NDLI: Power system stabilization using a free model based inverse dynamic linear controller

Content Provider	IEEE Xplore Digital Library
Author	Lee, K.Y. Hee-Sang Ko
Copyright Year	2001
Description	Author affiliation: Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA (Lee, K.Y.)
Abstract	This paper presents an implementation of power system stabilizer using inverse dynamic linear controller. Traditionally, multilayer neural network is used for a universal approximator and applied to a system as a neurocontroller. In this case, at least two neural networks are required and continuous tuning of the neurocontroller is required. Moreover, training of the neural network is required, considering all possible disturbances, which is impractical in real situation. In this paper, an inverse dynamic linear model (IDLM) is introduced to avoid this problem. The inverse dynamic linear controller consists of an IDLM and an error reduction linear model (ERLM). It does not require much time to train the IDLM. Once the IDLM is trained, it does not require retuning for cases with other types of disturbances. The controller is tested for a one machine and infinite-bus power system for various operating conditions.
Sponsorship	Power Eng. Soc.
Starting Page	983
Ending Page	988
File Size	588126
Page Count	6
File Format	PDF
ISBN	0780371739
DOI	10.1109/PESS.2001.970190
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-15
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power system modeling Power system dynamics Inverse problems Control systems Artificial neural networks Power system control Power systems Power system reliability Neural networks Nonlinear control systems
Content Type	Text
Resource Type	Article

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