Neural-Network-Based MPPT Control of a Stand-Alone Hybrid Power Generation System

被引:215
作者
Lin, Whei-Min [1 ]
Hong, Chih-Ming [1 ]
Chen, Chiung-Hsing [2 ]
机构
[1] Natl Sun Yat Sen Univ, Dept Elect Engn, Kaohsiung 80424, Taiwan
[2] Natl Kaohsiung Marine Univ, Dept Elect Commun Engn, Kaohsiung 81157, Taiwan
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; POINT TRACKING; WIND; ENERGY; OPTIMIZATION; INVERTER; DESIGN;
D O I
10.1109/TPEL.2011.2161775
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:3571 / 3581
页数:11
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