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| Title: | Optimal wide-area monitoring and nonlinear adaptive coordinating neurocontrol of a power system with wind power integration and multiple FACTS devices |
| Author (s): | Qiao, Wei Venayagamoorthy, Ganesh K. Harley, Ronald G. |
| Department/Lab Affiliations: | Electrical and Computer Engineering |
| Keywords: | FACTS devices adaptive critic designs particle swarm optimization radial basis function network wide-area control wind power |
| Issue Date: | 2008-04 |
| Publisher: | Elsevier |
| Citation: | Qiao, Wei, Ganesh K. Venayagamoorthy, and Ronald G. Harley. “Optimal wide-area monitoring and nonlinear adaptive coordinating neurocontrol of a power system with wind power integration and multiple FACTS devices” Neural Networks, Vol. 21, pp. 466-475, 2008. |
| Abstract: | Wide-area coordinating control is becoming an important issue and a challenging problem in the power industry. This paper proposes a novel optimal wide-area coordinating neurocontrol (WACNC), based on wide-area measurements, for a power system with power system stabilizers, a large wind farm and multiple flexible ac transmission system (FACTS) devices. An optimal wide-area monitor (OWAM), which is a radial basis function neural network (RBFNN), is designed to identify the input-output dynamics of the nonlinear power system. Its parameters are optimized through particle swarm optimization (PSO). Based on the OWAM, the WACNC is then designed by using the dual heuristic programming (DHP) method and RBFNNs, while considering the effect of signal transmission delays. The WACNC operates at a global level to coordinate the actions of local power system controllers. Each local controller communicates with the WACNC, receives remote control signals from the WACNC to enhance its dynamic performance and therefore helps improve system-wide dynamic and transient performance. The proposed control is verified by simulation studies on a multimachine power system. |
| Type: | Article - Journal text |
| In Title: | Neural Networks |
| 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 | Optimal wide-area monitoring and nonlinear adaptive coordinating neurocontrol of a power system with wind power integration and multiple FACTS devices |
| contributor.author | Qiao, Wei |
| contributor.author | Venayagamoorthy, Ganesh K. |
| contributor.author | Harley, Ronald G. |
| contributor.deptlab | Electrical and Computer Engineering |
| contributor.sponsor | National Science Foundation |
| subject | FACTS devices |
| subject | adaptive critic designs |
| subject | particle swarm optimization |
| subject | radial basis function network |
| subject | wide-area control |
| subject | wind power |
| date.issued | 2008-04 |
| publisher | Elsevier |
| identifier.citation | Qiao, Wei, Ganesh K. Venayagamoorthy, and Ronald G. Harley. “Optimal wide-area monitoring and nonlinear adaptive coordinating neurocontrol of a power system with wind power integration and multiple FACTS devices” Neural Networks, Vol. 21, pp. 466-475, 2008. |
| identifier.pub.URI | |
| description.abstract | Wide-area coordinating control is becoming an important issue and a challenging problem in the power industry. This paper proposes a novel optimal wide-area coordinating neurocontrol (WACNC), based on wide-area measurements, for a power system with power system stabilizers, a large wind farm and multiple flexible ac transmission system (FACTS) devices. An optimal wide-area monitor (OWAM), which is a radial basis function neural network (RBFNN), is designed to identify the input-output dynamics of the nonlinear power system. Its parameters are optimized through particle swarm optimization (PSO). Based on the OWAM, the WACNC is then designed by using the dual heuristic programming (DHP) method and RBFNNs, while considering the effect of signal transmission delays. The WACNC operates at a global level to coordinate the actions of local power system controllers. Each local controller communicates with the WACNC, receives remote control signals from the WACNC to enhance its dynamic performance and therefore helps improve system-wide dynamic and transient performance. The proposed control is verified by simulation studies on a multimachine power system. |
| 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 | Neural Networks |
| date.available | 2008-07-25T14:05:11Z |
| identifier.persist.URI |