基于GWO优化ICEEMDAN分解的混合储能系统功率分配策略*

doi:10.11985/2022.04.027

摘要/Abstract

摘要：

针对风电波动降低电网对其消纳水平的问题，设计了一种采用灰狼算法优化(Grey wolf optimizer，GWO)改进的自适应噪声的完备集成经验模态分解(Improved complete ensemble empirical mode decomposition with adaptive noise，ICEEMDAN)的混合储能系统功率分配策略。首先，以风电并网功率与原始功率的互相关系数，以及经ICEEMDAN分解获得的各固有模态函数(Intrinsic modal function，IMF)样本熵作为适应度函数，采用GWO进行ICEEMDAN算法中参数信噪比μ和高频、低频功率分量分界点k进行寻优。其次，采用ICEEMDAN分解风电功率，将低频IMF信号作为风电并网功率，高频IMF信号作为混合储能系统功率，以各相邻高频IMF信号信息熵为依据，实现混合储能系统功率的一次分配；根据超级电容的荷电状态，利用模糊控制对蓄电池、超级电容器的功率进行修正，实现混合储能系统功率的二次分配。最后，将上述平抑风电波动控制策略同其他风电平抑策略进行对比，验证了所提策略的有效性和优越性。

关键词: 风电, 混合储能, ICEEMDAN, 灰狼优化算法, 模糊控制

Abstract:

Aiming at the problem that wind power fluctuation reduces the consumption level of power grid, a power distribution strategy of hybrid energy storage system is designed, which adopts the GWO(Grey wolf optimizer) to optimize and improve ICEEMDAN(Improved complete ensemble empirical mode decomposition with adaptive noise). Firstly, using the cross-correlation coefficient between the wind power grid-connected power and the original power, and the sample entropy of the intrinsic modal function(IMF) obtained by ICEEMDAN decomposition as the fitness function, GWO is used to calculate the parameter signal-to-noise ratio in the ICEEMDAN algorithm. The sum of μ and the boundary point k of high-frequency and low-frequency power components are optimized. Secondly, the wind power is decomposed by ICEEMDAN, the low-frequency IMF signal is used as the wind power grid-connected power, the high-frequency IMF signal is used as the power of the hybrid energy storage system, and the information entropy of each adjacent high-frequency IMF signal is used as the basis to realize the power of the hybrid energy storage system. According to the state of charge of the super capacitor, the power of the battery and the super capacitor is corrected by fuzzy control to realize the secondary distribution of the power of the hybrid energy storage system. Finally, the above-mentioned wind power fluctuation control strategy is compared with other wind power mitigation strategies to verify the effectiveness and superiority of this method.

Key words: Wind power, hybrid energy storage, ICEEMDAN, grey wolf optimization algorithm, fuzzy control

中图分类号:

TM911

刘勇, 刘大鹏, 穆勇, 李振成, 王顺. 基于GWO优化ICEEMDAN分解的混合储能系统功率分配策略^*[J]. 电气工程学报, 2022, 17(4): 257-267.

LIU Yong, LIU Dapeng, MU Yong, LI Zhencheng, WANG Shun. Power Distribution Strategy of Hybrid Energy Storage System Based on GWO Optimization of ICEEMDAN Decomposition[J]. Journal of Electrical Engineering, 2022, 17(4): 257-267.

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风电场装机容量/MW	1 min有功功率变化最大限值/MW	10 min有功功率变化最大限值/MW
<30	3	10
30~150	装机容量/10	装机容量/3
>150	15	50

ΔSOC_tg	SOC_tg
ΔSOC_tg	NB	NS	NM	PM	PS	PB
VS	VS	VS	VS	VB	VB	VB
S	S	MB	B	VB	VB	VB
M	MS	B	VB	VB	MB	MS
B	MB	VB	VB	B	MB	S
VB	VB	VB	VB	VS	VS	VS

参数	数值
参数	蓄电池	超级电容
充放电效率	0.9	0.95
SOC上下限	[0.15~0.85]	[0.1~0.9]

信号	imf₁	imf₂	imf₃
imf₁	1	0.061 2	0.054
imf₂	0.061 2	1	0.084 6
imf₃	0.054	0.084 6	1