基于时间序列分析和卡尔曼滤波算法的风电场风速预测优化模型

为提高风电场风速的预测精度，解决时序模型预测延时的问题，文章提出了一种时间序列分析和卡尔曼滤波相结合的混合算法。基本思路为：首先利用时间序列分析理论，对风电场风速信号进行非平稳建模，得到符合其变化规律的模型方程；其次通过得到的模型方程推导出卡尔曼滤波法的状态方程和观测方程；然后依靠卡尔曼预测递推方程进行预测；最后对某实测风速信号进行预测。实例分析表明：采用该混合算法可以提高预测精度，而且较好地解决了预测延时问题。

A Wind Speed Forecasting Optimization Model for Wind Farms Based on Time Series Analysis and Kalman Filter Algorithm

To improve the wind speed forecasting accuracy for wind farm and solve the problem of time delay of forecasting by time series model, the authors propose a hybrid algorithm integrating time series analysis with Kalman filter. The basic thinking of this algorithm is as following: firstly, by use of time series analysis theory the non-stationary modeling for wind speed signals of wind farm is proceeded to obtain the model equation conforming to its variation law; secondly, by means of the obtained model equation the state equation and observational equation for Kalman filter are deduced; thirdly, the wind speed is forecasted by Kalman forecasting recurrence equation; finally, the forecasting for varying wind speed measured in a certain wind farm is conducted to validate the proposed hybrid algorithm. Case study results show that by using this hybrid algorithm the forecasting accuracy of wind speed can be improved and the time delay in the forecasting is well solved.