Characterisation of wind speed series and power in Durban
Keywords:Dynamic Simulation, Markov Chain, Weibull distribution, Wind Speed, Wind Turbine
Both the planning and operating of a wind farm demand an appropriate wind speed model of its location. The model also helps predict the dynamic behaviour of wind turbines and wind power potential in the location. This study characterises the wind speed series and power in Durban (29.9560°S, 30.9730°E), South Africa, using Markov chain and Weibull distribution. Comparison of statistical quantities of measured and Markov model-generated wind speed series revealed that the model accurately represented the measured wind speed series. The Markov model and Weibull distribution were also compared through their corresponding probability density functions. The root mean square error of the Markov model against the measured wind speed series was nearly one-tenth that of the Weibull distribution, indicating the effectiveness of the former. Finally, the analysis of wind power density showed that Durban and its environs need large wind turbines with hub heights greater than 85 m for efficient utilisation of the available wind energy.
Asif, M. and Muneer, T. Energy supply, its demand and security issues for developed and emerging economies, Renewable and Sustainable Energy Reviews, 2007, 11: 1388–413, 2007.
Pimentel, D., Herz, M., Glickstein, M., Zimmerman, M., Allen, R., Becker, K., Evans, J., Hussain, B., Sarsfeld, R., Grosfeld, A. and Seidel, T. Renewable Energy: Current and Potential Issues, BioScience, 2002, 52(12):1111–1120.
Herberta, G., Iniyan, S., Sreevalsan, E. and Rajapandian, S. A review of wind energy technologies, Renewable and Sustainable Energy Reviews, 2007, 11: 1117–1145.
Feijóo, A. and Villanueva, D. Assessing wind speed simulation methods, Renewable and Sustainable Energy Reviews, 2016, 56: 473–483.
Goudarzi, A., Davidson, I. E., Ahmadi, A. and Venayagamoorthy, G. K. Intelligent analysis of wind turbine power curve, in IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) 2014: 1-7.
Slootweg, J. G. Wind power modelling and impact on power system dynamics, 2003, Ridderkerk, the Netherlands: Delft University of Technology.
Safari, B. Modeling wind speed and wind power distributions in Rwanda, Renewable and Sustainable Energy Reviews, 2011, 15: 925–935.
Zárate-Miñano, R., Anghel, M. and Milano, F. Continuous wind speed models based on stochastic differential equations, Applied Energy, 2013, 104: 42–49.
Lei, M., Shiyan, L., Chuanwen, J., Hongling, L. and Yan, Z. A review on the forecasting of wind speed and generated power, Renewable and Sustainable Energy Reviews, 2009, 13 (4): 915–920.
Nfaoui, H. Essiarab, H. and Sayigh, A. A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco, Renewable Energy, 2004, 29: 1407-1418.
Freris, L. Wind energy conversion systems, 1990, Cambridge, UK: Prentice Hall.
Arslan, T., Bulut, Y. M. and Yavuz, A. A. Comparative study of numerical methods for determining Weibull parameters for wind energy potential, Renewable and Sustainable Energy Reviews, 2014, 40: 820–825.
Mohammadi, K., Alavi, O., Mostafaeipour, A., Goudarzi, N. and Jalilvand, M. Assessing different parameters estimation methods of Weibull distribution to compute wind power density, Energy Conversion and Management, 2016, 108: 322-335.
Romero-Ternero, V. Influence of the fitted probability distribution type on the annual mean power generated by wind turbines. A case study at the Canary Islands, Energy Conversion and Management, 2008, 49(8): 2047–2054.
Carta, J. and Mentado, D. A continuous bivariate model for wind power density and wind turbine energy output estimations, Energy Conversion and Management, 2007, 48(2): 420–432.
Chang, T. Estimation of wind energy potential using different probability density functions, Applied Energy, 2011, 88(5): 1848–1856.
Hu, J. and Wang, J. Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression, Energy, 2015, 93: 1456-1466.
Qin, Z. Li, W. and Xiong, X. Estimating wind speed probability distribution using kernel density method, Electric Power Systems Research, 2011, 81(12): 2139–2146.
Shokrzadeh, S. Jozani, M. J. and Bibeau, E. Wind turbine power curve modeling using advanced parametric and nonparametric methods, IEEE Transactions on Sustainable Energy, 2014, 5(4): 1262-1269.
Kazemi, M. and Goudarzi, A. A novel method for estimating wind turbines power output based on least square approximation, International Journal of Engineering and Advanced Technology, 2012, 2(1): 97–101.
Villanueva, D. and Feijóo, A. A genetic algorithm for the simulation of correlated wind speeds, International Journal of Electrical Power and Energy Systems, 2009, 1(2): 107–112.
Shamshad, A. Bawadi, M., Hussin, W., Majid, T. and Sanusi, S. First and second order Markov chain models for synthetic generation of wind speed time series, Energy, 2005, 30: 693-708.
Kantza, H., Holsteina, D., Ragwitzb, M. and Vitanov, N. K. Markov chain model for turbulent wind speed data, Physica A, 2004, 342: 315-321.
Ettoumi, F. Y., Sauvageot, H. and Adane, A. Statistical bivariate modeling of wind using first-order Markov chain and Weibull distribution, Renewable Energy, 2003, 28: 1787-1802.
D’Amico, G., Petroni, F. and Prattico, F. First and second order semi-Markov chains for wind speed modeling, Physica A, 2012, 392: 1194-1201.
Otto, A. Wind Atlas For South Africa, June 2004, SANEDI.
Herbst, L. and Lalk, J. A case study of climate variability effects on wind resources in South Africa, Journal of Energy in Southern Africa, August 2014, 2-10.
Mosetlhe, T. C., Yusuff, A. A., Hamam, Y. and Jimoh, A. A. Estimation of wind speed statistical distribution at Vredendal, South Africa, International Journal of Power and Energy Systems, 2016, DOI: 10.2316/P.2016.839-017.
Heier, S. Grid integration of wind energy conversion systems, 2005, Chichester: John Wiley & Sons.
Meyn, S. and Tweedie, R. Markov chains and stochastic stability, 2005: Springer-Verlag.
Hocaoglu, F. O., Gerek, O. N. and Kurban, M. The Effect of Markov Chain State Size for Synthetic Wind Speed Generation, in Proceedings of the 10th International Conference on Probabilistic Methods Applied to Power Systems, 2008. PMAPS '08.
Scott, M. Characterizations of strong ergodicity for continuous time Markov chains, 1979, PhD thesis: Iowa State University Ames.
Aksoy, H., Toprak, Z. F., Aytek, A. and Unal, N. E. Stochastic generation ofhourly mean wind speed data, Renewable Energy, 2004, 29: 2111–2131.
Agustín, E.-S. C. Estimation of Extreme Wind Speeds by Using Mixed Distributions, Ingeniería, Investigación y Tecnología, 2013, 14(2): 153–162.
Datta, R. and Ranganathan, V. T. A method of tracking the peak power points for a variable speed wind energy conversion system, IEEE Transaction on Energy Conversion, 2003, 18(1): 163-168.
Ramachandra, T., Subramanian, D. and Joshi, N. Wind energy potential assessment in Uttarannada district of Karnataka, India, Renewable Energy, 1997, 10(4): 585–611.
Bhaskar, B. Energy Security and Economic Development in India: a holistic approach, The Energy and Resources Institute (TERI), 2013.
How to Cite
Copyright remains with the author(s).
Publishing rights remain with the author(s)
All articles published in JESA can be re-used under the following CC license: CC BY-SA Creative Commons Attribution-ShareAlike 4.0 International License.