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| Title: | Comparisons of an adaptive neural network based controller and an optimized conventional power system stabilizer | |
| Author (s): | Wenxin, Liu Venayagamoorthy, Ganesh K. Sarangapani, Jagannathan Wunsch, Donald C. Crow, Mariesa L. Liu, Li 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: | adaptive control closed loop systems control nonlinearities neurocontrollers nonlinear dynamical systems power system control power system stability | |
| Issue Date: | 2007 | |
| Publisher: | Institute of Electrical and Electronics Engineers IEEE | |
| Citation: | Wenxin, Liu, Venayagamoorthy, G., Jagannathan, S., Wunsch, D., Crow, M., Liu, L., and Cartes, D.A. “Comparisons Of An Adaptive Neural Network Based Controller And An Optimized Conventional Power System Stabilizer.” 16th IEEE International Conference on Control Applications, Part of IEEE Multi-conference on Systems and Control, Singapore, 1-3 October 2007 | |
| Abstract: | Power system stabilizers are widely used to damp out the low frequency oscillations in power systems. In power system control literature, there is a lack of stability analysis for proposed controller designs. This paper proposes a Neural Network (NN) based stabilizing controller design based on a sixth order single machine infinite bus power system model. The NN is used to compensate the complex nonlinear dynamics of power system. To speed up the learning process, an adaptive signal is introduced to the NN's weights updating rule. The NN can be directly used online without offline training process. Magnitude constraint of the activators is modeled as saturation nonlinearities and is included in the stability analysis. The proposed controller design is compared with Conventional Power System Stabilizers whose parameters are optimized by Particle Swarm Optimization. Simulation results demonstrate the effectiveness of the proposed controller design. | |
| Type: | Article text | |
| 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 | Comparisons of an adaptive neural network based controller and an optimized conventional power system stabilizer | |
| contributor.author | Wenxin, Liu | |
| contributor.author | Venayagamoorthy, Ganesh K. | |
| contributor.author | Sarangapani, Jagannathan | |
| contributor.author | Wunsch, Donald C. | |
| contributor.author | Crow, Mariesa L. | |
| contributor.author | Liu, Li | |
| 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 | adaptive control | |
| subject | closed loop systems | |
| subject | control nonlinearities | |
| subject | neurocontrollers | |
| subject | nonlinear dynamical systems | |
| subject | power system control | |
| subject | power system stability | |
| date.issued | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers IEEE | |
| identifier.citation | Wenxin, Liu, Venayagamoorthy, G., Jagannathan, S., Wunsch, D., Crow, M., Liu, L., and Cartes, D.A. “Comparisons Of An Adaptive Neural Network Based Controller And An Optimized Conventional Power System Stabilizer.” 16th IEEE International Conference on Control Applications, Part of IEEE Multi-conference on Systems and Control, Singapore, 1-3 October 2007 | |
| identifier.pub.URI | ||
| description.abstract | Power system stabilizers are widely used to damp out the low frequency oscillations in power systems. In power system control literature, there is a lack of stability analysis for proposed controller designs. This paper proposes a Neural Network (NN) based stabilizing controller design based on a sixth order single machine infinite bus power system model. The NN is used to compensate the complex nonlinear dynamics of power system. To speed up the learning process, an adaptive signal is introduced to the NN's weights updating rule. The NN can be directly used online without offline training process. Magnitude constraint of the activators is modeled as saturation nonlinearities and is included in the stability analysis. The proposed controller design is compared with Conventional Power System Stabilizers whose parameters are optimized by Particle Swarm Optimization. Simulation results demonstrate the effectiveness of the proposed controller design. | |
| type | Article | |
| 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 | ||
| date.accessioned | 2008-07-22T21:09:09Z | |
| date.available | 2008-07-31T21:39:11Z | |
| identifier.persist.URI | ||
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