基于动态时间规整的风电功率爬坡滚动修正模型

不断提高风电爬坡事件特征量的预测精度对电力系统安全稳定运行意义重大.因此,提出一种爬坡事件特征量与数值天气预报(NWP)气象数据相结合的风电爬坡滚动修正模型.首先,基于PRAA算法获得历史数据库与预测数据库中的所有爬坡事件特征量,建立爬坡特征量预测误差向量矩阵.然后,分析误差向量矩阵与NWP中各气象数据的线性和非线性相关关系,识别影响爬坡特征量预测误差的有效气象指标.最后,基于动态时间规整实现未来与历史数据库中具有相似特征的有效气象指标匹配,得到未来爬坡事件预测误差修正的参考集,并进行滚动修正.算例表明,该修正模型能有效降低爬坡幅值误差,提高爬坡事件预测的精度.

Rolling Correction Model of Ramp for Wind Power Based on Dynamic Time Warping

Continuously improving the prediction accuracy of the characteristic quantities of wind power ramp events is of great significance to the safe and stable operation of the power system. Therefore, this paper proposes a rolling correction model of ramp for wind power that combines the characteristic quantities of ramp events with meteorological data of numerical weather prediction(NWP). First, based on the parameter and resolution adaptive (PRAA) algorithm, all the characteristic quantities of ramp events in the historical database and the prediction database are obtained, and the forecasting error vector matrix of ramp characteristic quantities is established. Then, the linear and non-linear correlation relationships of the error vector matrix and the meteorological data in NWP are analyzed and effective meteorological indicators that affect the prediction error of the ramp characteristic quantities are identified. Finally, effective meteorological indicators with the similar characteristic in future and historical databases are matched based on dynamic time warping (DTW). The correctional reference set of predicted errors of future ramp events is obtained and rolling corrected. The calculation example shows that the correction model can effectively reduce the ramp amplitude error and improve the prediction accuracy of ramp events.