Abstract

This paper presents the design of an optimal neurocontroller that replaces the conventional automatic voltage regulator (AVR) and the turbine governor for a turbogenerator connected to the power grid. The neurocontroller design uses a novel technique based on the adaptive critic designs (ACDs), specifically on heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Results show that both neurocontrollers are robust, but that DHP outperforms HDP or conventional controllers, especially when the system conditions and configuration change. This paper also shows how to design optimal neurocontrollers for nonlinear systems, such as turbogenerators, without having to do continually online training of the neural networks, thus avoiding risks of instability.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Keywords and Phrases

Adaptive Critic Designs; Backpropagation; Dual Heuristic Programming Adaptive Critics; Dynamic Programming; Heuristic Dynamic Programming; Heuristic Programming; Instability; Neural Nets; Neurocontrol; Neurocontrollers; Nonlinear Systems; Optimal Control; Optimal Neurocontroller; Stability; Turbine Governor; Turbogenerator; Turbogenerators

International Standard Serial Number (ISSN)

1045-9227

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2002 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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