based on multiscale permutation entropy, fuzzy c-means clustering, and general regression neural network for abnormal signal detection, the MCEEMDAN is proposed. The endpoint mirror method is used to suppress the endpoint effect problem in the decomposition stage. The proposed algorithm is able to decompose the original signal into a series of intrinsic mode functions, which can complete the first filtering. The research shows that it can efficiently suppress the mode confusing phenomenon of empirical mode decomposition (EMD) and is also more complete and orthogonal than ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD). The optimal denoising smooth model is established for choosing optimal intrinsic mode functions to complete the second filtering. It can ensure that the reconstructed filtered signal has better smoothness and similarity. The optimal denoising smooth model of MCEEMDAN can not only keep useful details of the original signal but also reduce the noise and smooth signal. The bifurcation characteristic of the chaotic oscillator is applied in weak signal detection. The zero-sequence current’s denoising result is extracted as the input signal of the Duffing system. The faulty line could be selected by observing the phase diagram of the system. The research results verify the usability and effectiveness of the proposed method.