The design of 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 PID 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 continually online train the neural networks, thus avoiding risks of instability.

Meeting Name

36th IAS Annual Meeting of the IEEE Industry Applications Conference, 2001


Electrical and Computer Engineering

Keywords and Phrases

Adaptive Critic Technique; Control Simulation; Control System Analysis; Control System Synthesis; Disturbances; Dynamic Performance Improvement; Exciters; Feedback; Feedback Variables; Generator Dual Heuristic Programming Excitation Neurocontrol; Heuristic Programming; Machine Control; Machine Theory; Multimachine Power System; Neurocontrollers; Optimal Control; Optimal Neurocontrol Design; Power System Control; Power System Stability; Robust Control; Stability Improvement; Turbogenerators; Voltage Control

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2001 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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