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| Title: | Fault-tolerant optimal neurocontrol for a static synchronous series compensator connected to a power network | |
| Author (s): | Qiao, Wei Harley, Ronald G. Venayagamoorthy, Ganesh K. | |
| Department/Lab Affiliations: | Electrical and Computer Engineering | |
| Keywords: | Dual heuristic programming (DHP) fault-tolerant optimal neurocontrol missing sensor restoration (MSR) particle swarm optimization (PSO) radial basis funtion network static synchronous series compensator (SSSC) | |
| Issue Date: | 2008-02 | |
| Publisher: | Institute of Electrical and Electronics Engineers IEEE | |
| Citation: | Qiao, Wei, Ronald G. Harley, and Ganesh Kumar Venayagamoorthy. “Fault-Tolerant Optimal Neurocontrol for a Static Synchronous Series Compensator Connected to a Power Network.” IEEE Transactions on Industry Applications, Vol. 44, pp. 74-84, Jan./Feb. 2008. | |
| Abstract: | This paper proposes a novel fault-tolerant optimal neurocontrol scheme (FTONC) for a static synchronous series compensator (SSSC) connected to a multimachine benchmark power system. The dual heuristic programming technique and radial basis function neural networks are used to design a nonlinear optimal neurocontroller (NONC) for the external control of the SSSC. Compared to the conventional external linear controller, the NONC improves the damping performance of the SSSC. The internal control of the SSSC is achieved by a conventional linear controller. A sensor evaluation and (missing sensor) restoration scheme (SERS) is designed by using the autoassociative neural networks and particle swarm optimization. This SERS provides a set of fault-tolerant measurements to the SSSC controllers, and therefore, guarantees a fault-tolerant control for the SSSC. The proposed FTONC is verified by simulation studies in the PSCAD/EMTDC environment. | |
| Type: | Article - Journal text | |
| In Title: | IEEE Transactions on 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 | Fault-tolerant optimal neurocontrol for a static synchronous series compensator connected to a power network | |
| contributor.author | Qiao, Wei | |
| contributor.author | Harley, Ronald G. | |
| contributor.author | Venayagamoorthy, Ganesh K. | |
| contributor.deptlab | Electrical and Computer Engineering | |
| subject | Dual heuristic programming (DHP) | |
| subject | fault-tolerant optimal neurocontrol | |
| subject | missing sensor restoration (MSR) | |
| subject | particle swarm optimization (PSO) | |
| subject | radial basis funtion network | |
| subject | static synchronous series compensator (SSSC) | |
| date.issued | 2008-02 | |
| publisher | Institute of Electrical and Electronics Engineers IEEE | |
| identifier.citation | Qiao, Wei, Ronald G. Harley, and Ganesh Kumar Venayagamoorthy. “Fault-Tolerant Optimal Neurocontrol for a Static Synchronous Series Compensator Connected to a Power Network.” IEEE Transactions on Industry Applications, Vol. 44, pp. 74-84, Jan./Feb. 2008. | |
| identifier.pub.URI | ||
| description.abstract | This paper proposes a novel fault-tolerant optimal neurocontrol scheme (FTONC) for a static synchronous series compensator (SSSC) connected to a multimachine benchmark power system. The dual heuristic programming technique and radial basis function neural networks are used to design a nonlinear optimal neurocontroller (NONC) for the external control of the SSSC. Compared to the conventional external linear controller, the NONC improves the damping performance of the SSSC. The internal control of the SSSC is achieved by a conventional linear controller. A sensor evaluation and (missing sensor) restoration scheme (SERS) is designed by using the autoassociative neural networks and particle swarm optimization. This SERS provides a set of fault-tolerant measurements to the SSSC controllers, and therefore, guarantees a fault-tolerant control for the SSSC. The proposed FTONC is verified by simulation studies in the PSCAD/EMTDC environment. | |
| 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 on Industry Applications | |
| date.accessioned | 2008-07-15T18:22:23Z | |
| date.available | 2008-07-25T14:16:18Z | |
| identifier.persist.URI | ||
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