Generalized Dynamic Fuzzy Neural Network-based Tracking Control of PV

Author

Yang Xu ; Zeng Chengbi

Author_Institution

Sch. of Electr. Eng. & Inf., Sichuan Univ., Chengdu, China

fYear

2010

fDate

28-31 March 2010

Firstpage

1

Lastpage

4

Abstract

This paper introduces a new maximum power point tracking (MPPT) based on a new generalized dynamic fuzzy neural network (GD-FNN) for photovoltaic (PV) systems. Fuzzy control rules can be generated or deleted automatically according to their significance to the control system, and no predefined fuzzy rules are required. The basic principle and the implementation procedures of GD-FNN are elaborated in the paper and the PV simulation model in Matlab/Simulink is also developed. Compared with the conventional fuzzy control method, the proposed approach can effectively improve the MPPT speed and accuracy simultaneously. Furthermore, it is simple and can be easily implemented.

Keywords

fuzzy control; fuzzy neural nets; maximum power point trackers; photovoltaic power systems; power system control; Matlab-Simulink; fuzzy control rules; generalized dynamic fuzzy neural network; maximum power point tracking; photovoltaic systems; Automatic control; Automatic generation control; Control systems; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Mathematical model; Neural networks; Photovoltaic systems; Solar power generation;

fLanguage

English

Publisher

ieee

Conference_Titel

Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific

Conference_Location

Chengdu

Print_ISBN

978-1-4244-4812-8

Electronic_ISBN

978-1-4244-4813-5

Type

conf

DOI

10.1109/APPEEC.2010.5448885

Filename

5448885