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

被引：218

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

来源：

IEEE TRANSACTIONS ON POWER ELECTRONICS | 2011年 / 26卷 / 12期

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

页码：3571 / 3581

页数：11

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