Modified Neural Network for Dynamic Control and Operation of a Hybrid Generation Systems
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| Format: | Artículo científico |
| Sprache: | en |
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Universidad Nacional Autónoma de México
2014
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| author | Cong-Hui Huang |
| author_facet | Cong-Hui Huang |
| contents | Modified Neural Network for Dynamic Control and Operation of a Hybrid Generation Systems Cong-Hui Huang Ingeniería diesel engine Photovoltaic system Elman neural network maximum power point tracking This paper presents modified neural network for dynamic control and operation of a hybrid generation systems. PV and wind power are the primary power sources of the system to take full advantages of renewable energy, and the diesel-engine is used as a backup system. The simulation model of the hybrid system was developed using MATLAB Simulink. To achieve a fast and stable response for the real power control, the intelligent controller consists of a Radial Basis Function Network (RBFN) and an modified Elman Neural Network (ENN) for maximum power point tracking (MPPT). The pitch angle of wind turbine is controlled by ENN, and the PV system uses RBFN, where the output signal is used to control the DC / DC boost converters to achieve the MPPT. And the results show the hybrid generation system can effectively extract the maximum power from the PV and wind energy sources. 2014 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47432794014 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.6 Vol.12 |
| format | Artículo científico |
| id | redalyc_47432794014 |
| institution | Redalyc |
| language | en |
| publishDate | 2014 |
| publisher | Universidad Nacional Autónoma de México |
| spellingShingle | Modified Neural Network for Dynamic Control and Operation of a Hybrid Generation Systems Cong-Hui Huang Ingeniería diesel engine Photovoltaic system Elman neural network maximum power point tracking Modified Neural Network for Dynamic Control and Operation of a Hybrid Generation Systems Cong-Hui Huang Ingeniería diesel engine Photovoltaic system Elman neural network maximum power point tracking This paper presents modified neural network for dynamic control and operation of a hybrid generation systems. PV and wind power are the primary power sources of the system to take full advantages of renewable energy, and the diesel-engine is used as a backup system. The simulation model of the hybrid system was developed using MATLAB Simulink. To achieve a fast and stable response for the real power control, the intelligent controller consists of a Radial Basis Function Network (RBFN) and an modified Elman Neural Network (ENN) for maximum power point tracking (MPPT). The pitch angle of wind turbine is controlled by ENN, and the PV system uses RBFN, where the output signal is used to control the DC / DC boost converters to achieve the MPPT. And the results show the hybrid generation system can effectively extract the maximum power from the PV and wind energy sources. 2014 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47432794014 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.6 Vol.12 |
| title | Modified Neural Network for Dynamic Control and Operation of a Hybrid Generation Systems |
| topic | Ingeniería diesel engine Photovoltaic system Elman neural network maximum power point tracking |
| url | https://www.redalyc.org/articulo.oa?id=47432794014 |