Faulty Line Selection Method for Distribution Network Based on Variable Scale Bistable System

Wang, Xiaowei; Gao, Jie; Song, Guobing; Cheng, Qi; Wei, Xiangxiang; Wei, Yanfang

doi:10.1155/2016/7436841

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…Stochastic resonance has some advantages in detecting some useful signals polluted by the Gaussian noise. The theory of SR has been developed in a nonlinear bistable system, which is defined by the Langevin equation as

B = normald x ((), t) / normald t = - normald U ((), x) / normald x + s ((), t) + normalΓ ((), t),

where U ( x ) is the potential function and is equal to − ax 2 /2 + bx 4 /4, a and b of U ( x ) are the potential function parameters, x ( t ) is the output signal, s ( t ) is the input signal, Γ( t ) is the Gaussian noise whose intensity is D , and t is the time.…”

Section: B‐spline Bistable Denoising Methodsmentioning

confidence: 99%

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Faulty line detection method based on B-spline bistable denoising for distribution network

Gao

Cheng

Wang

et al. 2018

Int Trans Electr Energ Syst

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Summary Neutral‐point ineffectively grounded power system earthed through arc suppression coil is widely used in medium‐voltage distribution system; it is called small current‐to‐ground system (SCGS) in China. When single‐phase‐to‐earth fault occurs at SCGS, the fault current is weak and the noise impairs the feature of fault current, both of which make faulty line detection difficult. Hence, this paper proposes a B‐spline bistable denoising method (BSBD) to process the transient zero‐sequence current. This proposed method consists of 2 approaches: (1) using B‐spline fitting method to improve the deficiency of bistable system and (2) the genetic algorithm used to obtain the parameters of B‐spline bistable system. Then, a faulty line detection criterion (FLDC) based on BSBD and polarity parameter is presented. Electromagnetic transients program is used for testing the BSBD and FLDC. Extensive case studies prove the effectiveness of BSBD and FLDC.

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B = normald x ((), t) / normald t = - normald U ((), x) / normald x + s ((), t) + normalΓ ((), t),

Section: B‐spline Bistable Denoising Methodsmentioning

confidence: 99%

“…When s ( t ) is A sin(2π f 0 t ) and b is 1, the SNR of bistable system output is given by the adiabatic approximation

R_{out} = \sqrt{2} a^{2} A^{2} e^{- a^{2} / 4 D} / ((), 4 D^{2}),

where A is the amplitude of s ( t ).…”

Section: B‐spline Bistable Denoising Methodsmentioning

confidence: 99%

Faulty line detection method based on B-spline bistable denoising for distribution network

Gao

Cheng

Wang

et al. 2018

Int Trans Electr Energ Syst

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“…As can be seen in Figure 5c, the MCEEMD has no spurious components, and it removes the noise components. The correlation coefficient [1,16] can be used to analyze the degree of linear association between variables. The correlation coefficient is close to 1, indicating a higher correlation between the two variables.…”

Section: Analysis Of the Simulation Signal Using Emd Ceemd And Mceemdmentioning

confidence: 99%

“…It is necessary to remove faults quickly for safety in operation. There are many difficulties in fault line selection, such as weak fault current signal and complex actual operating environment [1][2][3]. Due to the compensation effect of the arc suppression coil, the fundamental component of the zero-sequence current cannot select the fault line.…”

Section: Introductionmentioning

confidence: 99%

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

Hou

Guo

2020

Symmetry

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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.

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“…Data show that 80% of the fault occurring in the un-effectively grounded system is single-phase grounding fault. When a single-phase grounding fault occurs, the fault current is pretty small, and the zero-sequence current is nonstationary and nonlinear [1,2]. If the faulty line remains unsolved, it could cause a serious threat to the insulation of the distribution network.…”

Section: Introductionmentioning

confidence: 99%

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

Hou

Guo

2020

Mathematical Problems in Engineering

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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.

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Faulty Line Selection Method for Distribution Network Based on Variable Scale Bistable System

Cited by 8 publications

References 12 publications

Faulty line detection method based on B-spline bistable denoising for distribution network

Faulty line detection method based on B-spline bistable denoising for distribution network

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

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

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