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| Title: | A heuristic-dynamic-programming-based power system stabilizer for a turbogenerator in a single-machine power system | |
| Author (s): | Liu, Wenxin Venayagamoorthy, Ganesh K. Wunsch, Donald C. | |
| Department/Lab Affiliations: | Applied Computational Intelligence Laboratory Electrical and Computer Engineering Intelligent Systems Center | |
| Keywords: | adaptive critic design (ACD) discount factors heuristic dynamic programming (HDP) indirect adaptive control neural networks neuro-control neuro-identifier online training power system stabilizer (PSS) | |
| Issue Date: | 2005 | |
| Publisher: | Institute of Electrical and Electronics Engineers IEEE | |
| Citation: | Wenxin, Liu, Venayagamoorthy, Ganesh K., and Wunsch, Donald C. “A Heuristic-Dynamic-Programming-Based Power System Stabilizer for a Turbogenerator in a Single-Machine Power System.” IEEE Transactions in Industry Applications, vol. 41, no. 5, pp. 1377-1385, 2005. | |
| Abstract: | Power system stabilizers (PSSs) are used to generate supplementary control signals for the excitation system in order to damp the low-frequency power system oscillations. To overcome the drawbacks of a conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design based on heuristic dynamic programming (HDP) is presented in this paper. HDP, combining the concepts of dynamic programming and reinforcement learning, is used in the design of a nonlinear optimal power system stabilizer. Results show the effectiveness of this new technique. The performance of the HDP-based PSS is compared with the CPSS and the indirect-adaptive-neurocontrol-based PSS under small and large disturbances. In addition, the impact of different discount factors in the HDP PSS's performance is presented. | |
| Type: | Article - Journal text | |
| In Title: | IEEE Transactions in Industry Applications | |
| Copyright Notice: | 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. allows publisher's final version to be uploaded FULL COPYRIGHT INFORMATION: | |
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| title | A heuristic-dynamic-programming-based power system stabilizer for a turbogenerator in a single-machine power system | |
| contributor.author | Liu, Wenxin | |
| contributor.author | Venayagamoorthy, Ganesh K. | |
| contributor.author | Wunsch, Donald C. | |
| contributor.deptlab | Applied Computational Intelligence Laboratory | |
| contributor.deptlab | Electrical and Computer Engineering | |
| contributor.deptlab | Intelligent Systems Center | |
| subject | adaptive critic design (ACD) | |
| subject | discount factors | |
| subject | heuristic dynamic programming (HDP) | |
| subject | indirect adaptive control | |
| subject | neural networks | |
| subject | neuro-control | |
| subject | neuro-identifier | |
| subject | online training | |
| subject | power system stabilizer (PSS) | |
| date.issued | 2005 | |
| publisher | Institute of Electrical and Electronics Engineers IEEE | |
| identifier.citation | Wenxin, Liu, Venayagamoorthy, Ganesh K., and Wunsch, Donald C. “A Heuristic-Dynamic-Programming-Based Power System Stabilizer for a Turbogenerator in a Single-Machine Power System.” IEEE Transactions in Industry Applications, vol. 41, no. 5, pp. 1377-1385, 2005. | |
| identifier.pub.URI | ||
| description.abstract | Power system stabilizers (PSSs) are used to generate supplementary control signals for the excitation system in order to damp the low-frequency power system oscillations. To overcome the drawbacks of a conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design based on heuristic dynamic programming (HDP) is presented in this paper. HDP, combining the concepts of dynamic programming and reinforcement learning, is used in the design of a nonlinear optimal power system stabilizer. Results show the effectiveness of this new technique. The performance of the HDP-based PSS is compared with the CPSS and the indirect-adaptive-neurocontrol-based PSS under small and large disturbances. In addition, the impact of different discount factors in the HDP PSS's performance is presented. | |
| type | Article - Journal | |
| type.DCMIType | text | |
| type.status | Final version | |
| 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 | allows publisher's final version to be uploaded | |
| rights.URI | ||
| rights.URI | ||
| rights.URI | ||
| relation.isPartOf | IEEE Transactions in Industry Applications | |
| date.accessioned | 2008-07-22T21:44:27Z | |
| date.available | 2008-07-31T19:46:01Z | |
| identifier.persist.URI | ||
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