A New Intelligent Autoreclosing Scheme Using Artificial Neural Network and Taguchi's Methodology

Zahlay, F. Desta; Rao, K. Srinivasa; Ibrahim, Taib

doi:10.1109/tia.2010.2090936

Cited by 41 publications

(16 citation statements)

References 21 publications

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“…Other architectures were tested empirically, and it was observed that some of them showed better results for faults in the first tower, while other architectures proved better for locating faults in the last towers. In all cases, the resilient backpropagation training was used [16,18,[21][22][23][24][25].…”

Section: The Neural Network Developedmentioning

confidence: 99%

A new methodology of fault location for predictive maintenance of transmission lines

Silva

Negrão

Vieira

et al. 2012

International Journal of Electrical Power & Energy Systems

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Section: The Neural Network Developedmentioning

confidence: 99%

A new methodology of fault location for predictive maintenance of transmission lines

Silva

Negrão

Vieira

et al. 2012

International Journal of Electrical Power & Energy Systems

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“…The development of computer technology has allowed the use of intelligent algorithms to distinguish between permanent and transient faults and determine the arc extinction time . These intelligent algorithms require collection of large amount of fault data.…”

Section: Introductionmentioning

confidence: 99%

“…TFI methods based on intelligent algorithms. 27,28 These methods take full advantage of intelligent algorithms to distinguish transient faults from permanent ones.…”

mentioning

confidence: 99%

Faulty phase active power characteristics‐based adaptive single‐phase reclosing schemes for shunt reactors‐compensated wind power outgoing line

Chao

Fan³

et al. 2019

Wind Energy

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This paper proposes two novel adaptive single‐phase auto‐reclosing (ASPAR) schemes for shunt reactors‐compensated wind power outgoing (WPO) line. After extinction of a fault on a single phase‐tripped WPO line, due to the existence of shunt reactors and distributed capacitors, a free frequency component will emerge in the tripped faulty phase, which will make faulty phase active power calculated with instantaneous power algorithm have a bigger magnitude and more frequency components. According to these characteristics, transient fault identification (TFI) methods based on active power root mean square (RMS) value and twisty beat frequency were put forward. Subsequently, in view of abovementioned TFI methods, ASPAR schemes for a shunt reactors‐compensated WPO line based on faulty phase active power characteristics were established. Simulation experiments, which were conducted using the software PSCAD/EMTDC, verified that the proposed ASPAR schemes can distinguish transient faults from permanent ones rapidly and reliably. The experiments also proved that the proposed adaptive single‐phase reclosing schemes can minimize the influence of changes in fault location and transition resistance and, thus, are suitable for the inconsistent output power of a wind farm and the complex working condition of an outgoing line.

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“…Researches on Taguchi method have been conducted in various applications. These researches can be used as the guidelines to optimize ANN [10][11][12][13][14][15]. Comparative studies between heuristic method network and optimized network using Taguchi were conducted to evidently show that Taguchi method works in optimizing the ANN.…”

Section: Introductionmentioning

confidence: 99%

The Taguchi- Artificial Neural Network approach for the detection of incipient faults in oil-filled power transformer

Zakaria

Johari

Musirin

2013

2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO)

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This paper presents hybrid Taguchi-ArtificialNeural Network to detect incipient faults in oil-immersed power transformer. It involved the development of Artificial Neural Network (ANN) designs and embedding Taguchi methodology to fine tune the parameters of a backpropagation feed-forward ANN. Detection of incipient faults in power transformer is essential because it is one of the fundamental equipments in the power system. Dissolved gas analysis technique was used as it has been found as a reliable technique to detect incipient faults as it provides wealth of information in analyzing transformer condition. This study is based on IEC 60599 (2007) standard and historical data were used in the training and testing processes. Comparative studies were conducted between heuristic ANN design and optimized hybrid Taguchi-Neural Network. The results show the effectiveness of the optimized neural network using Taguchi methodology.

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A New Intelligent Autoreclosing Scheme Using Artificial Neural Network and Taguchi's Methodology

Cited by 41 publications

References 21 publications

A new methodology of fault location for predictive maintenance of transmission lines

A new methodology of fault location for predictive maintenance of transmission lines

Faulty phase active power characteristics‐based adaptive single‐phase reclosing schemes for shunt reactors‐compensated wind power outgoing line

The Taguchi- Artificial Neural Network approach for the detection of incipient faults in oil-filled power transformer

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