Feature extraction and classification method for switchgear faults based on sample entropy and cloud model

Jindong, Cui; Zheng, Que; Xin, Yechun; Zhou, Chuan; Wang, Qianggang; Zhou, Niancheng

doi:10.1049/iet-gtd.2016.1459

Cited by 17 publications

(19 citation statements)

References 27 publications

(39 reference statements)

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“…3. In some conventional approaches, the manner for determining the pre-set threshold values is complicated values [3,5,8,11,12,[21][22][23]. 4.…”

Section: Introductionmentioning

confidence: 99%

Simple algorithm for fault detection, classification and direction discrimination in power systems based on digital current signals processing

Mahmoud

2022

The Journal of Engineering

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A power system suffers from unexpected various faults, which needs smart devices for accurate estimates of fault detection, classification and direction discrimination to provide inspection, maintenance, and repair of the actual faults. Signal processing‐based protection technique can be used to detect the disturbances of the measured signals. This paper presents a digital protection scheme, based on three indices derived from the three‐phase input and output current signals of the protected equipment, to detect current disturbances, classify faults, discriminate fault directions, select faulty phase(s), define current unbalances, and identify CT saturation extent. Moreover, it develops a new closed‐tripping characteristic with a quadrilateral form for differentiating the fault location (internal/external) with respect to the element protection zone. A power system simulation is carried out using ATP platform, and the technique performance is validated via MATLAB software. Extensive simulation case studies show that the proposed algorithm provides a fast relay response and high accuracy under various operating and fault conditions. It is able to perform successfully the function of fault diagnosis within one periodic cycle. Besides, it responds very well with regard to dependability, security, stability and sensitivity. In addition to that it has the ability to recognize the saturated and unsaturated secondary current signals, measured using the current transformers, in the cases of internal and external faults.

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“…3. In some conventional approaches, the manner for determining the pre-set threshold values is complicated values [3,5,8,11,12,[21][22][23]. 4.…”

Section: Introductionmentioning

confidence: 99%

Simple algorithm for fault detection, classification and direction discrimination in power systems based on digital current signals processing

Mahmoud

2022

The Journal of Engineering

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“…However, there is always a need to develop innovative and efficient methods of busbar protection. References [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] present some modern techniques, which utilize sophisticated algorithms, to provide fast busbar protection and reliable performance during CT saturation. Some busbar protections based on Travelling Wave (TW), which used the transient fault information to avoid CT saturation effects and improve the speed and sensitivity of the protective relay [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…The polarity comparison of superimposed current was utilized for BB fault detection. The article in [17] presented a classification method for switchgear faults based on sample entropy and cloud model. The technique of BB fault zone determination using relevance vector machine was used in paper [18].…”

Section: Introductionmentioning

confidence: 99%

Integrated busbar protection scheme utilizing a numerical technique based on coherence method

Mahmoud

2021

The Journal of Engineering

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A Busbar (BB) is one of the most important elements of a power system, which links a variety of elements such as generators, power transformers, transmission lines and loads. The BB fault current magnitude is very large, which has to be immediately cleared in order to minimize the failure in the power system, and to avoid the CT saturation effects. This paper presents a novel BB protection scheme based on closed‐tripping characteristics of coherence coefficients computed for BB current signals. Three‐phase current signals of each circuit connected to the protected busbar are measured for calculating nine cross‐coherence coefficients. These coefficients are compared with each other and with a pre‐determined threshold value to identify the fault location, classification and direction discrimination, as well as to differentiate the internal and external faults with CT saturation extent. The scheme is tested under different types of external and internal faults with variations of faulty phase(s), fault locations, fault resistances, fault inception angles, and power flow angles. ATP platform is used for power system simulation, and MATLAB software is applied to validate the proposed protection algorithm. The simulation results prove that the scheme is fast, where its operating time is within a sub‐cycle time range, and it has the ability to discriminate between internal and external faults with/without CT saturation condition. Moreover, the numerical technique is immune to CT saturation effect, and it is simple, effective, secure, reliable, stable and feasible.

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“…14 Entropy is a method to measure the complexity of time series and has been widely used in feature extraction and fault diagnosis. 15 In the literature, 16,17 multiscale sample entropy (MSE) is applied to the fault diagnosis of rolling bearings. Studies in the literature 18,19 have applied multi-scale permutation entropy (MPE) to feature extraction of electroencephalogram signals and system mutation detection and achieved good results.…”

Section: Introductionmentioning

confidence: 99%

Partial discharge feature extraction based on synchrosqueezed windowed Fourier transform and multi-scale dispersion entropy

Sun

Wang

2020

Measurement and Control

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The recognition of partial discharge mode is an important indicator of the insulation condition in transformers, based on which maintenance can be arranged. Discharge feature extraction is the key to recognize discharge mode. To solve the problem of poor stability and low recognition rate of partial discharge mode, this paper proposes a feature extraction method based on synchrosqueezed windowed Fourier transform and multi-scale dispersion entropy. First, the four partial discharge signals collected under laboratory conditions are decomposed by synchrosqueezed windowed Fourier transform, then a number of band-limited intrinsic mode type functions are obtained, and the original feature quantities of partial discharge signals are obtained by calculating the multi-scale dispersion entropies of each intrinsic mode type function. Based on that, original feature quantity is optimized by using the maximum relevance and minimum redundancy criteria. Finally, the classification is implemented by the support vector machine. Experimental results show that in the case of noise interference, the proposed synchrosqueezed windowed Fourier transform–multi-scale dispersion entropy method can still accurately describe the feature of different discharge signals and has a higher recognition rate than both the empirical mode decomposition–multi-scale dispersion entropy method and the direct multi-scale dispersion entropy method.

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Feature extraction and classification method for switchgear faults based on sample entropy and cloud model

Cited by 17 publications

References 27 publications

Simple algorithm for fault detection, classification and direction discrimination in power systems based on digital current signals processing

Simple algorithm for fault detection, classification and direction discrimination in power systems based on digital current signals processing

Integrated busbar protection scheme utilizing a numerical technique based on coherence method

Partial discharge feature extraction based on synchrosqueezed windowed Fourier transform and multi-scale dispersion entropy

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