Quick detection of fault, classification and fault distance location are desirable features of any protective relaying scheme. Protection of double circuit transmission lines experiences problems due to zero sequence mutual coupling between faulted and healthy circuits. It is affected by network changes such as switching in/out of one of the parallel lines, thus causing under reach/over reach of conventional distance relaying. This book analyses two different approaches based on single and modular artificial neural networks for fault detection/classification and distance location. The proposed method uses the voltages and currents signals available at the local end of line. Effects of variations in power system parameters e.g. fault inception angle, CT saturation, source strengths, fault resistance, type, fault location including the effects of network changes: double circuit and single circuit operation have been investigated extensively on the performance of the neural network based protection scheme.The results of single and modular neural networks indicate that the modular approach detects/classify the faults correctly and estimates the fault distance with more accuracy.
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Quick detection of fault, classification and fault distance location are desirable features of any protective relaying scheme. Protection of double circuit transmission lines experiences problems due to zero sequence mutual coupling between faulted and healthy circuits. It is affected by network changes such as switching in/out of one of the parallel lines, thus causing under reach/over reach of conventional distance relaying. This book analyses two different approaches based on single and modular artificial neural networks for fault detection/classification and distance location. The proposed method uses the voltages and currents signals available at the local end of line. Effects of variations in power system parameters e.g. fault inception angle, CT saturation, source strengths, fault resistance, type, fault location including the effects of network changes: double circuit and single circuit operation have been investigated extensively on the performance of the neural network based protection scheme.The results of single and modular neural networks indicate that the modular approach detects/classify the faults correctly and estimates the fault distance with more accuracy.
Anamika Jain did her Ph. D. from CSVTU Bhilai,in the year 2010. She worked as Assistant Engineer with CSEB for 4.5 years. Since March 2009 she is serving as a faculty in the dept. of Electrical Engg. at NIT, Raipur, INDIA. Dr. Jain has more than 25 publications in various International Journals and Conferences. She is the member of IEEE & The IET.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Quick detection of fault, classification and fault distance location are desirable features of any protective relaying scheme. Protection of double circuit transmission lines experiences problems due to zero sequence mutual coupling between faulted and healthy circuits. It is affected by network changes such as switching in/out of one of the parallel lines, thus causing under reach/over reach of conventional distance relaying. This book analyses two different approaches based on single and modular artificial neural networks for fault detection/classification and distance location. The proposed method uses the voltages and currents signals available at the local end of line. Effects of variations in power system parameters e.g. fault inception angle, CT saturation, source strengths, fault resistance, type, fault location including the effects of network changes: double circuit and single circuit operation have been investigated extensively on the performance of the neural network based protection scheme.The results of single and modular neural networks indicate that the modular approach detects/classify the faults correctly and estimates the fault distance with more accuracy. 140 pp. Englisch. Nº de ref. del artículo: 9783846545638
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Librería: moluna, Greven, Alemania
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Jain AnamikaAnamika Jain did her Ph. D. from CSVTU Bhilai,in the year 2010. She worked as Assistant Engineer with CSEB for 4.5 years. Since March 2009 she is serving as a faculty in the dept. of Electrical Engg. at NIT, Raipur, INDI. Nº de ref. del artículo: 5498107
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Quick detection of fault, classification and fault distance location are desirable features of any protective relaying scheme. Protection of double circuit transmission lines experiences problems due to zero sequence mutual coupling between faulted and healthy circuits. It is affected by network changes such as switching in/out of one of the parallel lines, thus causing under reach/over reach of conventional distance relaying. This book analyses two different approaches based on single and modular artificial neural networks for fault detection/classification and distance location. The proposed method uses the voltages and currents signals available at the local end of line. Effects of variations in power system parameters e.g. fault inception angle, CT saturation, source strengths, fault resistance, type, fault location including the effects of network changes: double circuit and single circuit operation have been investigated extensively on the performance of the neural network based protection scheme.The results of single and modular neural networks indicate that the modular approach detects/classify the faults correctly and estimates the fault distance with more accuracy. Nº de ref. del artículo: 9783846545638
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Quick detection of fault, classification and fault distance location are desirable features of any protective relaying scheme. Protection of double circuit transmission lines experiences problems due to zero sequence mutual coupling between faulted and healthy circuits. It is affected by network changes such as switching in/out of one of the parallel lines, thus causing under reach/over reach of conventional distance relaying. This book analyses two different approaches based on single and modular artificial neural networks for fault detection/classification and distance location. The proposed method uses the voltages and currents signals available at the local end of line. Effects of variations in power system parameters e.g. fault inception angle, CT saturation, source strengths, fault resistance, type, fault location including the effects of network changes: double circuit and single circuit operation have been investigated extensively on the performance of the neural network based protection scheme.The results of single and modular neural networks indicate that the modular approach detects/classify the faults correctly and estimates the fault distance with more accuracy.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 140 pp. Englisch. Nº de ref. del artículo: 9783846545638
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Librería: preigu, Osnabrück, Alemania
Taschenbuch. Condición: Neu. Ann Based Fault Detection, Classification and Distance Location | Protection of double circuit transmission line | Anamika Jain | Taschenbuch | 140 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846545638 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Nº de ref. del artículo: 106724549
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Librería: Revaluation Books, Exeter, Reino Unido
Paperback. Condición: Brand New. 140 pages. 8.66x5.91x0.32 inches. In Stock. Nº de ref. del artículo: 3846545635
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Librería: Mispah books, Redhill, SURRE, Reino Unido
paperback. Condición: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book. Nº de ref. del artículo: ERICA82938465456356
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