基于原子稀疏分解和BP神经网络的风电功率爬坡事件预测

摘要

超短期风电功率爬坡事件越来越影响风电机组在电网中的运行。当前国内对爬坡事件的定义并不明确，缺少相应的预测方法。阐述了风电功率爬坡事件的物理含义，提出了一种基于原子稀疏分解和反向传播神经网络(BPNN)的组合预测方法，分别建立了原子分量自预测模型、残差分量预测模型和组合预测模型。以实际风电场数据进行验证，对不同预测方法和不同时间空间实测数据进行了较全面的分析，结果表明该方法可以提高预测精度，并能降低绝对平均误差和均方根误差计算值的统计区间。%Ultra-short-term wind power ramp events have been increasingly influencing the wind machine operation in power systems.The domestic definition of ramp events is not clear and the corresponding forecasting methods are absent at present. The physical meaning of the wind power ramp events is elaborated and a combination forecast method based on the atomic sparse decomposition (ASD) and back propagation (BP) neural networks is proposed.Atomic components self-prediction model,error component prediction model and the combination prediction model are established separately.The different prediction methods and different time space measured data are analyzed comprehensively through applications with real wind farm data.The results show that the proposed method can improve the prediction accuracy,and the statistical intervals of absolute mean error and root mean square error are also remarkably reduced.