Power system stabilization using neural networks
"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.
Electrical and Computer Engineering
Ph. D. in Electrical Engineering
University of Missouri--Rolla
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
xi, 83 pages
© 2005 Wenxin Liu, All rights reserved.
Dissertation - Citation
Electric power system stability
Adaptive control systems -- Design
Neural networks (Computer science)
Print OCLC #
Link to Catalog Record
Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.http://merlin.lib.umsystem.edu/record=b5545873~S5
Liu, Wenxin, "Power system stabilization using neural networks" (2005). Doctoral Dissertations. 1614.
Share My Dissertation If you are the author of this work and would like to grant permission to make it openly accessible to all, please click the button above.