Optimizing the wind power generation in low wind speed areas using an advanced hybrid RBF neural network coupled with the HGA-GSA optimization method-Reference-Cited by-同舟云学术

Optimizing the wind power generation in low wind speed areas using an advanced hybrid RBF neural network coupled with the HGA-GSA optimization method

Published:2016-10 Issue:10 Volume:30 Page:4735-4745
ISSN:1738-494X
Container-title:Journal of Mechanical Science and Technology
language:en
Short-container-title:J Mech Sci Technol

Author:

Assareh Ehsanolah,Poultangari Iman,Tandis Emad,Nedaei Mojtaba

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,Mechanics of Materials

Link

http://link.springer.com/content/pdf/10.1007/s12206-016-0945-4.pdf

Reference24 articles.

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2. M. Nedaei, A. Ataei, C. K. Yoo, J. K. Choi and E. Assareh, The potential of wind for energy production and water pumping in Iran, Saravan County, Distributed Generation & Alternative Energy Journal, 31 (1) (2016) 7–26.

3. M. Biglari, E. Assareh, M. Nedaei and I. Poultangari, An initial evaluation of wind resource in the Port of Chabahar in South East of Iran, Glob. J. Sci. Eng. Technol., 2 (14) (2013) 142–148.

4. M. Nedaei, Feasibility of installing wind turbines in south of Khuzestan Province in Iran in order to produce electricity, Proceedings of 17th Conference on Electrical Power Distribution Networks (EPDC), IEEE (2012) 1–6.

5. B. Boukhezzar and H. Siguerdidjane, Nonlinear control of a variable speed wind turbine using a two mass model, IEEE Transactions on Energy Conversion, 26 (1) (2011) 149–162.

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