期刊
IEEE TRANSACTIONS ON POWER ELECTRONICS
卷 26, 期 12, 页码 3571-3581出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPEL.2011.2161775
关键词
Diesel engine; improved Elman neural network (ENN); maximum power point tracking (MPPT); photovoltaic (PV) power system; radial basis function network (RBFN); wind power system
资金
- National Science Council in Taiwan, R.O.C. [NSC 100-3113-P-110-003, NSC 100-3113-E-194-001]
A stand-alone hybrid power system is proposed in this paper. The system consists of solar power, wind power, diesel engine, and an intelligent power controller. MATLAB/Simulink was used to build the dynamic model and simulate the system. 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 improved Elman neural network (ENN) for maximum power point tracking (MPPT). The pitch angle of wind turbine is controlled by the ENN, and the solar system uses RBFN, where the output signal is used to control the dc/dc boost converters to achieve the MPPT.
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