摘要
为有效解决风电大规模并网过程中面临的并网难和弃风等问题,实现可再生能源大规模平滑并网并保证大电网的安全稳定运行,采用集成经验模态分解(ensemble empirical mode decomposition,EEMD)和最小二乘双支持向量回归机(least square twin support vector regression,LSTSVR)算法进行风电场风速预测。分别介绍了LSTSVR、EEMD及自适应变异粒子群算法原理。给出基于EEMD和LSTSVR的风速预测流程,以安徽女儿岭风电场测风声雷达30、70 m处风速采样数据为例,开展基于EEMD和LSTSVR的风速预测算法验证,预测结果误差分析表明:基于EEMD+LSTSVR+自适应变异粒子群算法可以实现风电场风速的高精度预测。
In order to effectively solve the problems of grid connection difficulty and wind abandonment in the process of large-scale grid connection of renewable energy,realize large-scale smooth grid connection of renewable energy and ensure the safe and stable operation of large power grid,this paper predicts the wind power of wind farm.based on ensemble empirical mode decomposition and least square twin support vector regression(LSTSVR).The principles of least squares double support vector regression,ensemble empirical mode decomposition and adaptive mutation particle swarm optimization are introduced respectively;the wind speed prediction process based on ensemble empirical mode decomposition and LSTSVR is given;the wind speed at 30 m and 70 m of wind acoustic radar in Anhui Nuerling wind farm is taken as an example to carry out the wind speed prediction based on ensemble empirical mode decomposition and LSTSVR.The error analysis of the prediction results shows that the wind speed of wind farm can be predicted with high accuracy based on the Ensemble Empirical Mode Decomposition + LSTSVR + Adaptive Particle Swarm Optimization algorithm.
作者
谷豪
李山
李文帅
李智敏
许傲然
GU Hao;LI Shan;LI Wenshuai;LI Zhimin;XU Aoran(State Grid Henan Electric Power Company DC Operation&Maintenance Company,Zhengzhou 450000,China;School of Electric Power,Shenyang Institute of Engineering,Shenyang 110136,China)
出处
《供用电》
2022年第1期88-96,共9页
Distribution & Utilization
基金
辽宁省重点研发计划项目“基于新一代信息技术的高精度精细化风能感知与探测系统研发与产业化”(2020JH2/10100036)。
关键词
集成经验模态分解
最小二乘双支持向量回归机
自适应变异粒子群
均方根误差
平均绝对百分比误差
风电预测
ensemble empirical mode decomposition
least squares double support vector regression
adaptive mutation particle swarm optimization
root mean square error
mean absolute percentage error
wind power forecasting
