| Revista: | Journal of applied research and technology |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000380498 |
| ISSN: | 1665-6423 |
| Autores: | Huang, Cong-Hui1 |
| Instituciones: | 1Far East University, Department of Automation and Control Engineering, Tainan. Taiwán |
| Año: | 2014 |
| Periodo: | Dic |
| Volumen: | 12 |
| Número: | 6 |
| Paginación: | 1154-1164 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | 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 |
| Disciplinas: | Ingeniería, Ciencias de la computación |
| Palabras clave: | Ingeniería de control, Sistemas fotovoltaicos, Redes neuronales, Control dinámico, Sistemas de generación híbridos, Motores diesel |
| Keyword: | Engineering, Computer science, Control engineering, Photovoltaic systems, Neural networks, Dynamic control, Hybrid generation systems, Diesel engines |
| Texto completo: | Texto completo (Ver HTML) |
