A novel fuzzy-logic-based phase selection technique for power system relaying

Youssef, O.A.S.

doi:10.1016/j.epsr.2003.06.006

Cited by 14 publications

(3 citation statements)

References 21 publications

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“…This method does not identify the faulted phase. Contrary to this reference, [12] proposes a WT and FLS based phase selection algorithm using only line current signals, on the other hand reference [7] proposed an approach for selection of faulted phase based on superimposed or incremental phase voltages and currents initiated by faults, whereas [13] has proposes using superimposed negative-and positive-sequence currents to identify the faulted phase. This technique is extremely susceptible to fault resistance.…”

Section: Overview Of Faulted Phase Detection Methodsmentioning

confidence: 99%

Identifying line-to-ground faulted phase in low and medium voltage AC microgrid using principal component analysis and supervised machine-learning

Uzair

Zhu

2018

2018 Australasian Universities Power Engineering Conference (AUPEC)

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A supervised machine-learning based approach for faulted phase identification in bolted, low-and high-impedance line-to-ground faults using principal component analysis for feature extraction from multiple input signals is presented in this paper. DIgSILENT PowerFactory is used for simulating the underlying microgrid to obtain fault related data, while MATLAB is used for machine learning application. A 15-fold cross validation is applied to the training dataset for evaluation of different machine learning models and the results show supreme performance compared to previous methods.

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Section: Overview Of Faulted Phase Detection Methodsmentioning

confidence: 99%

Identifying line-to-ground faulted phase in low and medium voltage AC microgrid using principal component analysis and supervised machine-learning

Uzair

Zhu

2018

2018 Australasian Universities Power Engineering Conference (AUPEC)

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“…The algorithm has complex computations because of the use of repeated differences and second level wavelet decomposition to extract information from the signal. They also discussed a wavelet‐based fault classification technique using fuzzy logic for classification [16]. Most of the mentioned techniques require very high sampling rate, which is still not widely used in the commercially available devices.…”

Section: Introductionmentioning

confidence: 99%

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

Usama

Imam

et al. 2014

IET Generation, Transmission & Distribution

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“…Recently, modern technologies of the protection relays are 2 Mathematical Problems in Engineering based on artificial intelligence tools such as fuzzy logic (FL), artificial neural networks (ANNs), and the adaptive networkbased fuzzy inference system (ANFIS). Fuzzy logic-based transmission line relaying techniques for fault classification and fault location are proposed by many researchers [9][10][11]. However, these techniques cannot in any way guarantee precision results: for wide variation of faultconditions (high fault resistance, high fault inception angle, and far distance location from relay site).…”

Section: Introductionmentioning

confidence: 99%

Accurate Fault Classifier and Locator for EHV Transmission Lines Based on Artificial Neural Networks

Hessine

Saber

2014

Mathematical Problems in Engineering

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The ability to identify the fault type and to locate the fault in extra high voltage transmission lines is very important for the economic operation of modern power systems. Accurate algorithms for fault classification and location based on artificial neural network are suggested in this paper. Two fault classification algorithms are presented; the first one uses the single ANN approach and the second one uses the modular ANN approach. A comparative study of two classifiers is done in order to choose which ANN fault classifier structure leads to the best performance. Design and implementation of modular ANN-based fault locator are presented. Three fault locators are proposed and a comparative study of the three fault locators is carried out in order to determine which fault locator architecture leads to the accurate fault location. Instantaneous current and/or voltage samples were used as inputs to ANNs. For fault classification, only the pre-fault and post-fault samples of three-phase currents were used. For fault location, pre-fault and post-fault samples of three-phase currents and/or voltages were used. The proposed algorithms were evaluated under different fault scenarios. Studied simulation results which are presented confirm the effectiveness of the proposed algorithms.

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A novel fuzzy-logic-based phase selection technique for power system relaying

Cited by 14 publications

References 21 publications

Identifying line-to-ground faulted phase in low and medium voltage AC microgrid using principal component analysis and supervised machine-learning

Identifying line-to-ground faulted phase in low and medium voltage AC microgrid using principal component analysis and supervised machine-learning

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

Accurate Fault Classifier and Locator for EHV Transmission Lines Based on Artificial Neural Networks

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