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| Title: | Decentralized neural network-based excitation control of large-scale power systems |
| Author (s): | Liu, Wenxin Sarangapani, Jagannathan Venayagamoorthy, Ganesh K. Lu, Li Wunsch, Donald C. Crow, Mariesa L. Cartes, David A. |
| Department/Lab Affiliations: | Applied Computational Intelligence Laboratory Computer Science Electrical and Computer Engineering Energy Research and Development Center Engineering Management & Systems Engineering Intelligent Systems Center |
| Keywords: | decentralized control large-scale system neural networks power system control |
| Issue Date: | 2007 |
| Publisher: | Institute of Control, Robotics and Systems |
| Citation: | Wenxin Liu, Sarangapani Jagannathan, G.K. Venayagamoorthy, Li Lu, D.C. Wunsch II, M. Crow, and David A. Cartes. "Decentralized neural network-based excitation control of large-scale power systems" International Journal of Control, Automation, and Systems, vol. 5, no. 5, 2007, pp. 526-538. |
| Abstract: | This paper presents a neural network based decentralized excitation controller design for large-scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem control activities are guaranteed through rigorous stability analysis. Neural networks in the controller design are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded. To evaluate its performance, the proposed controller design is compared with conventional controllers optimized using particle swarm optimization. Simulations with a three-machine power system under different disturbances demonstrate the effectiveness of the proposed controller design. |
| Type: | Article - Journal text |
| In Title: | International Journal of Control, Automation, and Systems |
| 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. No full text allowed FULL COPYRIGHT INFORMATION: |
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| title | Decentralized neural network-based excitation control of large-scale power systems |
| contributor.author | Liu, Wenxin |
| contributor.author | Sarangapani, Jagannathan |
| contributor.author | Venayagamoorthy, Ganesh K. |
| contributor.author | Lu, Li |
| contributor.author | Wunsch, Donald C. |
| contributor.author | Crow, Mariesa L. |
| contributor.author | Cartes, David A. |
| contributor.deptlab | Applied Computational Intelligence Laboratory |
| contributor.deptlab | Computer Science |
| contributor.deptlab | Electrical and Computer Engineering |
| contributor.deptlab | Energy Research and Development Center |
| contributor.deptlab | Engineering Management & Systems Engineering |
| contributor.deptlab | Intelligent Systems Center |
| subject | decentralized control |
| subject | large-scale system |
| subject | neural networks |
| subject | power system control |
| date.issued | 2007 |
| publisher | Institute of Control, Robotics and Systems |
| identifier.citation | Wenxin Liu, Sarangapani Jagannathan, G.K. Venayagamoorthy, Li Lu, D.C. Wunsch II, M. Crow, and David A. Cartes. "Decentralized neural network-based excitation control of large-scale power systems" International Journal of Control, Automation, and Systems, vol. 5, no. 5, 2007, pp. 526-538. |
| identifier.pub.URI | |
| description.abstract | This paper presents a neural network based decentralized excitation controller design for large-scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem control activities are guaranteed through rigorous stability analysis. Neural networks in the controller design are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded. To evaluate its performance, the proposed controller design is compared with conventional controllers optimized using particle swarm optimization. Simulations with a three-machine power system under different disturbances demonstrate the effectiveness of the proposed controller design. |
| type | Article - Journal |
| type.DCMIType | text |
| 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 | No full text allowed |
| rights.URI | |
| relation.isPartOf | International Journal of Control, Automation, and Systems |
| date.available | 2008-07-15T21:28:08Z |
| identifier.persist.URI |