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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lino, O.Y. Fette, M. Dong, Z.Y. Ramirez, J.M. |
| Copyright Year | 2006 |
| Description | Author affiliation: Paderborn Univ. (Lino, O.Y.) |
| Abstract | This paper addresses innovative nonlinear approaches for dynamic equivalencing of machines for interconnected power systems. In contrast to the existing approaches that consider only fixed equivalencing steps without taking into consideration the machine parameters, these approaches reformulate the classical conditions incorporating real electromechanical model parameters and behaviours of the machines in the dynamic equivalencing. These aspects enable the integration of modern and intelligent techniques, such as the pattern recognition algorithms, fuzzy concept as machine splitting factor and the system identification by dynamical artificial neural networks. The electromechanical-based approaches generate accurate robust, non-linear dynamic equivalents and thereby enhance significantly their consistency and practical application on network reliability, management and planning. Test of these approaches have been performed and evaluated in large-scale model of the European interconnected electric power system (UCTE/CENTREL) and 16 multi machine system |
| Starting Page | 1306 |
| Ending Page | 1314 |
| File Size | 436157 |
| Page Count | 9 |
| File Format | |
| ISBN | 1424401771 |
| DOI | 10.1109/PSCE.2006.296494 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-10-29 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Intelligent networks Power system dynamics Power system interconnection Fuzzy neural networks Power systems Nonlinear dynamical systems Pattern recognition Power system modeling Artificial intelligence Machine intelligence |
| Content Type | Text |
| Resource Type | Article |
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