Intelligent Expert System for Power Quality Improvement Under Distorted and Unbalanced Conditions in Three-Phase AC Microgrids
This paper presents an expert system (ES) based on decoupled power/current decomposition and the k-nearest neighbor pattern recognition method to identify and choose the correct mitigation solution for power quality improvement in three-phase ac microgrids under non-sinusoidal current and voltage op...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | Brasil |
| Institución: | Universidade Estadual Paulista (UNESP) |
| Repositorio: | Repositório Institucional da UNESP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unesp.br:11449/186518 |
| Acceso en línea: | http://dx.doi.org/10.1109/TSG.2017.2771146 http://hdl.handle.net/11449/186518 |
| Access Level: | acceso abierto |
| Palabra clave: | Conservative power theory distributed generation expert system k-NN classifier harmonics microgrid power factor reactive power unbalance loads |
| Sumario: | This paper presents an expert system (ES) based on decoupled power/current decomposition and the k-nearest neighbor pattern recognition method to identify and choose the correct mitigation solution for power quality improvement in three-phase ac microgrids under non-sinusoidal current and voltage operations. By using power/current terms, load conformity factors and a k-nearest neighbor classifier, the proposed ES achieved 99.98% classification accuracy. Simulation studies were carried out in a PSCAD/EMTDC environment, where the IEEE 13-bus feeder test system was in a grid connected microgrid mode. The obtained results indicate that the proposed ES is robust and able to easily select an appropriate/adequate compensation solution. |
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