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| Title: | Dual heuristic programming based nonlinear optimal control for a synchronous generator |
| Author (s): | Park, Jung-Wook Harley, Ronald G. Venayagamoorthy, Ganesh K. Jang, Gilsoo |
| Department/Lab Affiliations: | Electrical and Computer Engineering |
| Keywords: | Adaptive critic designs dual heuristic programming optimal control power system stabilizer radial basis function neural network synchronous generator |
| Issue Date: | 2008-02 |
| Publisher: | Elsevier |
| Citation: | Park, Jung-Wook, Ronald G. Harley, Ganesh K. Venayagamoorthy, and Gilsoo Jang. “Dual Heuristic Programming Based Nonlinear Optimal Control for a Synchronous Generator” Engineering Applications of Artificial Intelligence, vol. 21, Issue 1, Feb. 2008, pp. 97-105. |
| 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. |
| Type: | Article - Journal text |
| In Title: | Engineering Applications of Artificial Intelligence |
| Copyright Notice: | Pre-print: author can archive with restrictions;Restriction: This does not include Cell Press; Post-print: author can archive; This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: |
| Publisher URL: | |
| Link to this page: |
| title | Dual heuristic programming based nonlinear optimal control for a synchronous generator |
| contributor.author | Park, Jung-Wook |
| contributor.author | Harley, Ronald G. |
| contributor.author | Venayagamoorthy, Ganesh K. |
| contributor.author | Jang, Gilsoo |
| contributor.deptlab | Electrical and Computer Engineering |
| contributor.sponsor | Ministry of Commerce, Industry and Energy |
| subject | Adaptive critic designs |
| subject | dual heuristic programming |
| subject | optimal control |
| subject | power system stabilizer |
| subject | radial basis function neural network |
| subject | synchronous generator |
| date.issued | 2008-02 |
| publisher | Elsevier |
| identifier.citation | Park, Jung-Wook, Ronald G. Harley, Ganesh K. Venayagamoorthy, and Gilsoo Jang. “Dual Heuristic Programming Based Nonlinear Optimal Control for a Synchronous Generator” Engineering Applications of Artificial Intelligence, vol. 21, Issue 1, Feb. 2008, pp. 97-105. |
| identifier.pub.URI | |
| description.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. |
| type | Article - Journal |
| type.DCMIType | text |
| type.status | Postprint |
| rights | Pre-print: author can archive with restrictions;Restriction: This does not include Cell Press; Post-print: author can archive; |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| rights.URI | |
| relation.isPartOf | Engineering Applications of Artificial Intelligence |
| date.available | 2008-07-25T13:50:42Z |
| identifier.persist.URI |