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RC Circuit Parameter Estimation for DC Electric Traction Substation Using Linear Artificial Neural Network Scheme
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 Title & Authors
RC Circuit Parameter Estimation for DC Electric Traction Substation Using Linear Artificial Neural Network Scheme
Bae, Chang Han; Kim, Young Guk; Park, Chan Kyoung; Kim, Yong Ki; Han, Moon Seob;
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 Abstract
Overhead line voltage of DC railway traction substations has rising or falling characteristics depending on the acceleration and regenerative braking of the subway train loads. The suppression of this irregular fluctuation of the line voltage gives rise to improved energy efficiency of both the railway substation and the trains. This paper presents parameter estimation schemes using the RC circuit model for an overhead line voltage at a 1500V DC electric railway traction substation. A linear artificial neural network with a back-propagation learning algorithm was trained using the measurement data for an overhead line voltage and four feeder currents. The least square estimation method was configured to implement batch processing of these measurement data. These estimation results have been presented and performance analysis has been achieved through raw data simulation.
 Keywords
Linear artificial neural network;Least square estimation;DC electric traction substation;Overhead line voltage;Regenerative energy;
 Language
Korean
 Cited by
 References
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