基于VMD-WPE和SSA-ELM的短期风电功率预测研究

针对风电功率序列非线性、非平稳性特点,提出一种变分模态分解（VMD）-加权排列熵（WPE）和麻雀算法（SSA）优化极限学习机（ELM）的混合风电功率预测模型。首先,采用VMD技术将原始序列分解为多个固有模态分量,再采用WPE技术将各分量重组成若干个复杂度差异较大的子序列。然后,利用启发式SSA算法对ELM的参数进行优化,建立风电功率预测优化模型。最后,采用西北某风电场实际数据对所提模型进行验证。结果表明,与其他模型相比,所提模型提高了预测性能。

SHORT TERM WIND POWER FORECASTING BASED ON VMD-WPE AND SSA-ELM

Aiming at the nonlinear and non-stationary characteristics of wind power series, a hybrid wind power prediction model based on variational mode decomposition （VMD）, weighted permutation entropy （WPE） and sparrow algorithm （SSA）-optimized extreme learning machine （ELM） is proposed. Firstly, the original sequence is decomposed into multiple intrinsic mode components by VMD technology, and then each component is reconstructed into several subsequences with different complexity by WPE technology. Then, a new heuristic SSA algorithm is used to optimize the parameters of ELM, and the wind power prediction optimization model is established. Finally, the actual data of a wind farm in Northwest China is used to verify the proposed model. The results show that the prediction performance of the model is improved compared with other models.