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Alternative Moment Method for wind energy potential and turbine energy output estimation

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  • Akdağ, Seyit Ahmet
  • Güler, Önder

Abstract

An accurate analysis of wind characteristics is a critical factor for wind farm energy output estimation. Therefore, it is necessary to develop an effective and efficient method to asses wind energy resource. The main objective of the present paper is to introduce a novel method to estimate Weibull distribution parameters. This method is called Alternative Moment Method (AMM). AMM method is expressed in an analytical form and doesn’t need iterative procedure. The efficiency and accuracy of the introduced method is compared with commonly used parameter estimation methods. Result of the graphical comparison showed that AMM method is better than Justus Moment Method (JMM) and Novel Energy Pattern Factor Method (NEPFM). While mean error of AMM was 3.53 × 10−7%, JMM and NEPFM was 0.63% and 0.0031% respectively. AMM method is validated by comparing wind turbine energy output estimation accuracy for 144 cases. AMM mean energy output estimation error for 750 kW and 1600 kW wind turbine was 0.039% and 0.043%, respectively. Moreover, data from 16 weather station distributed around Spain is used to evaluate the power density estimation capability of developed method. Result showed that mean absolute error of AMM and MLH was 3.47% and 10.59%, respectively.

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  • Akdağ, Seyit Ahmet & Güler, Önder, 2018. "Alternative Moment Method for wind energy potential and turbine energy output estimation," Renewable Energy, Elsevier, vol. 120(C), pages 69-77.
    • Handle: RePEc:eee:renene:v:120:y:2018:i:c:p:69-77
    DOI: 10.1016/j.renene.2017.12.072
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    1. Jowder, Fawzi A.L., 2009. "Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain," Applied Energy, Elsevier, vol. 86(4), pages 538-545, April.
    2. Chang, Tsang-Jung & Tu, Yi-Long, 2007. "Evaluation of monthly capacity factor of WECS using chronological and probabilistic wind speed data: A case study of Taiwan," Renewable Energy, Elsevier, vol. 32(12), pages 1999-2010.
    3. Abul Kalam Azad & Mohammad Golam Rasul & Talal Yusaf, 2014. "Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications," Energies, MDPI, vol. 7(5), pages 1-30, May.
    4. Zhang, Hua & Yu, Yong-Jing & Liu, Zhi-Yuan, 2014. "Study on the Maximum Entropy Principle applied to the annual wind speed probability distribution: A case study for observations of intertidal zone anemometer towers of Rudong in East China Sea," Applied Energy, Elsevier, vol. 114(C), pages 931-938.
    5. Akdag, S.A. & Bagiorgas, H.S. & Mihalakakou, G., 2010. "Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean," Applied Energy, Elsevier, vol. 87(8), pages 2566-2573, August.
    6. Akdag, Seyit Ahmet & Güler, Önder, 2010. "Evaluation of wind energy investment interest and electricity generation cost analysis for Turkey," Applied Energy, Elsevier, vol. 87(8), pages 2574-2580, August.
    7. Chang, Tian Pau, 2011. "Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application," Applied Energy, Elsevier, vol. 88(1), pages 272-282, January.
    8. Dong, Yao & Wang, Jianzhou & Jiang, He & Shi, Xiaomeng, 2013. "Intelligent optimized wind resource assessment and wind turbines selection in Huitengxile of Inner Mongolia, China," Applied Energy, Elsevier, vol. 109(C), pages 239-253.
    9. Chang, Tian Pau, 2011. "Estimation of wind energy potential using different probability density functions," Applied Energy, Elsevier, vol. 88(5), pages 1848-1856, May.
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