Faults classification in series compensated lines based on wavelet entropy and neural network

Singh, Sunil K.; Vishwakarma, D. N.

doi:10.1109/microcom.2016.7522549

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Another ANN-based methodology on fault detection algorithms was tested on an ultrafast transmission line [33]. The fault classification in series-compensated transmission lines using norm entropy values, that have advantages in terms of reducing training time in a neural network, was presented [34]. The wavelet transforms and MRA have been used in an extra-high voltage line for real-time fault analysis, as the performance of an algorithm in this system is independent of fault impedance and fault angle [35].…”

Section: Literature Reviewmentioning

confidence: 99%

Fault Classifications in Distribution Systems Consisting of Wind Power as Distributed Generation Using Discrete Wavelet Transforms

Patcharoen

Ngaopitakkul

2019

Sustainability

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This paper proposed a fault type classification algorithm in a distribution system consisting of multiple distributed generations (DGs). The study also discussed the changing of signal characteristics in the distribution system with DGs during the occurrence of different fault types. Discrete Wavelet Transform (DWT)-based signal processing has been used to construct a classification algorithm and a decision tree to classify fault types. The input data for the algorithm is extracted from the three-phase current signal under normal conditions and during fault occurrence. These signals are recorded from the substation, load, and DG bus. The performance of the proposed classifying algorithm has been tested on a simulation system that was modeled after part of Thailand’s 22 kV distribution system, with a 2-MW wind power generation as the DG, connected to the distribution line by PSCAD software. The parameters that were taken into consideration consisted of the fault type, location of the fault, location of DG(s), and the number of DGs, to evaluate the performance of the proposed algorithm under various conditions. The result of the simulation indicated significant changes in current signal characteristics when installing DGs. In addition, the proposed algorithm has achieved a satisfactory accuracy in terms of identifying and classifying fault types when applied to a distribution system with multiple DGs.

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Section: Literature Reviewmentioning

confidence: 99%

Fault Classifications in Distribution Systems Consisting of Wind Power as Distributed Generation Using Discrete Wavelet Transforms

Patcharoen

Ngaopitakkul

2019

Sustainability

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“…AI has been combined with wavelets to improve the accuracy of fault classification algorithms in electrical systems [7][8][9][10][11][12]. AI enhances accuracy and reduces the time to classify faults.…”

Section: Introductionmentioning

confidence: 99%

“…AI enhances accuracy and reduces the time to classify faults. Wavelets with neural networks (NNs) have been used to detect and identify fault types in transmission line systems [7][8][9][10][11]. The algorithm makes use of wavelet transform-based approximate coefficients of three-phase voltage and current signals obtained over a quarter cycle to detect and classify faults.…”

Section: Introductionmentioning

confidence: 99%

“…However, many studies only focus on transmission lines or transformers, and few have combined the two components when analyzing systems. Most of the references use wavelets to detect and identify fault types in electrical systems, but they do not analyze the mother wavelet [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]20,21]. Additionally, there are research articles that compare mother wavelets [18,19,22,23].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Various Mother Wavelets for Fault Classification in Electrical Systems

et al. 2020

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This paper presents a comparative study on mother wavelets using a fault type classification algorithm in a power system. The study aims to evaluate the performance of the protection algorithm by implementing different mother wavelets for signal analysis and determines a suitable mother wavelet for power system protection applications. The factors that influence the fault signal, such as the fault location, fault type, and inception angle, have been considered during testing. The algorithm operates by applying the discrete wavelet transform (DWT) to the three-phase current and zero-sequence signal obtained from the experimental setup. The DWT extracts high-frequency components from the signals during both the normal and fault states. The coefficients at scales 1–3 have been decomposed using different mother wavelets, such as Daubechies (db), symlets (sym), biorthogonal (bior), and Coiflets (coif). The results reveal different coefficient values for the different mother wavelets even though the behaviors are similar. The coefficient for any mother wavelet has the same behavior but does not have the same value. Therefore, this finding has shown that the mother wavelet has a significant impact on the accuracy of the fault classification algorithm.

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Wavelet operated single index based fault detection scheme for transmission line protection with swarm intelligent support

2019

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Faults classification in series compensated lines based on wavelet entropy and neural network

Cited by 5 publications

References 17 publications

Fault Classifications in Distribution Systems Consisting of Wind Power as Distributed Generation Using Discrete Wavelet Transforms

Fault Classifications in Distribution Systems Consisting of Wind Power as Distributed Generation Using Discrete Wavelet Transforms

Comparison of Various Mother Wavelets for Fault Classification in Electrical Systems

Wavelet operated single index based fault detection scheme for transmission line protection with swarm intelligent support

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