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.
|Number of pages||9|
|Journal||Engineering Applications of Artificial Intelligence|
|Publication status||Published - 2008 Feb|
Bibliographical noteFunding 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