Dual heuristic programming based nonlinear optimal control for a synchronous generator

Authors:
Jung-Wook Park;Ronald G. Harley;Ganesh K. Venayagamoorthy;Gilsoo Jang
Affiliations:
School of Electrical and Electronic Engineering, C735, Yonsei University, 134 Sinchon-dong, Seodaemun-gu, Seoul 120-749, South Korea;School of Electrical and Computer Engineering, Georgia Institute of Technology, 176 Van Leer Building, GA 30332-0250, USA;Department of Electrical and Computer Engineering, University of Missouri-Rolla, 132 Emerson Electric Co. Hall, 1870 Miner Circle, MO 65409-0249, USA;School of Electrical Engineering, Korea University, Anam-dong Seongbuk-Gu, Seoul 136-701, South Korea
Venue:
Engineering Applications of Artificial Intelligence
Year:
2008

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Abstract

This paper presents the design of an infinite horizon nonlinear optimal neurocontroller that replaces the conventional automatic voltage regulator and the turbine governor (CONVC) for the control of a synchronous generator connected to an electric power grid. The neurocontroller design uses the novel optimization neuro-dynamic programming algorithm based on dual heuristic programming (DHP), which has the most robust control capability among the adaptive critic designs family. The radial basis function neural network (RBFNN) is used as the function approximator to implement the DHP technique. The DHP based optimal neurocontroller (DHPNC) using the RBFNN shows improved dynamic damping compared to the CONVC even when a power system stabilizer is added. Also, the DHPNC provides a robust feedback loop in real-time operation without the need for continual on-line training, thus reducing any risk of possible instability associated with the neural network based controllers.