基于改进混合高斯模型的风速分布拟合与风机年发电量估算  

Wind Speed Distribution Fitting and Annual Electricity Generation Estimation of Wind Turbine Based on Improved Mixture Gaussian Model

作　　者：王玲芝 张新波 WANG Lingzhi;ZHANG Xinbo(School of Automation,Xi’an University of Posts and Telecommunications,Xi’an 710121,Shaanxi Province,China)

机构地区：[1]西安邮电大学自动化学院,陕西省西安市710121

出　　处：《发电技术》2025年第1期103-112,共10页Power Generation Technology

基　　金：国家自然科学基金项目(52177194,62073259)。

摘　　要：【目的】为解决混合高斯模型在低风速段、高风速段以及复杂峰值、波谷部分存在较大误差的问题,提出了一种改进的混合高斯模型。【方法】改进模型的所有子分量取相同的形状参数,用风速样本值代替位置参数。同时,采用非线性最小二乘法优化调整形状参数和子分量的权重,使得模型可以精确地逼近包括风速样本局部点在内的概率密度分布。基于国内外4组风速分布数据,将该模型与混合核密度模型、混合高斯模型进行拟合效果比较,并使用2种误差指标和卡方检验系数评估3种模型的拟合优度。【结果】改进的混合高斯模型对复杂风速分布的拟合效果得到了极大提升,而且能够准确地拟合低风速段、高风速段及峰值、波谷部分的风速分布概率。此外,通过比较基于3种模型的风机年发电量估算,进一步验证了改进模型的有效性和优越性。【结论】提出的更高精度的风速分布概率模型有助于准确评估风电场的发电潜力和经济效益,对风电场的规划设计具有重要的指导意义。[Objectives]To address the significant errors in low wind speed section,high wind speed section,and peak and trough sections of the mixture Gaussian model,this paper proposes an improved mixture Gaussian model.[Methods]In the improved model,all subcomponents have the same shape parameter,and the wind speed sample values are used to replace the position parameters.Meanwhile,the nonlinear least squares method is employed to optimize and adjust the shape parameters and the weights of the subcomponents,allowing the model to accurately approximate the probability density distribution,including local points of wind speed samples.Based on four sets of wind speed distribution data from both domestic and international sources,the fitting performance of this model is compared with that of the mixed kernel density model and the Gaussian mixture model.The goodness of fit of the three models is evaluated using two error metrics and the Chi-square test coefficient.[Results]The improved Gaussian mixture model significantly enhances the fitting performance for complex wind speed distributions,and it can accurately fit the wind speed distribution probabilities in low wind speed section,high wind speed section,and peak and trough sections.Additionally,by comparing the annual electricity generation estimation of wind turbines based on the three models,the effectiveness and advantages of the improved model are further verified.[Conclusions]The proposed wind speed distribution probability model of higher precision helps accurately evaluate the power generation potential and economic benefits of wind farms,providing crucial guidance for wind farm planning and design.

关 键 词：风力发电 风速概率分布 混合高斯模型 非线性最小二乘法 拟合性能 风机 年发电量 

分 类 号：TK81[动力工程及工程热物理—流体机械及工程] TM614[电气工程—电力系统及自动化]