APPLICATION OF THE MAXIMUM ENTROPY PRINCIPLE TO WIND POWER INDUSTRY
DOI:
https://doi.org/10.35546/kntu2078-4481.2025.3.1.33Keywords:
wind energy power conversion, maximum entropy principle, distribution function, wind energy potential, Weibull distributionAbstract
Renewable energy sources have gained much attention due to the recent energy crisis and the urge to get clean energy. Wind energy is one of the important renewable energy resources. In the field of wind energy conversion, accurate determination of the probability distribution of wind speed ensures the efficient use of wind energy, thereby improving the position of wind power. The two-parameter Weibull function is most often used to describe such a distribution. However, for cases where the wind speed distribution by gradation has a character that is not typical for open areas, the accuracy of the Weibull function may not be sufficient. The paper presents the results of mathematical modeling of wind potential and determination of wind power plant productivity based on the principle of maximum entropy using modern mathematical, statistical, and computer methods of calculation and analysis. A method for applying the maximum entropy principle to describe the wind speed distribution function has been developed. It has been shown that the use of the Curve Fitting package of the Matlab system allows significantly simplifying and automating the process of determining the Lagrange multipliers. Optimal wind speed distribution functions obtained by the maximum entropy method for different numbers of moment functions are determined. The statistical characteristics of the developed distributions are compared with the Weibull distribution. It has been proven that the description of the specific power of the wind flow based on the maximum entropy method has the highest accuracy and better reflects the features of the experimental wind speed distribution. Based on the obtained wind speed distributions, the wind energy potential of the area was determined. It was shown that the maximum entropy method gives the best agreement with the experimental data. The use of the developed toolkit allows increasing the accuracy of mathematical modeling of wind speed distribution, and with it the accuracy of long-term forecasting of wind turbine electricity generation.
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