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.
Highlights
As the un-effectively grounded system fails, the zero-sequence current contains strong noise and nonstationary features. is paper proposes a novel faulty line selection method based on modified complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) and Duffing oscillator
Based on multiscale permutation entropy, fuzzy c-means clustering, and general regression neural network for abnormal signal detection, the MCEEMDAN is proposed. e endpoint mirror method is used to suppress the endpoint effect problem in the decomposition stage. e proposed algorithm is able to decompose the original signal into a series of intrinsic mode functions, which can complete the first filtering. e research shows that it can efficiently suppress the mode confusing phenomenon of empirical mode decomposition (EMD) and is more complete and orthogonal than ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD). e optimal denoising smooth model is established for choosing optimal intrinsic mode functions to complete the second filtering
It is complete and orthogonal than EEMD and CEEMD. e optimal denoising smooth mathematical model, which weights the contradiction between similarity and smoothness of the filtered signal, is established for completing signal reconstruction and effectively extracting useful information from the original signal. e zero-sequence current in each line, which is processed by the MCEEMDAN optimal denoising smooth mathematical model, is extracted as the periodic external dynamic of the Duffing system. e trisection symmetry phase estimation method is applied for searching the critical phase, and each input signal is moved according to the critical phase. e faulty line could be selected by observing the phase diagram of the Duffing system
Summary
FCM, which is an unsupervised learning, uses a membership degree to determine the value of each sample objective, which belongs to a cluster. E MPEs of the signals are clustered by the FCM algorithm. E MPE of 12 × 200 groups of signals is taken as the input of GRNN, and the output is the corresponding signal category. E smoothness factor 0.1212 is brought into the GRNN, and the above MPE of 12 × 200 sample signals is trained to obtain the prediction model. Is shows that the signal’s MPE as the characteristic quantity combining with FCM unsupervised clustering and GRNN tutor learning can complete the random detection of the signal, and the detection rate is maintained at 100%. E distinguishing effect of different types of signals is ideal, and the characteristics of the same type of signals are closely related. is shows that the signal’s MPE as the characteristic quantity combining with FCM unsupervised clustering and GRNN tutor learning can complete the random detection of the signal, and the detection rate is maintained at 100%. is method can be used to identify abnormal signals
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