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ISSN 0254-0096 CN 11-2082/K

太阳能学报 ›› 2022, Vol. 43 ›› Issue (12): 360-367.DOI: 10.19912/j.0254-0096.tynxb.2021-0629

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基于VMD-WPE和SSA-ELM的短期风电功率预测研究

刘栋, 魏霞, 王维庆, 叶家豪   

  1. 新疆大学电气工程学院,乌鲁木齐 830047
  • 收稿日期:2021-06-07 出版日期:2022-12-28 发布日期:2023-06-28
  • 通讯作者: 魏 霞(1977—),女,硕士、副教授,主要从事电网工业大数据方面的研究。30462111@qq.com
  • 基金资助:
    国家自然科学基金(52067020)

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

Liu Dong, Wei Xia, Wang Weiqing, Ye Jiahao   

  1. School of Electrical Engineering, Xinjiang University, Urumqi 830047, China
  • Received:2021-06-07 Online:2022-12-28 Published:2023-06-28

摘要: 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。

关键词: 风电功率预测, 变分模态分解, 加权排列熵, 麻雀算法, 极限学习机

Abstract: 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.

Key words: wind power forecasting, variational mode decomposition, weighted permutation entropy, sparrow search algorithm, extreme learning machine

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