The design of nonlinear optimal neurocontrollers that replace the conventional automatic voltage regulators for excitation control of turbogenerators in a multimachine power system is presented in this paper. The neurocontroller design is based on dual heuristic programming (DHP), a powerful adaptive critic technique. The feedback variables are completely based on local measurements from the generators. Simulations on a three-machine power system demonstrate that DHP-based neurocontrol is much more effective than the conventional proportional-integral-derivative control for improving dynamic performance and stability of the power grid under small and large disturbances. This paper also shows how to design optimal multiple neurocontrollers for nonlinear systems, such as power systems, without having to do continually online training of the neural networks, thus avoiding risks of neural network instability.


Electrical and Computer Engineering

Second Department

Computer Science

Keywords and Phrases

Adaptive Critic Technique; Control Design; Control Simulation; Control System Analysis; Control System Synthesis; Dual Heuristic Programming Excitation Neurocontrol; Dynamic Performance; Feedback Variables; Heuristic Programming; Machine Control; Machine Theory; Multimachine Power System Generators; Neurocontrollers; Nonlinear Control Systems; Nonlinear Optimal Neurocontrollers; Optimal Control; Power System Control; Power System Stability; Stability Improvement; Turbogenerators; Voltage Regulators

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version

Final Version

File Type





© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jan 2003