Journal of the Korean Society for Railway (한국철도학회논문집)

The Korean Society for Railway (한국철도학회)
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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
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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.

직류 전철변전소의 가선전압은 전동차들의 회생제동 및 역행가속패턴에 따라 급격히 상승 또는 하강하는 특성을 갖는다. 가선전압 순시 변동폭을 최소로 유지함으로써, 전철변전소와 전동차들의 에너지 효율을 개선시키기 위한 다양한 연구들이 이루어지고 있다. 본 논문은 직류전철 변전소의 가선전압의 급격한 변동특성을 모델링하고 선형인공 신경망 알고리즘을 이용한 가선전압 회로모델의 파라메터 추정 방법을 제안하며, 최소자승법을 이용한 추정방법과의 비교를 통해 이 방법의 타당성을 입증한다. 가선전압 및 피더전류들의 누적 측정값을 사용하여 일괄처리 최소자승법으로 RC 병렬회로의 파라메터들을 추정한 결과를 제시하며, 실시간 가선전압 및 피더전류 측정값을 이용하여 오차역 전파방식으로 학습되는 선형인공신경망 기법 추정 결과를 분석한다.

Keywords

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