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Partial discharge (PD) seriously affects the reliability of the distribution system due to electrical stress and the duration of the installation. Recent technology advance brings the analysis of the PD act as the guideline and maintenance strategy can be carried out when a parameter exceeding the predefined level. This book presents an artificial neural network (ANN) modelling in detecting the PD signal. PD signals are generated from experimental laboratory and simulation by using electromagnetic transient program-alternative transient program (EMTP-ATP). There are two analyses are carried out; classification and de-noising of PD signal. The first analysis used the straight forward procedure in PD signal classification. Second analysis presents the de-noising of PD signal using three different techniques; ANN, fast Fourier transforms (FFT) and discrete wavelet transform (DWT). The de-noising algorithm is implemented to discover a clean PD signal from disrupted signal. The performance of the de-nosing techniques was evaluated by comparing the signal to noise ratio (SNR). The result of this analysis shows ANN is the best de-noising technique compare to others.
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Partial Discharge Recognition Using Artificial Neural Network
Editore:
LAP LAMBERT Academic Publishing Jul 2020, 2020
ISBN 10: 6202678747
ISBN 13: 9786202678742
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Partial discharge (PD) seriously affects the reliability of the distribution system due to electrical stress and the duration of the installation. Recent technology advance brings the analysis of the PD act as the guideline and maintenance strategy can be carried out when a parameter exceeding the predefined level. This book presents an artificial neural network (ANN) modelling in detecting the PD signal. PD signals are generated from experimental laboratory and simulation by using electromagnetic transient program-alternative transient program (EMTP-ATP). There are two analyses are carried out; classification and de-noising of PD signal. The first analysis used the straight forward procedure in PD signal classification. Second analysis presents the de-noising of PD signal using three different techniques; ANN, fast Fourier transforms (FFT) and discrete wavelet transform (DWT). The de-noising algorithm is implemented to discover a clean PD signal from disrupted signal. The performance of the de-nosing techniques was evaluated by comparing the signal to noise ratio (SNR). The result of this analysis shows ANN is the best de-noising technique compare to others. 80 pp. Englisch. Codice articolo 9786202678742
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Partial Discharge Recognition Using Artificial Neural Network
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LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202678747
ISBN 13: 9786202678742
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Partial Discharge Recognition Using Artificial Neural Network
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LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202678747
ISBN 13: 9786202678742
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Partial Discharge Recognition Using Artificial Neural Network
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LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202678747
ISBN 13: 9786202678742
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Partial Discharge Recognition Using Artificial Neural Network
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LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202678747
ISBN 13: 9786202678742
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Isa MuzamirMuzamir Isa was born in Malaysia in 1979. He received Doctoral (Ph.D) degree from Aalto University, Helsinki, Finland. His research interests are partial discharge measurement, detection and location technique, and power s. Codice articolo 494631176
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Partial Discharge Recognition Using Artificial Neural Network
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LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202678747
ISBN 13: 9786202678742
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Partial discharge (PD) seriously affects the reliability of the distribution system due to electrical stress and the duration of the installation. Recent technology advance brings the analysis of the PD act as the guideline and maintenance strategy can be carried out when a parameter exceeding the predefined level. This book presents an artificial neural network (ANN) modelling in detecting the PD signal. PD signals are generated from experimental laboratory and simulation by using electromagnetic transient program-alternative transient program (EMTP-ATP). There are two analyses are carried out; classification and de-noising of PD signal. The first analysis used the straight forward procedure in PD signal classification. Second analysis presents the de-noising of PD signal using three different techniques; ANN, fast Fourier transforms (FFT) and discrete wavelet transform (DWT). The de-noising algorithm is implemented to discover a clean PD signal from disrupted signal. The performance of the de-nosing techniques was evaluated by comparing the signal to noise ratio (SNR). The result of this analysis shows ANN is the best de-noising technique compare to others. Codice articolo 9786202678742
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Partial Discharge Recognition Using Artificial Neural Network
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LAP LAMBERT Academic Publishing Jul 2020, 2020
ISBN 10: 6202678747
ISBN 13: 9786202678742
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Partial discharge (PD) seriously affects the reliability of the distribution system due to electrical stress and the duration of the installation. Recent technology advance brings the analysis of the PD act as the guideline and maintenance strategy can be carried out when a parameter exceeding the predefined level. This book presents an artificial neural network (ANN) modelling in detecting the PD signal. PD signals are generated from experimental laboratory and simulation by using electromagnetic transient program-alternative transient program (EMTP-ATP). There are two analyses are carried out; classification and de-noising of PD signal. The first analysis used the straight forward procedure in PD signal classification. Second analysis presents the de-noising of PD signal using three different techniques; ANN, fast Fourier transforms (FFT) and discrete wavelet transform (DWT). The de-noising algorithm is implemented to discover a clean PD signal from disrupted signal. The performance of the de-nosing techniques was evaluated by comparing the signal to noise ratio (SNR). The result of this analysis shows ANN is the best de-noising technique compare to others.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch. Codice articolo 9786202678742
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Partial Discharge Recognition Using Artificial Neural Network
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LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202678747
ISBN 13: 9786202678742
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Da: preigu, Osnabrück, Germania
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Taschenbuch. Condizione: Neu. Partial Discharge Recognition Using Artificial Neural Network | Muzamir Isa (u. a.) | Taschenbuch | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202678742 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Codice articolo 120447729
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