- Volume 19 Issue 3
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RC Circuit Parameter Estimation for DC Electric Traction Substation Using Linear Artificial Neural Network Scheme
선형인공신경망을 이용한 직류 전철변전소의 RC 회로정수 추정
- Bae, Chang Han (Railroad Type Approval Team, Korea Railroad Research Institute) ;
- Kim, Young Guk (Railroad Type Approval Team, Korea Railroad Research Institute) ;
- Park, Chan Kyoung (Railroad Type Approval Team, Korea Railroad Research Institute) ;
- Kim, Yong Ki (Transportation Environmental Research Team, Korea Railroad Research Institute) ;
- Han, Moon Seob (Wireless Power Transfer System Research Team, Korea Railroad Research Institute)
- Received : 2016.04.07
- Accepted : 2016.06.09
- Published : 2016.06.30
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.
Supported by : 한국철도기술연구원
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