An extension neural network based incremental MPPT method for a PV system

Author

Chao, Kuei-Hsiang ; Wang, Meng-Huei ; Lee, Yu-Hsu

Author_Institution

Dept. of Electr. Eng., Nat. Chin-Yi Univ. of Technol., Taichung, Taiwan

Volume

2

fYear

2011

fDate

10-13 July 2011

Firstpage

654

Lastpage

660

Abstract

In this paper, a novel incremental conductance (INC) maximum power point tracking (MPPT) method based on extension neural network (ENN) is developed to make full use of photovoltaic (PV) array output power. The proposed method can adjust the step size to track the PV array´s maximum power point (MPP) automatically. Compared with the conventional fixed step size INC and variable step size INC methods, the presented approach is able to effectively improve the dynamic response and steady state performance of a PV system simultaneously. A theoretical analysis and the design principle of the proposed method are described in detail. Some simulation results are performed to verify the effectiveness of the proposed ENN MPPT method.

Keywords

dynamic response; neural nets; photovoltaic power systems; power engineering computing; solar cell arrays; ENN MPPT method; INC; PV array output power; PV system; conventional fixed step size; design principle; dynamic response; extension neural network; incremental MPPT method; incremental conductance; maximum power point tracking method; photovoltaic array; steady state performance; theoretical analysis; Arrays; Clustering algorithms; Equations; Machine learning; Steady-state; Training; Tuning; Extension neural network (ENN); Incremental conductance (INC) method; Maximum power point tracking (MPPT); Photovoltaic (PV) system;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2011 International Conference on

Conference_Location

Guilin

ISSN

2160-133X

Print_ISBN

978-1-4577-0305-8

Type

conf

DOI

10.1109/ICMLC.2011.6016761

Filename

6016761