scholarly journals Optimal Denoising and Feature Extraction Methods Using Modified CEEMD Combined with Duffing System and Their Applications in Fault Line Selection of Non-Solid-Earthed Network

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 536 ◽  
Author(s):  
Sizu Hou ◽  
Wei Guo
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Line ◽  
Fault Line Selection ◽  
Duffing System ◽  
Mode Decomposition ◽  
Force Signal ◽  
Zero Sequence ◽  
Line Selection

As the non-solid-earthed network fails, the zero-sequence current of each line is highly non-stationary, and the noise component is serious. This paper proposes a fault line selection method based on modified complementary ensemble empirical mode decomposition (MCEEMD) and the Duffing system. Here, based on generalized composite multiscale permutation entropy (GCMPE) and support vector machine (SVM) for signal randomness detection, the complementary ensemble empirical mode decomposition is modified. The MCEEMD algorithm has good adaptability, and it can restrain the modal aliasing of empirical mode decomposition (EMD) at a certain level. The Duffing system is highly sensitive when the frequency of the external force signal is the same as that of the internal force signal. For automatically identifying chaotic characteristics, by using the texture features of the phase diagram, the method can quickly obtain the numerical criterion of the chaotic nature. Firstly, the zero-sequence current is decomposed into a series of intrinsic mode functions (IMF) to complete the first noise-reduction. Then an optimized smooth denoising model is established to select optimal IMF for signal reconstruction, which can complete the second noise-reduction. Finally, the reconstructed signal is put into the Duffing system. The trisection symmetry phase estimation is used to determine the relative phase of the detection signal. The faulty line in the non-solid-earthed network is selected with the diagram outputted by the Duffing system.

Fault Line Selection in Resonant Grounded System Based on IMEn

2014 ◽  
Vol 960-961 ◽  
pp. 755-758
Author(s):  
Meng Zhang ◽  
Jian Wen Ren
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal Analysis ◽  
Single Phase ◽  
Sample Entropy ◽  
Fault Current ◽  
Fault Line ◽  
Fault Line Selection ◽  
Ground Fault ◽  
Mode Decomposition ◽  
Line Selection

When the single phase ground fault occurs in resonant grounded system, there are significant differences in the components of fault phase current between fault line and non-fault line. This feature can be reflected by intrinsic mode entropy (IMEn) of fault current. IMEn is a new signal analysis over multiple oscillation levels. It corresponds to the sample entropy (SampEn) and the empirical mode decomposition (EMD). The results of fault simulations in different conditions indicate that the method is reliable.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Sizu Hou ◽  
Wei Guo
Keyword(s):  
Signal Detection ◽  
Empirical Mode Decomposition ◽  
Duffing Oscillator ◽  
Ensemble Empirical Mode Decomposition ◽  
Original Signal ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Smooth Model ◽  
Zero Sequence ◽  
Line Selection

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.

scholarly journals Small Current Fault Line Selection Based on Improved Empirical Mode Decomposition and Fractal Dimension Method

CONVERTER ◽  
2021 ◽  
pp. 09-18
Author(s):  
Chao Liu, Limei Yan,Yina Zhou
Keyword(s):  
Fractal Dimension ◽  
Fault Line ◽  
Small Current ◽  
Grounding System ◽  
Scale Free ◽  
Fault Line Selection ◽  
Mode Decomposition ◽  
Zero Sequence ◽  
Line Selection ◽  
Line Slope

In this paper, the fault steady state and transient characteristics of small current grounding system are analyzed, and the distribution of transient zero sequence current is introduced. A fault line selection based on EMD and fractal dimension method is proposed. After the parameter is determined, the problem is proposed and improved. Using the simulated annealing K-means algorithm to find the scale-free interval curve to get the line slope is the correlation dimension of the line. Finally, by comparing the size of the associated dimension, you can select the corresponding line of the faulty distribution network.

Fault Line Selection and Section Location of Distribution Network Based on S-Transform and Zero-Sequence Reactive Power

2013 ◽  
Vol 442 ◽  
pp. 176-182
Author(s):  
Nan Hua Yu ◽  
Rui Li ◽  
Hong Guang Cao ◽  
Wei Qing Tao ◽  
Chuan Jian Li
Keyword(s):  
Reactive Power ◽  
Fault Line ◽  
Fault Line Selection ◽  
S Transform ◽  
Transient Energy ◽  
Zero Sequence ◽  
Line Selection ◽  
The Difference ◽  
Main Station ◽  
Fault Information

An approach of fault line selection and section location based on S-Transform transient energy integrated with zero-sequence reactive direction is proposed. Fault information of zero-sequence currents collected by each feeder terminal unit (FTU) were uploaded to the main station on the use of real-time communication technologies,and then the main station take analysis for the whole network fault information, using S-transform to obtain the main characteristic frequency of the signal, then compare the transient energy and zero-sequence reactive direction of each feeder line of the frequency to achieve fault line selection. According to the difference of waveform in front and back of the fault point in the fault line,using this above method to realize section location. The simulations and analysis demonstrate the accuracy and reliability of this approach , and its not affected by fault close angle, noise and other factors.

A Novel Fault Line Selection Method of Distribution Network Based on Wavelet Packet Energy

2013 ◽  
Vol 340 ◽  
pp. 572-577 ◽  
Author(s):  
Xiao Wei Wang ◽  
Yu Jun Zhang ◽  
Ya Xiao Hou
Keyword(s):  
Wavelet Packet ◽  
Distribution Networks ◽  
Maximum Energy ◽  
Fault Line ◽  
Line Energy ◽  
Branch Line ◽  
Fault Line Selection ◽  
Zero Sequence ◽  
Line Selection ◽  
Base Band

This article introduces a novel fault line selecting method which is based on the wavelet packet energy. With the db10 wavelet packet decomposition of zero-sequence transient current signal of the fault branch line, it can eliminate the component of base band of reflecting the fundamental frequency. Based on this, it can get wavelet energy of the various branch line. If a line of energy is obvious superior in contrast with the other line energy, then it is the first decision for branch line fault. Secondly, it can give the possible fault line sorting according to the principle of from large to smalland start the circuit recluse to monitor the current of the order in sequence. While 3U0 is disappeared, then determining the fault line is the maximum energy lines. If not, then continuing to monitor energy big line until the fault line is selected. The simulation results show that the accuracy rate of the fault circuit selecting method is higher, it can be applied to the fault circuit selecting of cable mixed in distribution networks.

scholarly journals Single-phase-to-ground Fault Line Selection Method Based on STOA-SVM

2021 ◽  
Vol 2095 (1) ◽  
pp. 012029
Author(s):  
Yicen Liu ◽  
Xiaojiang Liu ◽  
Songhai Fan ◽  
Xiaomin Ma ◽  
Sijing Deng
Keyword(s):  
Single Phase ◽  
Distribution Network ◽  
Ensemble Empirical Mode Decomposition ◽  
Selection Method ◽  
Support Vector ◽  
Ground Fault ◽  
Mode Decomposition ◽  
Before And After ◽  
Zero Sequence ◽  
Line Selection

Abstract In view of the complex characteristics of nonlinearity and non-stableness of the zero-order current of each line after the single-phase ground fault of the distribution network, a distribution network fault selection method based on Sooty Tern Optimization Algorithm(STOA) and the combination of support vector machine is proposed. At first, the zero-sequence current before and after fault is obtained, then five kinds of IMFs including different components are obtained by ensemble empirical mode decomposition, and the energy entropy of the fault transient zero-sequence current is obtained by Hilbert transform, the results of training and testing are obtained by inputting the feature vector. The simulation results show that the accuracy of the proposed line selection model is 97.5%.

Random Noise Reduction Based on Ensemble Empirical Mode Decomposition and Wavelet Threshold Filtering

2012 ◽  
Vol 518-523 ◽  
pp. 3887-3890 ◽  
Author(s):  
Wei Chen ◽  
Shang Xu Wang ◽  
Xiao Yu Chuai ◽  
Zhen Zhang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Reduction Method ◽  
Random Noise ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Eemd Method

This paper presents a random noise reduction method based on ensemble empirical mode decomposition (EEMD) and wavelet threshold filtering. Firstly, we have conducted spectrum analysis and analyzed the frequency band range of effective signals and noise. Secondly, we make use of EEMD method on seismic signals to obtain intrinsic mode functions (IMFs) of each trace. Then, wavelet threshold noise reduction method is used on the high frequency IMFs of each trace to obtain new high frequency IMFs. Finally, reconstruct the desired signal by adding the new high frequency IMFs on the low frequency IMFs and the trend item together. When applying our method on synthetic seismic record and field data we can get good results.

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