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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chia-Chi Chu Hung-Chi Tsai |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Nat. Tsing Hua Univ., Hsinchu (Chia-Chi Chu) |
| Abstract | A Lyapunov-based adaptive neural network unified power flow controller (UPFC) is developed for improving transient stability of power systems. A simple UPFC dynamical model, composed of a controllable shunt susceptance on the shunt side and an ideal complex transformer on the series side, is utilized to analyze UPFC dynamical characteristics and control parameters. The corresponding energy function and the damping control strategy of a classical generator embedded with a UPFC is derived analytically. This energy-based damping control strategy can also be extended into the interconnected power systems by considering the associated two-machine equivalent model. In order to consider more detailed power system models and model uncertainty issues, we incorporate the adaptive recurrent neural network into our UPFC damping controller. This controller can be treated as neural network approximations of Lyapunov control actions in real time and can adjust the corresponding weights in the neural network by the built-in back propagation algorithm. Simulation results demonstrate that the proposed control strategy is very effective for suppressing power swing even under severe system conditions. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 546816 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424419050 |
| ISSN | 19325517 |
| DOI | 10.1109/PES.2008.4596580 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-07-20 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Power systems Power system dynamics Generators Adaptation model Damping Converters Artificial neural networks Back Propagation UPFC FACTS Transient Stability Energy functions Recurrent Neural network |
| Content Type | Text |
| Resource Type | Article |
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