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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Baek, Seung-Mook Park, Jung-Wook Venayagamoorthy, Ganesh K. |
| Copyright Year | 2007 |
| Description | Author affiliation: School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea (Baek, Seung-Mook) || Real-Time Power and Intelligent Systems Laboratory, the Department of Electrical and Computer Engineering, University of Missouri-Rolla, MO 65409-0249, USA. e-mail: gkumar@ieee.org (Venayagamoorthy, Ganesh K.) || School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea. phone: +82-2-2123-5867; Fax: +82-2-313-2879; e-mail: jungpark@yonsei.ac.kr (Park, Jung-Wook) |
| Abstract | This paper describes the optimal tuning for the output limits of the power system stabilizer (PSS), which can improve the system damping performance immediately following a large disturbance. The non-smooth nonlinear parameters such as the saturation limits of the PSS cannot be tuned by the conventional methods based on linear approaches. To implement the systematic optimal tuning for the output limits of the PSS, a feedforward neural network (FFNN) is applied to the hybrid system model based on the differential-algebraic-impulsive-switched (DAIS) structure. The FFNN is firstly designed to identify the trajectory sensitivities obtained from the DAIS structure. Thereafter, it estimates the second-order derivatives of an objective function J, which is used during iterations of optimization process. The performance of the optimal output limits tuned by the proposed method is evaluated by applying a large disturbance to a power system. |
| Starting Page | 979 |
| Ending Page | 984 |
| File Size | 379630 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424413799 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2007.4371091 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Power system dynamics Hybrid power systems Nonlinear dynamical systems Neural networks Power system transients Damping Feedforward neural networks Power system simulation Power systems Helium |
| Content Type | Text |
| Resource Type | Article |
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