Wind energy potential assessment based on wind speed, its direction and power data

Zhiming Wang,Weimin Liu

doi:10.1038/s41598-021-96376-7

Abstract

Based on wind speed, direction and power data, an assessment method of wind energy potential using finite mixture statistical distributions is proposed. Considering the correlation existing and the effect between wind speed and direction, the angular-linear modeling approach is adopted to construct the joint probability density function of wind speed and direction. For modeling the distribution of wind power density and estimating model parameters of null or low wind speed and multimodal wind speed data, based on expectation–maximization algorithm, a two-component three-parameter Weibull mixture distribution is chosen as wind speed model, and a von Mises mixture distribution with nine components and six components are selected as the models of wind direction and the correlation circular variable between wind speed and direction, respectively. A comprehensive technique of model selection, which includes Akaike information criterion, Bayesian information criterion, the coefficient of determination R2 and root mean squared error, is used to select the optimal model in all candidate models. The proposed method is applied to averaged 10-min field monitoring wind data and compared with the other estimation methods and judged by the values of R2 and root mean squared error, histogram plot and wind rose diagram. The results show that the proposed method is effective and the area under study is not suitable for wide wind turbine applications, and the estimated wind energy potential would be inaccuracy without considering the influence of wind direction.

Highlights

Based on wind speed, direction and power data, an assessment method of wind energy potential using finite mixture statistical distributions is proposed
Based on the information criteria of Akaike information criterion (AIC) and Bayesian information criterion (BIC), we can see that the fit accuracy of mixture model is higher than that of single model, and the accuracy of LSE is the lowest
Based on EM algorithm, an assessment method of wind energy potential using finite mixture statistical distribution model is proposed, the probability density function of wind power density and the annual energy output are given for use in wind energy analyses

Summary

Introduction

Direction and power data, an assessment method of wind energy potential using finite mixture statistical distributions is proposed. Wind speed is not constant, it always fluctuates with the varying of air temperature over a period of time in different geographic locations and seasons In this case, we can take wind speed as a random variable and describe it by a probability density function (pdf). If the frequency distribution of wind speed is comprehensively expressed by an estimated pdf, the wind power density and wind energy output of wind turbines can be evaluated, which can help us make a reasonable decision whether to build a wind farm in the observed area or not, and reduce the uncertainties and the errors of wind power output e stimation[7]. The two-parameter Weibull distribution model is often recognized as an effective model and is widely used in the field of wind industry to estimate wind energy potential mainly. Some mixture distribution m odels[5,8,15,16,17,18,19,20,21,22,23,24], which consist of several single distribution models (called components), are used, such as the Weibull-Weibull mixture, the Gamma-Weibull mixture, the truncated Normal-Weibull mixture, etc

Methods

Results

Conclusion