Design and implementation of a wavelet analysis‐based shunt fault detection and identification module for transmission lines application

Usama, Y.; Lu, Xiaomin; Imam, Hasib; Sen, Chitradeep; Kar, Narayan C.

doi:10.1049/iet-gtd.2013.0200

Cited by 26 publications

(19 citation statements)

References 17 publications

(20 reference statements)

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“…The power cable fault state (PCFS) can be denoted by localized characteristics in the time-frequency domain. It can highlight mutative components of the processed signals by flexibly changing the window of the time-frequency domain, and can then extract the power cable fault information effectively [18][19][20][21]. After comparing the wavelet modulus difference of the target traveling wave from the two ends, the fault type can be recognized [22].…”

Section: Description Of Short-circuit Fault Components In Online Powementioning

confidence: 99%

Fault Sensing Using Fractal Dimension and Wavelet

2016

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Abstract:A new fusion sensing (FS) method was proposed by using the improved fractal box dimension (IFBD) and a developed maximum wavelet coefficient (DMWC) for fault sensing of an online power cable. There are four strategies that were used. Firstly, the traditional fractal box dimension was improved to enlarge the feature distances between the different fault classes. Secondly, the IFBD recognition algorithm was proposed by using the improved fractal dimension feature extracted from the three-phase currents for the first stage of fault recognition. Thirdly, the DMWC recognition algorithm was developed based on the K-transform and wavelet analysis to establish the relationship between the maximum wavelet coefficient and the fault class. Fourthly, the FS method was formed by combining the IFBD algorithm and the DMWC algorithm in order to recognize the 10 types of short circuit faults of online power. The designed test system proved that the FS method increased the fault recognition accuracy obviously. In addition, the parameters of the initial angle, transient resistance, and fault distance had no influence on the FS method.

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Section: Description Of Short-circuit Fault Components In Online Powementioning

confidence: 99%

Fault Sensing Using Fractal Dimension and Wavelet

2016

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“…Hence, the conventional one-terminal and two-terminal fault detection methods are not useful anymore. In recent years, many fault detection and classification methods are presented [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Most of these methods use the voltage and current phasors at terminals, calculated using the sampled data over one power cycle.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection for Multi-terminal Transmission Line with Nuclear Power Plant Based on Wavelet Transform Ahmed R. Adly(1),*, Alaa M. Abdel-hamed (2), Said A. Kotb (1), Magdy M. Zak (1) 1) ETRR-2, Nuclear Research Center, Atomic Energy Authority, Egypt 2) High Institute of Engineering, EL Shorouk Academy, Egypt

Zaky

Kotb²,

Adly³

2019

Arab Journal of Nuclear Sciences and Applications

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This paper proposes an improved scheme of fault detection and classification for multi -terminal transmission lines using discrete wavelet transform (DWT). The proposed scheme can correctly detect and classify the faults. The proposed scheme is dependent on line current data only. This scheme is derived in the spectral domain and is based on the application of the DWT. The scheme uses an adaptive threshold level to detect and classify the faults. The ATP/EMTP program is used to evaluate the presented approach. Additionally, the nuclear power plants are planned to be integrated with the Egypt electric network in 2026, hence, the presented approach takes into consideration the installation of El Dabaa power station. The presented approach achieves accurate results under numerous and enhanced the fault detection and classification methodologies.

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“…Additionally, it requires an appropriate decomposition levels before creating the feature [21]. AI techniques require an appropriate training process, and these may increase the challenges practically especially for the huge systems with wide variation in faults [14,[22][23][24][25][26]. Conventional distance protections are facing some challenges to deal with far-end high resistance faults.…”

Section: Introductionmentioning

confidence: 99%

A cumulative standard deviation sum based method for high resistance fault identification and classification in power transmission lines

Musa

et al. 2018

Prot Control Mod Power Syst

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High resistance fault poses an enormous challenge to the existing algorithms of fault detection and fault classification. In this paper, the standard deviation and accumulation method are employed to perform the fault detection and classification. It is primarily built in two stages. Firstly, the standard deviations for the measured current's signals of the local and remote terminals is computed to extract the fault feature. Secondly, the cumulative approach is used to enlarge the fault feature to perform the high resistance fault. The proposed scheme is known as Standard Deviation Index (SDI), and it is obtained for the three phases and zero sequence. The proposed algorithm has been tested through different fault circumstances such as multiple faults locations, fault resistances, and fault inception time. Moreover, far-end faults with high-resistance, faults happened nearby the terminal, faults considering variable loading angle, sudden load change, different sampling frequency, bad signaling and a fault occurred in the presence of series compensation are also discussed. The results show that the proposed scheme performed remarkably well regarding the fault with resistance up to 1.5kΩ and can be detected within a millisecond after the fault inception. Additionally, the computational simplicity that characterizes the processes makes it more efficient and suitable for domain applications.

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Design and implementation of a wavelet analysis‐based shunt fault detection and identification module for transmission lines application

Cited by 26 publications

References 17 publications

Fault Sensing Using Fractal Dimension and Wavelet

Fault Sensing Using Fractal Dimension and Wavelet

A cumulative standard deviation sum based method for high resistance fault identification and classification in power transmission lines

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