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Modeling and Design Optimization of Variable-Speed Wind Turbine Systems

Author

Listed:
  • Ulas Eminoglu

    (Department of Electrical and Electronic Engineering, Nigde University, Nigde 51245, Turkey)

  • Saffet Ayasun

    (Department of Electrical and Electronic Engineering, Nigde University, Nigde 51245, Turkey)

Abstract

As a result of the increase in energy demand and government subsidies, the usage of wind turbine system (WTS) has increased dramatically. Due to the higher energy production of a variable-speed WTS as compared to a fixed-speed WTS, the demand for this type of WTS has increased. In this study, a new method for the calculation of the power output of variable-speed WTSs is proposed. The proposed model is developed from the S-type curve used for population growth, and is only a function of the rated power and rated (nominal) wind speed. It has the advantage of enabling the user to calculate power output without using the rotor power coefficient. Additionally, by using the developed model, a mathematical method to calculate the value of rated wind speed in terms of turbine capacity factor and the scale parameter of the Weibull distribution for a given wind site is also proposed. Design optimization studies are performed by using the particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms, which are applied into this type of problem for the first time. Different sites such as Northern and Mediterranean sites of Europe have been studied. Analyses for various parameters are also presented in order to evaluate the effect of rated wind speed on the design parameters and produced energy cost. Results show that proposed models are reliable and very useful for modeling and optimization of WTSs design by taking into account the wind potential of the region. Results also show that the PSO algorithm has better performance than the ABC algorithm for this type of problem.

Suggested Citation

  • Ulas Eminoglu & Saffet Ayasun, 2014. "Modeling and Design Optimization of Variable-Speed Wind Turbine Systems," Energies, MDPI, vol. 7(1), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:1:p:402-419:d:32316
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    References listed on IDEAS

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    1. Oscar Barambones, 2012. "Sliding Mode Control Strategy for Wind Turbine Power Maximization," Energies, MDPI, vol. 5(7), pages 1-21, July.
    2. Kongnam, C. & Nuchprayoon, S., 2010. "A particle swarm optimization for wind energy control problem," Renewable Energy, Elsevier, vol. 35(11), pages 2431-2438.
    3. Li, H. & Chen, Z., 2009. "Design optimization and site matching of direct-drive permanent magnet wind power generator systems," Renewable Energy, Elsevier, vol. 34(4), pages 1175-1184.
    4. Maki, Kevin & Sbragio, Ricardo & Vlahopoulos, Nickolas, 2012. "System design of a wind turbine using a multi-level optimization approach," Renewable Energy, Elsevier, vol. 43(C), pages 101-110.
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    Cited by:

    1. Weijie Cheng & Renli Cheng & Jun Shi & Cong Zhang & Gaoxing Sun & Dong Hua, 2018. "Interval Power Flow Analysis Considering Interval Output of Wind Farms through Affine Arithmetic and Optimizing-Scenarios Method," Energies, MDPI, vol. 11(11), pages 1-23, November.
    2. Chih-Hong Lin, 2016. "Wind Turbine Driving a PM Synchronous Generator Using Novel Recurrent Chebyshev Neural Network Control with the Ideal Learning Rate," Energies, MDPI, vol. 9(6), pages 1-25, June.
    3. Longfu Luo & Xiaofeng Zhang & Dongran Song & Weiyi Tang & Jian Yang & Li Li & Xiaoyu Tian & Wu Wen, 2018. "Optimal Design of Rated Wind Speed and Rotor Radius to Minimizing the Cost of Energy for Offshore Wind Turbines," Energies, MDPI, vol. 11(10), pages 1-17, October.
    4. Carneiro, Tatiane C. & Melo, Sofia P. & Carvalho, Paulo C.M. & Braga, Arthur Plínio de S., 2016. "Particle Swarm Optimization method for estimation of Weibull parameters: A case study for the Brazilian northeast region," Renewable Energy, Elsevier, vol. 86(C), pages 751-759.
    5. Chen, Jincheng & Wang, Feng & Stelson, Kim A., 2018. "A mathematical approach to minimizing the cost of energy for large utility wind turbines," Applied Energy, Elsevier, vol. 228(C), pages 1413-1422.
    6. Zhiqiang Yang & Minghui Yin & Yan Xu & Yun Zou & Zhao Yang Dong & Qian Zhou, 2016. "Inverse Aerodynamic Optimization Considering Impacts of Design Tip Speed Ratio for Variable-Speed Wind Turbines," Energies, MDPI, vol. 9(12), pages 1-15, December.
    7. Yin, Minghui & Yang, Zhiqiang & Xu, Yan & Liu, Jiankun & Zhou, Lianjun & Zou, Yun, 2018. "Aerodynamic optimization for variable-speed wind turbines based on wind energy capture efficiency," Applied Energy, Elsevier, vol. 221(C), pages 508-521.
    8. Minh Quan Duong & Francesco Grimaccia & Sonia Leva & Marco Mussetta & Kim Hung Le, 2015. "Improving Transient Stability in a Grid-Connected Squirrel-Cage Induction Generator Wind Turbine System Using a Fuzzy Logic Controller," Energies, MDPI, vol. 8(7), pages 1-22, June.

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