Location wise comparison of mixture distributions for assessment of wind power potential: A parametric study

ABSTRACT

Scientific literature discussed various types of mixture models and models derived from maximum entropy principle using short-term wind speed data for their relative assessment. The literature on suitability of these mixture models for long-term data is rarely available. However, for correct assessment of wind power potential both wind speed and wind direction are equally important. Therefore, in this paper, both wind speed and wind direction are simultaneously analyzed using several types of mixture distribution and compared the same with conventional Weibull distribution. For wind speed and wind power density assessment, the mixture distributions such as Weibull--Weibull distribution, Gamma--Weibull distribution, Truncated Normal--Weibull distribution, Truncated Normal--Normal distribution, proposed Truncated Normal--Gamma distribution and Gamma--Gamma distribution along with MEP-distribution are compared with conventional 2-parameter Weibull distribution. Similarly, for wind direction analysis, the finite mixtures of von-Mises distribution are compared with conventional von-Mises distribution. Judgment criteria include R², RMSE, Kolmogorov--Smirnov test and relative percentage error in wind power density. The sites selected are the three onshore locations of India, viz., Calcutta, Trivandrum, and Ahmedabad. The results show that for wind speed assessment, mixture distribution performs better than the conventional Weibull distribution for analyzing wind power density. However, location wise comparison of all mixture distribution is of prime importance. For wind direction analysis, finite mixture of two von-Mises distributions proved to be a suitable candidate for Indian climatology.

KEYWORDS:

• Weibull distribution
• wind direction
• wind power density
• wind rose
• wind speed

Funding

The authors would like to thank the Bhabha Atomic Research Center for providing the fund to carry out research.

Acknowledgments

The authors would like to thank the Indian Meteorological Department, Pune for the supply of wind data used in this research. The corresponding author is grateful to DAAD and Prof. Michael Kasperski for motivating to carry out research in wind climate modeling.

Additional information

Funding

The authors would like to thank the Bhabha Atomic Research Center for providing the fund to carry out research.