Probabilistic Modelling of 80 m Mast Measured Wind Resource: A case study

ABSTRACT

Eliciting information regarding available energy in the wind resource is intricately linked to the viability of a wind power project. The energy output of wind power plant is affected primarily by the wind speed characteristics. Probability distributions have proved to be useful for stochastic modeling of the wind resource and power potential estimation. This work fits ten most popular wind speed marginal distributions and four mixture distributions to 80 m mast measured 10-min averaged wind speed data in Odisha, India. The yearly mean wind speed is found to be 5.95 m/s with a coefficient of variation of 52% whereas the highest monthly mean wind speed of 9.14 m/s occurs in May. It is found that only generalized extreme value distribution could fit the yearly wind speed whereas a number of marginal and mixture distributions are found suitable for modeling monthly and seasonal wind speeds. The best fitted Weibull shape and scale parameters are 2.00 and 6.72 m/s, respectively. The annual wind power density (WPD) of 247 W/m² with a very high summer WPD of 414 W/m² confirms the feasibility of commercial wind power development.

KEYWORDS:

• Measured wind speed
• marginal distributions
• mixture distributions
• parameter estimation
• wind power density

Acknowledgments

This research is financially supported by the research grant under Collaborative Research and Innovation Scheme of Veer Surendra Sai University of Technology (Ref. No. VSSUT/TEQIP/34/2020). The data used in this work is proprietary of National Institute of Wind Energy, Govt. of India.

Additional information

Notes on contributors

Rajat Kanti Samal

Rajat Kanti Samal has received his B.E. in Electrical Engineering from University College of Engineering Burla, M.Tech. in Water Resources Development (Hydroelectric Power) from Indian Institute of Technology Roorkee and Ph.D. degree in Electrical Engineering (Power Systems) from Veer Surendra Sai University of Technology Burla, Odisha, India.  His research interests include renewable resource stochastic modelling and forecasting, power system operation and control, and power electronic converters.