潘超;王典;蔡国伟;杨雨晴;于凤娇;

• 1:现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学)

摘要(Abstract)：

为了提高大型风场风功率预测的精度和效率,构建动物种群仿生算法分别嵌入空间提取及多步预测双模块的组合预测模型。对于风速特征提取模块,利用基于动物寻巢行为的仿生算法嵌套卷积神经网络。优选典型风机位置信息,结合典型风机数据重构空间风速矩阵,通过降低输入数据复杂度以提高提取效率。利用卷积神经网络提取重构矩阵特征信息,实现风速特征信息降维。针对多步预测模块参数设置问题,采用基于动物繁衍行为的寻优算法优化支持向量回归模型参数,结合空间特征信息对风速进行多步预测,将所得风速预测值通过等效风能利用系数法进行风功率预测。最后,将文中方法应用于实际风场的风速及风功率预测,通过对比分析验证所提方法。

关键词(KeyWords)： 风速预测;时空相关性;卷积神经网络;支持向量回归

Abstract:

Keywords:

基金项目(Foundation): 国家重点研发计划重点专项(2016YFB0900100);; 国家自然科学基金项目(51507028)~~

作者(Author): 潘超;王典;蔡国伟;杨雨晴;于凤娇;

Email:

DOI: 10.13335/j.1000-3673.pst.2019.2627

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