Power System Aggregate Load Area Dynamic Modeling by Learning Based on WAMS

  • Authors:

  • Huimin Yang;Jinyu Wen

  • Affiliations:

  • Power Security and High Efficiency Lab, Huazhong University of Science and Technology, Wuhan, China 430074;Power Security and High Efficiency Lab, Huazhong University of Science and Technology, Wuhan, China 430074

  • Venue:
  • ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
  • Year:
  • 2009

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Abstract

This paper is concerned with an investigation of a methodology using intelligent learning techniques based on WAMS to construct power system load area model. An aggregate load area dynamic model (ALADM) is proposed to represent large area loads of power system. A population diversity-based genetic algorithm (GA) combined with the recursive least squares (RLS) method is used to obtain the structure and parameters of the load model. Simulation results on EPRI 36-bus power system is given to show the potential of this new methodology of power system modeling.