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| Title: | Adaptive neural network based stabilizing controller design for single machine infinite bus power systems | |
| Author (s): | Liu, Wenxin Sarangapani, Jagannathan Venayagamoorthy, Ganesh K. 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: | particle swarm optimization power systems stabilizing control | |
| Subject Terms: | Neural networks (Computer science) | |
| Issue Date: | 2007 | |
| Publisher: | Watam Press | |
| Citation: | Liu, Wenxin, Sarangapani Jagannathan, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa L. Crow and David A. Cartes. “Adaptive Neural Network Based Stabilizing Controller Design for Single Machine Infinite Bus Power Systems.” Dynamics of Continuous, Discrete and Impulse Systems, Series A, vol. 14 (S1), pp. 494-502, 2007. | |
| Abstract: | Power system stabilizers are widely used to generate supplementary control signals for the excitation system in order to damp out the low frequency oscillations. In power system control literature, the performances of the proposed controllers were mostly demonstrated using simulation results without any rigorous stability analysis. This paper proposes a stabilizing neural network (NN) controller based on a sixth order single machine infinite bus power system model. The NN is used to approximate the complex nonlinear dynamics of power system. Unlike the other indirect adaptive NN control schemes, there is no offline training process and the NN can be directly used online and learn through time. Magnitude constraint of the activators is modeled as saturation nonlinearities and is included in the Lyapunov stability analysis. The new NN controller design is compared with conventional power system stabilizers (CPSS) whose parameters are fine tuned by particle swarm optimization (PSO). Simulation results demonstrate that the proposed NN controller design can successfully damp out power system oscillations. The control algorithms of this paper can also be applied to other similar nonlinear control problems. | |
| Type: | Article - Journal text | |
| In Title: | Dynamics of Continuous, Discrete and Impulse Systems, Series A. | |
| 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 final version FULL COPYRIGHT INFORMATION: | |
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| title | Adaptive neural network based stabilizing controller design for single machine infinite bus power systems | |
| contributor.author | Liu, Wenxin | |
| contributor.author | Sarangapani, Jagannathan | |
| contributor.author | Venayagamoorthy, Ganesh K. | |
| 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 | particle swarm optimization | |
| subject | power systems | |
| subject | stabilizing control | |
| subject.LCSH | Neural networks (Computer science) | |
| date.issued | 2007 | |
| publisher | Watam Press | |
| identifier.citation | Liu, Wenxin, Sarangapani Jagannathan, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa L. Crow and David A. Cartes. “Adaptive Neural Network Based Stabilizing Controller Design for Single Machine Infinite Bus Power Systems.” Dynamics of Continuous, Discrete and Impulse Systems, Series A, vol. 14 (S1), pp. 494-502, 2007. | |
| identifier.pub.URI | ||
| description.abstract | Power system stabilizers are widely used to generate supplementary control signals for the excitation system in order to damp out the low frequency oscillations. In power system control literature, the performances of the proposed controllers were mostly demonstrated using simulation results without any rigorous stability analysis. This paper proposes a stabilizing neural network (NN) controller based on a sixth order single machine infinite bus power system model. The NN is used to approximate the complex nonlinear dynamics of power system. Unlike the other indirect adaptive NN control schemes, there is no offline training process and the NN can be directly used online and learn through time. Magnitude constraint of the activators is modeled as saturation nonlinearities and is included in the Lyapunov stability analysis. The new NN controller design is compared with conventional power system stabilizers (CPSS) whose parameters are fine tuned by particle swarm optimization (PSO). Simulation results demonstrate that the proposed NN controller design can successfully damp out power system oscillations. The control algorithms of this paper can also be applied to other similar nonlinear control problems. | |
| 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 final version | |
| rights.URI | ||
| relation.isPartOf | Dynamics of Continuous, Discrete and Impulse Systems, Series A. | |
| date.accessioned | 2008-07-22T19:26:08Z | |
| date.available | 2008-07-29T16:03:10Z | |
| identifier.persist.URI | ||
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