摘要
为解决风电场并网时的功率波动影响电网稳定性的问题,提出一种基于北方苍鹰(northern goshawk optimization,NGO)算法优化变分模态分解(variational mode decomposition,VMD)参数的混合储能功率分配策略。首先,按照风电场并网技术规范,采用自适应平均滤波法对风力发电功率进行滤波,并由滤波后的并网功率计算出波动功率。然后,采用NGO优化VMD算法中分解模态数K值和二次惩罚因子α值的最优值组合,将波动功率信号经VMD分解后实现在锂电池和超级电容器的功率分配,最后,采用双重模糊控制对混合储能系统(hybrid energy storage system,HESS)的荷电状态(state of charge,SOC)进行优化,完成HESS功率的二次分配。仿真结果表明,该控制策略不仅能够满足风电并网最大功率波动要求,还可以保持SOC维持在合理范围,实现HESS长期安全运行。
Power fluctuations during wind power grid connection affect the stability of the power grid.To address this issue,a hybrid energy storage power allocation strategy based on the northern goshawk optimization(NGO) algorithm was proposed to optimize the parameters of variational mode decomposition(VMD).Firstly,the generation power of wind power was filtered in accordance with the regulations of wind power grid connection technology using the adaptive averaging filtering method,and the fluctuation power was calculated from the filtered power.Then,the optimal combination of K value(number of decomposition modes) and α value(quadratic penalty factor) in the NGO-VMD algorithm was used to realize power allocation between lithium batteries and supercapacitors after the fluctuation power signal was decomposed by VMD.Finally,the state of charge(SOC) of the hybrid energy storage system(HESS) was optimized using dual fuzzy control to achieve the secondary power allocation of the HESS.Simulation outcomes demonstrate that employing this control strategy achieves not only compliance with the maximum power fluctuation requirements of wind power grid connection but also the maintenance of SOC within a reasonable range,ensuring the long-term secure operation of HESS.
作者
王海燕
钱林宇
WANG Haiyan;QIAN Linyu(College of Automation Engineering,Shanghai University of Electric Power,Shanghai 200090,China)
出处
《中国电力》
CSCD
北大核心
2024年第11期119-128,共10页
Electric Power
基金
上海市电站自动化技术重点实验室开放课题(13DZ2273800)。
关键词
风电并网
北方苍鹰算法
变分模态分解
混合储能
模糊控制
wind power grid connection
northern goshawk optimization
variational mode decomposition
hybrid energy storage
fuzzy control