基于模糊理论和逆推算法改进均值生成函数的短期风速预测研究

Research on short-term wind speed prediction based on improved mean generating function with fuzzy theory and back-stepping algorithm

准确预测风速对包含风电场的电力系统运行至关重要。为提高短期风速预测模型的精度、实用性,针对风速时间序列既有随机波动性又有趋势性的特点,文中提出了一种基于模糊理论和逆推算法改进均值生成函数的短期风速预测方法。首先利用模糊理论和逆推算法对均值生成函数进行了改进,推导出模糊均生函数,然后将其与最优子集回归模型相结合,建立短期风速预测模型。实例分析表明,与传统均值生成函数模型及经典的ARMA预测模型相比,所建新模型集合了均值生成函数和模糊理论以及逆推算法的优点,有效提高了短期风速的预测精度,具有良好的工程应用前景。

Accurate prediction of wind speed is of great importance to the operation of the power system with wind power plants. In order to improve the accuracy and practicality of short-term wind speed prediction,aimed at the characteristics of random variation and tendency of wind speed sample sequences,a short-term wind speed prediction method based on improved mean generating function with fuzzy theory and back-stepping algorithm is proposed. This paper firstly improved the construction process of the mean generating function by fuzzy theory and back-stepping algorithm,and then,combined it with the optimal subset regression algorithm to establish the short-term wind speed prediction model. The case analysis shows that,comparing the wind speeds forecasted by the proposed model with those forecasted by traditional mean generating function model and ARMA based model,the new prediction model combines the advantages of the mean generating function,the fuzzy theory and the back-stepping algorithm,which can greatly improve the prediction accuracy of shortterm wind speed,which has a broad prospect of engineering application.