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基于参数优化变分模态分解的混合储能功率分配策略 被引量:25

Hybrid Energy Storage Strategy Based on Parameter Optimized Variational Mode Decomposition
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摘要 为平滑风电输出功率,通常将功率型储能元件和能量型储能元件结合成混合储能系统与风电系统相连。为了提高混合储能系统的灵活性和经济性,对一种基于参数优化变分模态分解(Variational Mode Decomposition,VMD)的混合储能系统控制策略进行了研究。采用粒子群算法确定VMD算法中K值(分解模态数)和α值(二次惩罚因子)的最优值组合,预设K值和α值将不平衡功率信号经VMD分解后在蓄电池和超级电容之间进行合理分配,最后采用模糊控制对混合储能系统的荷电状态进行优化。仿真结果表明,所提方法既能实现储能元件间合理的功率分配,有效平抑风电波动,又能使荷电状态稳定在一定区间,实现混合储能系统长期安全运行。 To smooth the output of wind power generation the power-type energy storage elements and energy-type energy storage elements are usually combined into a hybrid energy storage system(HESS)and connected to the wind power system.To improve both flexibility and economy of HESS,the control strategy of HESS based on parameter optimized variational mode decomposition(VMD)was researched.Firstly,the particle swarm optimization(PSO)algorithm was utilized to determine the optimal combination of the values of decomposition mode number K and the secondary penalty factorαin the VMD algorithm.Secondly,the values of K andαwere preset to reasonably distribute the unbalanced power signal between battery and super-capacitor by VMD.Finally,the fuzzy control was adopted to optimize the state of charge(SOC)of the hybrid energy storage system.Simulation results show that by use of the proposed control strategy the reasonable power distribution between energy storage elements can be realized,the fluctuation of wind power output can be effectively suppressed and the SOC of the hybrid energy storage system can be stabilized in a certain interval to make the long-term secure operation of the hybrid energy storage system come true.
作者 杜佳耘 雷勇 李永凯 刘晖 DU Jiayun;LEI Yong;LI Yongkai;LIU Hui(College of Electrical Engineering,Sichuan University,Chengdu 610065,Sichuan Province,China;State Grid Shandong Electric Power Company Heze Power Supply Company,Heze 274000,Shandong Province,China)
出处 《现代电力》 北大核心 2021年第1期51-59,共9页 Modern Electric Power
关键词 变分模态分解 混合储能 粒子群算法 模糊控制 荷电状态 variational modal decomposition hybrid energy storage particle swarm optimization fuzzy control state of charge
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