Identifying the Primary Fault Section After Contingencies in Bulk Power Systems

Cardoso, Ghendy; Rolim, J.G.; Zurn, H.H.

doi:10.1109/tpwrd.2008.916743

Cited by 63 publications

(26 citation statements)

References 7 publications

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“…This makes ANNs a promising and attractive tool for modern power system applications. ANNs have been proposed for detection of power system harmonics [2][3][4], fault section estimation [5][6], fault diagnostics of power plant [7] and in protection strategies [8][9]. Applications of ANNs in reactive power transfer allocation [10] and ATC estimation [11] have been reported.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Identification of Multimachine Power System

Tiwari¹,

Naresh²,

Jha³

et al. 2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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This paper demonstrates an effective application of artificial neural networks for online identification of a multimachine power system. The paper presents a recurrent neural network as the identifier of the benchmark two area, four machine system. This neural identifier is trained using the static Backpropagation algorithm. The trained neural identifier is then tested using datasets generated by simulating the system under consideration at different operating points and a different loading condition. The test results clearly establish a satisfactory performance of the trained neural identifier in identification of the power system considered.

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Section: Introductionmentioning

confidence: 99%

Neural Network Based Identification of Multimachine Power System

Tiwari¹,

Naresh²,

Jha³

et al. 2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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show abstract

“…Up to now, the most established approaches, which have been put into or of potential for practical applications, can be grouped into two categories: the expert system-based [1][2][3][4][5] or the analytic model-based methods [6][7][8][9][10][11][12][13]. In addition, Artificial Intelligent methods such as Petri Nets [14,15], Artificial Neural Network [16][17][18], rough set (RS) [19], and Bayesian networks [20], have also been proposed for diagnosis of fault sections.…”

Section: Introductionmentioning

confidence: 99%

A new analytic approach for power system fault diagnosis employing the temporal information of alarm messages

Guo

Wen

Ledwich

et al. 2012

International Journal of Electrical Power & Energy Systems

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“…Thus far, expert systems [3] , [4] and artificial intelligence [5] , [6] have been proposed to replace the operators task.…”

Section: Introductionmentioning

confidence: 99%

A decision tree-based method for power system fault diagnosis by synchronized Phasor Measurements

Dehkordi

Dobakhshari

Ranjbar

2012

2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)

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This paper introduces a novel approach for power system fault diagnosis based on synchronized phasor measure ments during the fault. The synchronized measurements are obtained in real time from Phasor Measurement Units (PMUs) and compared with offline thresholds determined by decision trees (DTs) to diagnose the fault. The DTs have already been trained offline using detailed power system analysis for different fault cases. While the traditional methods for fault diagnosis use the status of protective relays (PRs) and circuit breakers (CBs) to infer the fault section in the power system, the proposed method uses the available signals following the fault and thus can be trusted even in case of protection system failure. Therefore, fault diagnosis can be achieved successfully especially in blackout situ ations, where many CBs and and/or PRs malfunction. Simulation results on the IEEE US-bus test system show that the proposed method is highly accurate in wide area fault diagnosis in spite of incomplete system observability.

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Identifying the Primary Fault Section After Contingencies in Bulk Power Systems

Cited by 63 publications

References 7 publications

Neural Network Based Identification of Multimachine Power System

Neural Network Based Identification of Multimachine Power System

A new analytic approach for power system fault diagnosis employing the temporal information of alarm messages

A decision tree-based method for power system fault diagnosis by synchronized Phasor Measurements

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