面向风电制氢的超短期组合功率预测  

COMBINED ULTRA-SHORT-TERM POWER PREDICTION FOR WIND POWER HYDROGEN PRODUCTION TECHNOLOGY

作　　者：赵宇洋 赵浩然 谭建鑫[3] 张礽恺 井延伟 孙鹤旭 Zhao Yuyang;Zhao Haoran;Tan Jianxin;Zhang Rengkai;Jing Yanwei;Sun Hexu(School of Electrical Engineering,Hebei University of Science and Technology,Shijiazhuang 050018,China;Hebei Wind Power/Photovoltaic Coupling Hydrogen Production and Comprehensive Utilization Engineering Laboratory,Shijiazhuang 050018,China;HCIG New-Energy Co.,Ltd.,Shijiazhuang 050011,China)

机构地区：[1]河北科技大学电气工程学院,石家庄050018 [2]风电/光伏耦合制氢及综合利用河北省工程实验室,石家庄050018 [3]河北建投新能源有限公司,石家庄050011

出　　处：《太阳能学报》2023年第3期162-168,共7页Acta Energiae Solaris Sinica

基　　金：河北省科技厅重点研发计划(20314501D,19214501D);河北省科技厅引进国外智力项目(2019YX005A);河北省教育厅青年基金(QN2021222)。

摘　　要：为解决因风电随机性带来的“弃风”问题,实现宽功率波动下的高效制氢,提出基于最小二乘支持向量机(LSSVM)的超短期组合预测模型,提高风电功率预测鲁棒性。通过变分模态分解(VMD)预处理将风电功率分解为不同带宽的子模态,以降低随机噪声及模态混叠的影响;引入蜻蜓算法(DA)优化LSSVM,建立超短期组合预测模型,以满足电解槽控制的时间分辨率及精度要求。以河北省某风电制氢示范项目为例,验证该算法对于高波动性数据具备更高的预测精度,为风电制氢系统的优化控制提供依据。A combined ultra-short-term wind power prediction strategy with high robustness based on least squares support vector machine(LSSVM)has been proposed,in order to solve the wind abandonment caused by wind power randomness and realize efficient hydrogen production under wide power fluctuation.Firstly,the original wind power data is decomposed into sub-modes with different bandwidth by variational modal decomposition(VMD),which reduces the influence of random noise and mode mixing significianly.Then dragonfly algorithm(DA)is introduced to optimize LSSVM kernel function and the combined ultra-short-term wind power prediction strategy which meets the time resolution and accuracy requirements of electrolytic cell control has been established finally.This model is validated by a wind power hydrogen production demonstration project output in Hebei Province.The superior prediction accuracy for high volatility wind power data is verified and the algorithm provides theoretical basis to improve the control of wind power hydrogen production system.

关 键 词：风电 风功率预测 制氢 最小二乘支持向量机 变分模态分解 蜻蜓算法 

分 类 号：TM614[电气工程—电力系统及自动化]