Generalized minimum variance neuro controller for power system stabilization

  • Authors:

  • Hee-Sang Ko;Kwang Y. Lee;Min-Jae Kang;Ho-Chan Kim

  • Affiliations:

  • Dept. of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada;Dept. of Electrical Engineering, The Pennsylvania State University, University Park, PA;Faculty of Electrical and Electronic Engineering, Cheju National University, Cheju, Korea;Faculty of Electrical and Electronic Engineering, Cheju National University, Cheju, Korea

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
  • Year:
  • 2006

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Abstract

This paper presents a power system stabilizer design that uses a generalized minimum variance-inverse dynamic neuro controller, which is the combination of the inverse dynamic neural model, the generalized minimum variance, and the neuro compensator. An inverse dynamic neural model represents the inverse dynamics of the system. The inverse dynamic neural model is trained to provide control input into the system, which makes the plant output reach the target value at the next sampling time. Once the inverse dynamic neural model is trained, it does not require retuning for cases with other types of disturbances. In this paper, a generalized minimum variance control scheme is adapted to prevent unstable system performance caused by non-minimum phase characteristics. In addition, a neural compensator is designed to compensate for modeling errors. The proposed control scheme is tested in a multimachine power system.