Development of intelligent MPPT (maximum power point tracking) control for a grid-connected hybrid power generation system

被引：116
|
作者：
Hong, Chih-Ming [2 ]
Ou, Ting-Chia [1 ]
Lu, Kai-Hung [2 ]
机构：
[1] Atom Energy Council, Inst Nucl Energy Res, Tao Yuan 325, Taiwan
[2] Natl Sun Yat Sen Univ, Dept Elect Engn, Kaohsiung 804, Taiwan
来源：
ENERGY | 2013年 / 50卷
关键词：
Hybrid power system; Wilcoxon (radial basis function network) RBFN; Improved (Elman neural network) ENN; (Maximum power point tracking) MPPT; Diesel-engine;
D O I：
10.1016/j.energy.2012.12.017
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
A hybrid power control system is proposed in the paper, consisting of solar power, wind power, and a diesel-engine. To achieve a fast and stable response for the real power control, an intelligent controller was proposed, which consists of the Wilcoxon (radial basis function network) RBFN and the improved (Elman neural network) ENN for (maximum power point tracking) MPPT. The pitch angle control of wind power uses improved ENN controller, and the output is fed to the wind turbine to achieve the MPPT. The solar array is integrated with an RBFN control algorithm to track the maximum power. MATLAB (MATrix LABoratory)/Simulink was used to build the dynamic model and simulate the solar and diesel-wind hybrid power system. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码：270 / 279
页数：10
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