基于最优概率分布模型的风能资源评估OA北大核心

The distribution of wind energy resources is affected by topography and climate,and wind energy resources vary greatly in different regions,so it is crucial to establish an accurate wind energy resource assessment model.In order to overcome the problem of limited adaptability of individual fitting models and parameter estimation methods,three probability distribution models(Weibull distribution,lognormal distribution and Burr distribution)are used to fit the wind speed distribution data;on this basis,three numerical methods(maximum likelihood method,method of moments estimation,and method of least squares)and an intelligent optimization algorithm(whale optimization algorithm)are applied to estimate the parameters of the three probability distribution models respectively.The optimal probability distribution model and its parameter estimation method for different regions are determined through comparative analysis.Finally,based on the optimal model and its parameters,the annual availability time of wind energy resources,average wind power density,and average wind speed are calculated.The wind speed distributions at the heights of 50 and 70 metres were simulated at five stations in Inner Mongolia,Tibet,Hubei,Shanghai and Xinjiang,China,for example.The results show that not only the optimal probability distribution models differ in different geographical areas,but also the optimal distribution models at different heights of the same site are different.Among the four parameter estimation methods,the whale optimization algorithm has the most accurate parameter identification results.The combination of multiple models and methods effectively improves the accuracy of wind energy resource assessment.