Design of artificial neural networks for distribution feeder loss analysis

  • Authors:

  • Tsung-En Lee;Chin-Ying Ho;Chia-Hung Lin;Meei-Song Kang

  • Affiliations:

  • Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan;Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan and Department of Electrical Engineering, Kao Yuan University, Lu Chu, Taiwan;Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan;Department of Electrical Engineering, Kao Yuan University, Lu Chu, Taiwan

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

To enhance the efficiency for power loss analysis of voluminous distribution feeders, ANN-based simplified power loss models with the Levenberg-Marquardt (LM) algorithm have been developed for overhead feeders and underground feeders, respectively. The three-phase load flow analysis is executed to obtain the sensitivity of feeder loss with variations in power loading, conductor length, and total capacity of distribution transformers. Through this, the data set for neural network training is prepared to derive the ANN-based simplified power loss models. The power loss of each distribution feeder can be easily derived from the key factors of hourly loading, feeder length, and transformer capacity. By integrating the power loss of all feeders, the power loss of the entire distribution system can thus be obtained to estimate the operation efficiency of the Taipower system.