DocumentCode :
1817048
Title :
A novel method of maximum power point tracking for a SRG based wind power generation system using AI
Author :
Mohseni, M. ; Niassati, N. ; Tajik, S. ; Afjei, E.
Author_Institution :
Sch. of Electr. & Comput. Eng., Univ. Coll. of Eng., Tehran, Iran
fYear :
2012
fDate :
15-16 Feb. 2012
Firstpage :
330
Lastpage :
335
Abstract :
A novel maximum power point tracking technique is introduced in this study, for a wind power generation system, based on switched reluctance generator. This method is based on the rotor speed control of the SRG, by adjusting the excitation current with respect to the wind speed, using an artificial neural network (ANN). In order to achieve best performance, considering the non-linear nature of the SRG wind power generation system, processes of optimization are performed, using the genetic algorithm (GA). Results obtained by the optimizations were used to train the ANN. The presented MPPT method is then modeled and simulated, in MATLAB®/SIMULINK™ environment, in order to investigate and verify its performance.
Keywords :
genetic algorithms; maximum power point trackers; neural nets; power engineering computing; reluctance generators; rotors; velocity control; wind power plants; AI; ANN; GA; MATLAB/SIMULINK; MPPT method; SRG wind power generation system; artificial neural network; genetic algorithm; maximum power point tracking method; nonlinear nature; optimization processes; rotor speed control; switched reluctance generator; Energy loss; Optimized production technology; Switches; Wind power generation; Artificial Neural Network; Genetic Algorithm; Maximum Power Point Tracking; Switched Reluctance Generator; Wind Power Generation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Electronics and Drive Systems Technology (PEDSTC), 2012 3rd
Conference_Location :
Tehran
Print_ISBN :
978-1-4673-0111-4
Type :
conf
DOI :
10.1109/PEDSTC.2012.6183350
Filename :
6183350
Link To Document :
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