Dual heuristic programming based nonlinear optimal control for a synchronous generator

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

doi:10.1016/j.engappai.2007.03.001

Dual heuristic programming based nonlinear optimal control for a synchronous generator

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

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

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.

Original language	English
Pages (from-to)	97-105
Number of pages	9
Journal	Engineering Applications of Artificial Intelligence
Volume	21
Issue number	1
DOIs	https://doi.org/10.1016/j.engappai.2007.03.001
Publication status	Published - 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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10.1016/j.engappai.2007.03.001

Cite this

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title = "Dual heuristic programming based nonlinear optimal control for a synchronous generator",

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.",

author = "Park, {Jung Wook} and Harley, {Ronald G.} and Venayagamoorthy, {Ganesh K.} and Gilsoo Jang",

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AU - Harley, Ronald G.

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AU - Jang, Gilsoo

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AB - 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.

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Dual heuristic programming based nonlinear optimal control for a synchronous generator

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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