基于经验模式分解和混沌相空间重构的风电功率短期预测

风电场发电功率的短期预测对并网风力发电系统的安全与稳定具有重要意义。根据风电功率时间序列非平稳、非周期的特点,文中运用经验模式分解理论将风电功率时间序列分解为随机分量和趋势分量,对随机分量采用径向基函数神经网络进行混沌预测;趋势分量采用最小二乘支持向量机进行混沌预测,拟合各分量的预测值得到最终的预测结果。以云南某风电场数据对所提出的模型进行验证,证明了该预测模型比传统人工神经网络预测模型具有更高的预测精度,可为风电功率预测提供参考。

Wind Power Short-term Forecasting Based on Empirical Mode Decomposition and Chaotic Phase Space Reconstruction

It is very important to forecast short-term wind farm output for the security and stability of the power grid.Due to its non-steady and non-periodic characteristic,the wind power time series is decomposed into the random component and trend component by using the empirical mode decomposition(EMD) theory.Chaotic prediction is made for the random components and trend components using neural network with radical basis function and using least squares support vector machine,respectively,thus the final consequence can be obtained by combining the prediction result of each component.The power output of a wind farm in Yunnan is used as the case study for the model proposed.The outcome shows that the prediction model has high accuracy compared with the traditional artificial neural prediction model and provides a reference for the wind power forecasting.