Power system stabilization using neural networks

Author

Wenxin Liu

Abstract

"This dissertation includes three papers on power system stabilization using neural network based controllers. Conventional power system stabilizers (CPSSs) are based on linearized models and their parameters are fine tuned to provide good performance around an operating point. At other operating points, the performance of the CPSS degrades. To overcome the drawbacks of CPSS, the first paper presents the design of a continual online trained indirect adaptive neural network (IANN) controller for a single machine infinite bus power system. The second paper presents the design of a nonlinear optimal neurocontroller using adaptive critic designs, combining the concepts of approximate dynamic programming and reinforcement learning, for power system stabilization...The third paper presents the design of a direct NN controller with stability analysis for a single machine power system"--Abstract, page iv.

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Electrical Engineering

Publisher

University of Missouri--Rolla

Publication Date

Summer 2005

Journal article titles appearing in thesis/dissertation

• Design of an adaptive neural network based power system stabilizer
• Heuristic dynamic programming based power system stabilizer for a turbogenerator in a single machine power system
• Neural network based stabilizing controller for single machine infinite bus power system with control limits

Pagination

xi, 83 pages

Note about bibliography

Includes bibliographical references.

Rights

© 2005 Wenxin Liu, All rights reserved.

Document Type

Dissertation - Citation

File Type

text

Language

English

Subject Headings

Electric power system stabilityAdaptive control systems -- DesignNeural networks (Computer science)Swarm intelligence

Thesis Number

T 8828

Print OCLC #

69659021

Recommended Citation

Liu, Wenxin, "Power system stabilization using neural networks" (2005). Doctoral Dissertations. 1614.
https://scholarsmine.mst.edu/doctoral_dissertations/1614