DocumentCode :
2104661
Title :
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
Link To Document :
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