Estimating wind speed probability distribution based on measured data at Burla in Odisha, India

ABSTRACT

In this work, the wind speed probability distribution is estimated for Burla location in the state of Odisha in the east coast of India. For this purpose, 10 min averaged wind speed data collected over one year period at Burla is utilized. Specifically, Weibull, Gamma, Lognormal, Inverse Gaussian distributions; mixture distributions such as Weibull-Weibull, Gamma-Weibull, Normal-Weibull, and Normal-Normal are examined to evaluate their suitability to represent the measured wind speed. The non-parametric kernel density method is also used to represent the measured wind speed wherever the parametric distributions are not suitable. Chi-square test and Kolmogorov-Smirnov goodness-of-fit tests are used to evaluate the suitability of each of the above distributions.

KEYWORDS:

• Goodness-of-fit tests
• kernel density
• mixture distributions
• parameter estimation
• wind speed probability distributions

Additional information

Funding

This research is financially supported by AICTE TEQIP SEED GRANT for the year 2016 of Veer Surendra Sai University of Technology.