Enhancement of Hybrid Power Scheme Based on Genetic Algorithm Using Three DC Source

Authors(2) :-U. Ramani, Dr. B. Kannapiran

Now a day's Solar Power is essential for all domestic purposes due to the demand of power consumption. But the single Solar system doesn't give the consistent yield due to sun power illumination. To conquer this issue, this project work presents enhancement of hybrid power scheme utilizes with PV/Fuel cell/Battery power to produce constant power using genetic algorithm (GA). The proposed hybrid scheme initially utilizes four module power controllers for independently expose to different duty cycles. The constant DC source is carried out by hybrid power scheme is to optimize the different duty cycle and to provide constant voltage through the load. This paper presents Genetic algorithm (GA) based model to enhance the duty cycle of the controller for a hybrid power control scheme. The simulation result was carried out by MATLAB/SIMULINK. From the model, the data required are generated and the performance is analyzed using a genetic algorithm. The GA based approach indicates better performance when compared with neural network controller.

Authors and Affiliations

U. Ramani
U. Ramani, Department of Instrumentation and Control Engineering, Kalasalingam University, Virudhunagar, Tamailnadu, India
Dr. B. Kannapiran
Dr. B. Kannapiran, Department of Instrumentation and Control Engineering, Kala

Hybrid DC source, Genetic algorithm, Duty cycle Enhancement.

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Publication Details

Published in : Volume 3 | Issue 5 | May-June 2017
Date of Publication : 2017-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 101-108
Manuscript Number : ICASCT2517
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

U. Ramani, Dr. B. Kannapiran , " Enhancement of Hybrid Power Scheme Based on Genetic Algorithm Using Three DC Source", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 5, pp.101-108, May-June-2017.
Journal URL : http://ijsrst.com/ICASCT2517

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