Suzhou Electric Appliance Research Institute
期刊号: CN32-1800/TM| ISSN1007-3175

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基于VMD-IGWO-SVM的风电功率超短期预测研究

来源:电工电气发布时间:2019-01-21 14:21 浏览次数:666
基于VMD-IGWO-SVM的风电功率超短期预测研究
 
沈岳峰,都洪基
(南京理工大学 自动化学院,江苏 南京 210094)
 
    摘 要:为了提高风电功率预测精度,保证风能的有效利用,提出一种基于变分模态分解和改进灰狼算法优化支持向量机的风电功率超短期组合预测模型。采用变分模态分解将风电功率序列分解为一系列具有不同中心频率的模态分量以降低其随机性,将各分量分别建立支持向量机预测模型,并采用改进灰狼算法对其参数寻优,将各分量的预测值叠加重构得到最终的预测值。实例仿真表明,所提的组合预测模型与其他预测模型相比具有更高的预测精度。
    关键词:风电功率超短期预测;变分模态分解;改进灰狼算法;支持向量机;预测精度
    中图分类号:TM715     文献标识码:A     文章编号:1007-3175(2019)01-0020-06
 
Research on Ultra-Short-Term Wind Power Prediction Based on VMD-IGWO-SVM
 
SHEN Yue-feng, DU Hong-ji
(School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China)
 
    Abstract: In order to improve the accuracy of wind power prediction and to ensure the effective utilization of wind energy, this paper proposed a combined model based on VMD and SVM optimized by IGWO for ultra-short-term wind power prediction. VMD was used to decompose the wind power series into a series of modal components with different central frequencies to reduce its randomness. The SVM prediction model was established for each component and its parameters were optimized by IGWO. The predicted value of each component was superimposed to get the final predicted value.Simulation results show that compared with other prediction models, the proposed combination prediction model has higher prediction accuracy.
    Key words: ultra-short-term wind power prediction; variational mode decomposition; improved grey wolf optimizer; support vector machine; prediction accuracy
 
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