Using neural network to interpret multiple alarms

Chan, Edward H. P.

doi:10.1109/67.53228

Cited by 26 publications

(4 citation statements)

References 1 publication

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“…Kirschen et al showed several examples that expert system can help control a power system during an emergency [7]. Additionally, Model-Based reasoning methods [8] and Neural Network approach [9] were applied in alarm processing to concentrate the alarm messages as well. Wei et al used two modules (i.e., the generation of candidate hypotheses and the truth evaluation for the hypotheses) in the developed approach, and these two modules are operating in parallel on online implementation for determining the fault/disturbance cause [10].…”

Section: Introductionmentioning

confidence: 99%

A time-driven and event-driven approach for substation feeder incident analysis

Chen

Chang

Lee

2016

International Journal of Electrical Power & Energy Systems

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Section: Introductionmentioning

confidence: 99%

A time-driven and event-driven approach for substation feeder incident analysis

Chen

Chang

Lee

2016

International Journal of Electrical Power & Energy Systems

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“…Para tentar solucionar esses problemas, vários trabalhos [1,2,3,4] vêm sendo desenvolvidos. Uma boa parte utiliza técnicas recentes como Redes Neurais e ainda, outra parte utiliza técnicas mais tradicionais, como sistemas baseados em Lógicas/Regras ou sistemas que utilizam duas ou mais destas Técnicas de Inteligência Artificial-Sistemas Híbridos -visando alcançar as vantagens de cada uma delas e reduzir suas desvantagens.…”

Section: Introductionunclassified

Sistemas Híbridos Aplicados à Filtragem de Sinais de Alarme de Proteção de uma Subestação Telecomandada

Lira¹,

Carvalho²

2016

Anais Do 4. Congresso Brasileiro De Redes Neurais

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In this work an Artificial Inteligent Hybrid System is applied to the filtering process of an alarm signal set, generated at a substation. This combined system consists in an Artificial Neural Network, using MLP backpropagation algorithm and a Fuzzy Logic Inference System of the Mamdani type. The results show that the Hybrid System is able to interpret correctly the alarm information, resulting in a better performance than the Neural System alone.

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“…The application of Expert systems, Fuzzy logic, wavelet, Neural networks (NNs) have successfully been applied for fault diagnosis in literature [6][7][8][9][10]. Recently, artificial neural network (ANN) approaches have been extensively applied to power system fault diagnosis [11][12][13][14]. Fundamental capabilities of ANN in on-line fault diagnosis have been verified, but certain practical problems remain unsolved, e.g., slow convergence in training, and manual determination of the network structure and parameters.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Approach for Fault Diagnosis in Power Transmission Systems Using Support Vector Machines

Ravikumar¹,

Thukaram²,

Khincha³

2007

International Journal of Emerging Electric Power Systems

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This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

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Using neural network to interpret multiple alarms

Cited by 26 publications

References 1 publication

A time-driven and event-driven approach for substation feeder incident analysis

A time-driven and event-driven approach for substation feeder incident analysis

Sistemas Híbridos Aplicados à Filtragem de Sinais de Alarme de Proteção de uma Subestação Telecomandada

Intelligent Approach for Fault Diagnosis in Power Transmission Systems Using Support Vector Machines

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