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Research Article

A novel estimation chart method based on capacity value calculated by using energy pattern factor to determine rated wind speed

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Pages 7268-7286 | Received 03 Dec 2021, Accepted 21 Jul 2022, Published online: 03 Aug 2022

References

  • Akdag, S. A., and A. Dinler. 2009. A new method to estimate Weibull parameters for wind energy applications. Energy Conversion and Management 50:1761–66. doi:10.1016/j.enconman.2009.03.020.
  • Amirinia, G., B. Kamranzad, and S. Mafi. 2017. Wind and wave energy potential in southern Caspian Sea using uncertainty analysis. Energy 120:332–45. doi:10.1016/j.energy.2016.11.088.
  • Asdrubali, F., G. Baldinelli, F. D’Alessandro, and F. Scrucca. 2015. Life cycle assessment of electricity production from renewable energies: Review and results harmonization. Renewable and Sustainable Energy Reviews 42:1113–22. doi:10.1016/j.rser.2014.10.082.
  • Ashuri, T., M. B. Zaaijer, J. R. R. A. Martins, G. J. W. Bussel, and G. A. M. Kuik. 2014. Multidisciplinary design optimization of offshore wind turbines for minimum levelized cost of energy. Renewable Energy 68:893–905. doi:10.1016/j.renene.2014.02.045.
  • Ben, U. C., A. E. Akpan, C. C. Mbonu, and C. H. Ufuafuonye. 2021. Integrated technical analysis of wind speed data for wind energy potential assessment in parts of southern and central Nigeria. Cleaner Engineering and Technology 2:100049. doi:10.1016/j.clet.2021.100049.
  • Bilgen, S. 2014. Structure and environmental impact of global energy consumption. Renewable and Sustainable Energy Reviews 38:890–902. doi:10.1016/j.rser.2014.07.004.
  • Capuzzi, M., A. Pirrera, and P. M. Weaver. 2014a. A novel adaptive blade concept for large-scale wind turbines. Part I: Aeroelastic behaviour. Energy 73:15–24.
  • Capuzzi, M., A. Pirrera, and P. M. Weaver. 2014b. A novel adaptive blade concept for large-scale wind turbines. Part II: Structural design and power performance. Energy 73:25–32. doi:10.1016/j.energy.2014.04.073.
  • Carroll, J., A. McDonald, I. Dinwoodie, D. McMillan, M. Revie, and I. Lazakis. 2017. Availability, operation and maintenance costs of offshore wind turbines with different drive train configurations. Wind Energy 20:361–78. doi:10.1002/we.2011.
  • Chellali, F., A. Khellaf, A. Belouchrani, and R. Khanniche. 2012. A comparison between wind speed distributions derived from the maximum entropy principle and Weibull distribution. Case of study; six regions of Algeria. Renewable and Sustainable Energy Reviews 16:379–85. doi:10.1016/j.rser.2011.08.002.
  • Chen, P., and D. Han. 2022. Effective wind speed estimation study of the wind turbine based on deep learning. Energy 247:123491. doi:10.1016/j.energy.2022.123491.
  • Chena, J., F. Wanga, and K. A. Stelson. 2018. A mathematical approach to minimizing the cost of energy for large utility wind turbines. Applied Energy 228:1413–22. doi:10.1016/j.apenergy.2018.06.150.
  • Chowdhury, S., J. Zhang, A. Messac, and L. Castillo. 2013. Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions. Renewable Energy 52:273–82. doi:10.1016/j.renene.2012.10.017.
  • Darwisha, A. S., S. Shaabana, E. Marsillacb, and N. M. Mahmoodc. 2019. A methodology for improving wind energy production in low wind speed regions, with a case study application in Iraq. Computers & Industrial Engineering 127:89–102. doi:10.1016/j.cie.2018.11.049.
  • Gualtieri, G. 2017. Improving investigation of wind turbine optimal site matching through the self organizing maps. Energy Conversion and Management 143:295–311. doi:10.1016/j.enconman.2017.04.017.
  • Gugliani, G. K., A. Sarkar, C. Ley, and S. Mandal. 2018. New methods to assess wind resources in terms of wind speed, load, power and direction. Renewable Energy 129:168–82. doi:10.1016/j.renene.2018.05.088.
  • Islam, M. R., Y. G. Guo, and J. G. Zhu. 2014. A review of offshore wind turbine nacelle: Technical challenges, and research and developmental trends. Renewable and Sustainable Energy Reviews 33:161–76. doi:10.1016/j.rser.2014.01.085.
  • Justus, C., W. Hargraves, A. Mikhail, and D. Graber. 1978. Methods for estimating wind speed frequency distributions. Journal of Applied Meteorology 17:350–53. doi:10.1175/1520-0450(1978)017<0350:MFEWSF>2.0.CO;2.
  • Kim, D. Y., Y. H. Kim, and B. S. Kim. 2021. Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear. Energy 214:119051. doi:10.1016/j.energy.2020.119051.
  • Manwell, J. F., J. G. McGowan, and A. L. Rogers. 2009. Wind energy explained: Theory, design and application. 2nd ed. Amherst, USA: John Wiley & Sons.
  • Mostafaeipour, A., M. Jadidi, K. Mohammadi, and A. Sedaghat. 2014. An analysis of wind energy potential and economic evaluation in Zahedan, Iran. Renewable and Sustainable Energy Reviews 30:641–50.
  • National Instruments, Wind Turbine Control Methods, 22 December 2008, http://www.ni.com/white-paper/8189/en/#top (accessed 23 September 2021)
  • Noorollahi, Y., M. A. Jokar, and A. Kalhor. 2016. Using artificial neural networks for temporal and spatial wind speed forecasting in Iran. Energy Conversion and Management 115:17–25. doi:10.1016/j.enconman.2016.02.041.
  • Oha, K. Y., J. Y. Kima, J. S. Leea, and K. W. Ryub. 2012. Wind resource assessment around Korean Peninsula for feasibility study on 100 MW class offshore wind farm. Renewable Energy 42:217–26. doi:10.1016/j.renene.2011.08.012.
  • Oyedepo, S. O., M. S. Adaramola, and S. S. Paul. 2012. Analysis of wind speed data and wind energy potential in three selected locations in south-east Nigeria. International Journal of Information and Electronics Engineering 7:1–11.
  • Perkin, S., D. Garrett, and P. Jensson. 2015. Optimal wind turbine selection methodology: A case study for Búrfell, Iceland. Renewable Energy 75:165–72. doi:10.1016/j.renene.2014.09.043.
  • Pöschke, F., V. Petrović, F. Berger, L. Neuhaus, M. Hölling, M. Kühn, and H. Schulte. 2022. Model-based wind turbine control design with power tracking capability: A wind-tunnel validation. Control Engineering Practice 120:105014. doi:10.1016/j.conengprac.2021.105014.
  • Renewables 2022 Global Status Report-REN 21. 2022. https://www.ren21.net/wp-content/uploads/2019/05/GSR2022_Full_Report.pdf (accessed 20 June 2022).
  • Rocha, P. A. C., R. C. de Sousa, C. F. de Andrade, and M. E. V. da Silva. 2012. Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil. Applied Energy 89:395–400. doi:10.1016/j.apenergy.2011.08.003.
  • Saidur, R., N. A. Rahim, M. R. Islam, and K. H. Solangi. 2011. Environmental impact of wind energy. Renewable and Sustainable Energy Reviews 5:15 2423–2430.
  • Sasser, C., M. Yu, and R. Delgado. 2022. Improvement of wind power prediction from meteorological characterization with machine learning models. Renewable Energy 183:491–501. doi:10.1016/j.renene.2021.10.034.
  • Sedaghat, A., and M. Mirhosseini. 2012. Aerodynamic design of a 300 kW horizontal axis wind turbine for province of Semnan. Energy Conversion and Management 63:87–94. doi:10.1016/j.enconman.2012.01.033.
  • Sedaghata, A., A. Hassanzadeh, J. Jamali, A. Mostafaeipour, and W. H. Chen. 2017. Determination of rated wind speed for maximum annual energy production of variable speed wind turbines. Applied Energy 205:781–89. doi:10.1016/j.apenergy.2017.08.079.
  • Shu, Z., Q. Li, and P. Chan. 2015. Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function. Applied Energy 156:362–73. doi:10.1016/j.apenergy.2015.07.027.
  • Siavash, N. K., B. Ghobadian, G. Najafi, A. Rohani, T. Tavakoli, E. Mahmoodi, R. Mamat, and M. Mazlan. 2021. Prediction of power generation and rotor angular speed of a small wind turbine equipped to a controllable duct using artificial neural network and multiple linear regression. Environmental Research 196:110434. doi:10.1016/j.envres.2020.110434.
  • Song, D., Y. Tu, L. Wang, F. Jin, Z. Li, C. Huang, E. Xia, R. M. Rizk-Allah, J. Yang, M. Su, et al. 2022. Coordinated optimization on energy capture and torque fluctuation of wind turbines via variable weight NMPC with fuzzy regulator. Applied Energy 312:118821. doi:10.1016/j.apenergy.2022.118821.
  • Sun, S., C. Wanga, Y. Wanga, X. Zhu, and H. Lu. 2022. Multi-objective optimization dispatching of a micro-grid considering uncertainty in wind power forecasting. Energy Reports 8:2859–74. doi:10.1016/j.egyr.2022.01.175.
  • Wind Turbine Control Methods. National Instruments http://www.ni.com/whitepaper/8189/en/, (accessed 15 July 2021).
  • Wiser, R., M. Bolinger, G. Heath, D. Keyser, E. Lantz, J. Macknick, T. Mai, and D. Millstein. 2016. Long-term implications of sustained wind power growth in the United States: Potential benefits and secondary impacts. Applied Energy 179:146–58. doi:10.1016/j.apenergy.2016.06.123.
  • XXXX. http://www.urban-wind.org/pdf/SMALL_WIND_TURBINES_GUIDE_final.pdf (accessed 15 June 2021)
  • Zhao, Y., L. Ye, Z. Li, X. Song, Y. Lang, and J. Su. 2016. A novel bidirectional mechanism based on time series model for wind power forecasting. Applied Energy 177:793–803. doi:10.1016/j.apenergy.2016.03.096.
  • Zou, R., J. Yang, Y. Wang, F. Liu, M. Essaaidi, and D. Srinivasan. 2021. Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer. Applied Energy 304:117707. doi:10.1016/j.apenergy.2021.117707.

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