Dual heuristic programming based nonlinear optimal control for a synchronous generator

Jung Wook Park, Ronald G. Harley, Ganesh K. Venayagamoorthy, Gilsoo Jang

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)97-105
Number of pages9
JournalEngineering Applications of Artificial Intelligence
Issue number1
Publication statusPublished - 2008 Feb

Bibliographical note

Funding Information:
This work was supported by the MOCIE through EIRC program with APSRC at Korea University, Seoul, South Korea.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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