An improved scheme for identifying fault zone in a series compensated transmission line using undecimated wavelet transform and Chebyshev Neural Network

Vyas, Bhargav; Das, Biswarup; Maheshwari, R. P.

doi:10.1016/j.ijepes.2014.06.030

Cited by 26 publications

(4 citation statements)

References 22 publications

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“…Chebyshev Neural Network (ChNN) is a unit-layer network that offers advantages in terms of design and learning complexities. Additionally, it outperforms other classifiers such as SVM and fuzzy logic based systems due to the absence of a performance controlling parameter [12]. Hence using the superiority of DWT -ChNN, a new differential protection algorithm for the classification of internal fault condition from inrush condition is presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

Application of Discrete Wavelet Transform and Chebyshev Neural Network for Differential Protection of Indirect Symmetrical Phase Shift Transformer

2024

IJFMR

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The Indirect Symmetrical Phase Shift Transformer (ISPST) stands out from a power transformer due to its combination of electrically connected and magnetically coupled circuits. Hence in this work, an intelligent differential protection algorithm, based on Discrete Wavelet Transform (DWT) and Chebyshev Neural Network (ChNN), is proposed as main classifier to discriminate internal fault and inrush. Half cycle thee phase post fault differential current is considered for the proposed algorithm. PSCAD/EMTDC software is utilized to simulate different operating conditions of ISPST, resulting in the simulation of a significant amount of internal faults and inrush cases. The algorithm under consideration has undergone extensive evaluation across numerous cases, resulting in an accuracy rate exceeding 99%. The results indicate that the classifier based on DWT - ChNN yields extremely promising results, even when dealing with a noisy signal, current transformer (CT) saturation, and varying ISPST ratings. Superiority of the proposed algorithm is also compared with Multilayer Perceptron (MLP), Radial Basis Function Neural Network (RBFNN) and Probabilistic Neural Network (PNN) based approaches under the same conditions and it is found that proposed classifier is the most efficient and rapid among all alternative classifiers for the differential protection scheme of an ISPST under the considered conditions.

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Section: Introductionmentioning

confidence: 99%

Application of Discrete Wavelet Transform and Chebyshev Neural Network for Differential Protection of Indirect Symmetrical Phase Shift Transformer

2024

IJFMR

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“…Chebyshev neural network (ChNN) is employed for carrying out the faults in a series-compensated power line in [13]. ChNN and an undecimated wavelet transform are used in [14] for identification of the fault's zone. In contrast, other schemes [13], [14] utilize specific datasets for training a decisionmaking algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…ChNN and an undecimated wavelet transform are used in [14] for identification of the fault's zone. In contrast, other schemes [13], [14] utilize specific datasets for training a decisionmaking algorithm. A pilot protection scheme is proposed in [15] to recognize fault conditions in a series-compensated line.…”

Section: Introductionmentioning

confidence: 99%

Faulty Phase Identification for Transmission Line with Metal Oxide Varistor-protected Series Compensator

Musa

et al. 2021

Journal of Modern Power Systems and Clean Energy

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The nonlinear operation of metal oxide varistors (MOV)-protected series compensator in transmission lines introduces complications into fault detection approaches. The accuracy of a conventional fault detection schemes is adversely affected by continuous change of the system impedance and load current at the point of a series compensation unit. Thus, this study suggests a method for detecting the faulted phase in MOV-protected series-compensated transmission lines. Primarily, the fault feature is identified using the covariance coefficients of the current samples during the fault period and the current samples during the pre-fault period. Furthermore, a convenience fault detection index is established by applying the cumulative sum technique. Extensive validation through different fault circumstances is accomplished, including different fault positions, resistances, and inception times. The experimental results show that the proposed method performs well with high resistance or impedance faults, faults in noisy conditions, and close-in and far-end faults. The proposed method is simple and efficient for faulty phase detection in MOV-protected series-compensated transmission lines.

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“…In addition, DSP and descrete wavelet transform (DWT) analysis of fault signals waveforms records at monitoring location of a multi-bus meshed network for useful feature information gathering adopted to detect and classify faults [10]. Protection fault analysis scheme on a series and shunt compensated TL on this network topology using half-cycle post-fault current signal for ANN training [11][12]. Other hybrid network topology protection scheme approach for fault identification, classification, and location in combined overhead TL and underground cables network topology using hybrid ANN-Fuzzy logic [13].…”

Section: Introductionmentioning

confidence: 99%

Faults Signature Extraction in Wind Farm Integrated Transmission Line Topology

Emmanuel¹,

Othman²,

Hizam³

et al. 2018

IJEECS

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<p>The integration of Renewable Green Energy Sources (RGES) like Wind Farm Generators (WFG), and Photo Voltaic (PV) systems into convention power system as a future solution to the increase in global energy demands, generation cost reduction, and limited climate impact. The innovation introduced protection compromise challenges in power system due to in-feeds fault current penetration from RGES on existing system, leading to an undesired trip of the healthy section of TL, equipment damages, and safety failure. A comparison study of extracted faults signature from two proposed Transmission Line (TL) network topologies with and without WFG integration, for onward fault identification, and classification model design. Descrete wavelet multiresolution Analysis (DWMRA) of extracted one-cycle fault signal signatures from 11 faults type’s scenarios in Matlab. Result demonstrated a unique fault signatures across all simulated faults scenarios harness for future work of an adaptive unit protection model for this new area of DG integration.</p>

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An improved scheme for identifying fault zone in a series compensated transmission line using undecimated wavelet transform and Chebyshev Neural Network

Cited by 26 publications

References 22 publications

Application of Discrete Wavelet Transform and Chebyshev Neural Network for Differential Protection of Indirect Symmetrical Phase Shift Transformer

Application of Discrete Wavelet Transform and Chebyshev Neural Network for Differential Protection of Indirect Symmetrical Phase Shift Transformer

Faulty Phase Identification for Transmission Line with Metal Oxide Varistor-protected Series Compensator

Faults Signature Extraction in Wind Farm Integrated Transmission Line Topology

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