【作者】 夏冬；

【导师】 吴俊勇；

【作者基本信息】 北京交通大学 ， 电气工程， 2012， 硕士

【摘要】 风电功率预测对电力系统有着重要的意义,精确的预测可以降低风电并网带来的冲击,提高风电渗透率,增强电力系统运行的稳定性。由于风电的随机性和波动性,加之我国风电场所处地形复杂,风况相对不稳定,我国的风电预测在精度上仍然有待提高。在此背景下,本文以时间序列分析为基础,选取多种风电预测模型对风电场风速和功率进行短期预测,主要工作包括以下几个方面：对风电场运行数据进行统计分析,并对其进行预处理。分别对多种方法进行研究,建立了风电预测的单一模型和综合模型。提出了基于遗忘因子优化的AR参数估计模型,将系统辨识中比较常用且精度较高的最小二乘估计应用于风电功率预测,利用递推方程进行建模。为避免数据饱和现象,在最小二乘算法中加入了遗忘因子来控制历史数据对未来预测情况的影响,其中遗忘因子根据误差情况进行自动调整来控制数据窗的大小提出了基于D-S证据理论的改进ARMA模型,将各单一模型的预测值作为多方面信息进行融合,以达到修正ARMA参数的目的。之后,对基于D-S证据理论的改进ARMA模型在选型方面进行了优化。在已有的优化模型的基础上,提出了基于组合模型的递推预测算法,将各种算法进行误差验证,选出误差最小的模型进行下一步预测,再利用变遗忘因子最小二乘算法对结果进行修正,使预测误差进一步降低。对内蒙古某风电场功率进行了预测,首先对单机风速进行预测,然后对各台风机进行功率拟合,最后将单机功率叠加得到风电场总功率。将各种算法的预测结果进行比较,实验证明组合模型在预测中具有较好的精度。更多还原

【Abstract】 Wind power prediction is significant to electrical power system. An accurate forecasting can reduce the impact of wind power on power system, and increase the penetration of wind power and enhance the stability of power system operation. Because of the intermittence and fluctuation of the wind and the complexity of China’s topography, the wind condition is not stable relatively. So China’s wind power prediction is remained to be improved in accuracy.Under the circumstances, several models are studied and used to make the prediction based on Time Series Analysis in this paper. The main works are as follow:Statistical analysis of wind farm operation data is studied. Several single models and integrated models are established and the preprocessing is made before the establishment of models.An integrated model based on D-S Evidence Theory is put forward. The weights of single models are extracted as the evidence and the integrated model is established with multi-fusion of the belief function before the final result is presented. Then, the integrated model is optimized in the selection stage of single models.A recursive least square model based on auto-regulation forgetting factor is put forward. The result is predicted by recursive equation, and the auto-regulation forgetting factor is designed to adjust automatically according to the error.The wind power prediction model library is built based on optimized models and the optimization algorism of recursive prediction based on variable models is presented. The least relative error model is selected to predict wind speed or power, and the result is revised by recursive least square model based on auto-regulation forgetting factor, at least, the final result is more accurate.更多还原