基于麻雀搜索算法优化变分模态分解和混合核极限学习机的短期风电功率预测

Short-term Wind Power Prediction Based on SSA Optimized Variational Mode Decomposition and Hybrid Kernel Extreme Learning Machine

为提高风电功率的预测精度,提出了一种变分模态分解(variational mode decomposition,VMD)和混合核极限学习机(hybrid kernel extreme learning machine,HKELM)相结合的短期风电功率预测方法。针对VMD和HKELM参数难调问题,以及实现两算法参数的自适应选择,采用麻雀搜索算法(sparrow search algorithm,SSA)对两种算法中的关键参数进行优化。首先,基于3种信号分解指标设计SSA优化VMD的适应度函数,对VMD关键参数进行寻优,利用优化后的VMD将风电功率分解为一组平稳子分量。然后,结合径向基核函数与多项式核函数优点组成混合核函数,对各分量结合气象特征分别建立兼顾学习与泛化能力的HKELM预测模型,并使用SSA对模型参数进行寻优,以充分发挥模型性能。最后,将各分量预测值叠加,得到最终预测结果。以中国内蒙古某风电场实际数据进行仿真实验,结果表明,该方法相比于其他预测方法具有更高的预测精度。

In this study,we propose a short-term wind power prediction method combining variational mode decomposition(VMD)and hybrid kernel extreme learning machine(HKELM)in order to improve the prediction accuracy of wind power.Sparrow search algorithm(SSA)is used to optimize the key parameters in VMD and HKELM such that it addresses the difficulty in tuning parameters and achieves adaptive selection of the parameters of the two algorithms.Firstly,the SSA is designed to optimize the fitness function of the VMD based on three signal decomposition indexes.The optimized VMD is then used to decompose the wind power into a set of smooth subcomponents.Then,a hybrid kernel function is formed by combining the radial basis kernel function and polynomial kernel function.An HKELM prediction model with learning and generalization capabilities is built for each component combined with meteorological features,and SSA is used to optimize the model parameters to fully utilize the model performance.Finally,the final prediction results are obtained by superimposing the predicted values of each component.Our simulation experiments used actual data from a wind farm in Inner Mongolia,China,and the results show that the proposed method has higher prediction accuracy than other prediction methods.