Application of a Basic Variable Neighborhood Search Algorithm in the Coordinated Tuning of PSS and POD Controllers
This paper proposes the application of a Basic Variable Neighborhood Search algorithm in the coordinated and simultaneous tuning of the parameters of damping controllers known as power system stabilizer and thyristor-controlled series capacitor–power oscillation damping. The controllers are inserted...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | Brasil |
| Institución: | Universidade Estadual Paulista (UNESP) |
| Repositorio: | Repositório Institucional da UNESP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unesp.br:11449/169880 |
| Acceso en línea: | http://dx.doi.org/10.1007/s40313-017-0321-3 http://hdl.handle.net/11449/169880 |
| Access Level: | acceso abierto |
| Palabra clave: | Basic Variable Neighborhood Search POD Power system stabilizers Small-signal stability TCSC |
| Sumario: | This paper proposes the application of a Basic Variable Neighborhood Search algorithm in the coordinated and simultaneous tuning of the parameters of damping controllers known as power system stabilizer and thyristor-controlled series capacitor–power oscillation damping. The controllers are inserted into the multi-machine power system New England (10 generators, 39 buses and 46 transmission lines) in order to guarantee its small-signal stability. A current injection model for the thyristor-controlled series capacitor is presented and incorporated into the current sensitivity model, which is used to represent the electric power system and its components. The performance of the method proposed in this work is compared to three other methods found in the literature: local search, iterated local search and particle swarm optimization. The results show that, of the techniques analyzed, the Basic Variable Neighborhood Search is the most efficient for this type of problem, presenting high convergence rates and the shortest processing times with robust solutions considering different scenarios with load variations. |
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