基于集合经验模态分解的混合储能系统功率分配

doi:10.16628/j.cnki.2095-8188.2020.05.004

电器与能效管理技术 ›› 2020, Vol. 590 ›› Issue (5): 21-27.doi: 10.16628/j.cnki.2095-8188.2020.05.004

基于集合经验模态分解的混合储能系统功率分配

郑熙东, 江修波

福州大学电气工程与自动化学院, 福建福州 350108

收稿日期:2020-01-17 出版日期:2020-05-30 发布日期:2020-05-29
作者简介:郑熙东(1996—),男,硕士研究生,研究方向为风电并网和储能平抑风电波动。江修波(1960—),男,教授,研究方向为电力系统运行及管理、电力设备绝缘诊断等。

Power Allocation of Hybrid Energy Storage System Based on Ensemble Empirical Mode Decomposition

ZHENG Xidong¹, JIANG Xiubo²

College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China

Received:2020-01-17 Online:2020-05-30 Published:2020-05-29

摘要/Abstract

摘要： 基于集合经验模态分解(EEMD)对风电波动进行平抑,提出了采用EEMD方法求解出不同的固有模态函数分量和余量。首先,通过信息熵理论求解出固有模态函数能量熵差值最大的阶次作为一个分界点;接着,通过样本熵理论求解出自我相似度最低的阶次作为另一个分界点,通过两个分界点得到初始的功率分配信号;最后,通过模糊控制优化理论对混合储能系统功率分配进行修正。算例分析表明,所提策略能够自适应实现功率合理分配和并网功率平滑,混合储能系统均工作在荷电状态合理区间,能有效提高系统运行稳定性和使用寿命。

关键词: 集合经验模态分解, 能量熵, 样本熵, 模糊控制, 荷电状态

Abstract: Based on the adaptive ensemble empirical mode decomposition (EEMD),the new method was proposed to reduce the wind power fluctuation.Firstly,the order with the largest difference of the energy entropy of the intrinsic mode function was calculated by the information entropy theory.And then,the order with the lowest self-similarity was calculated by the sample entropy theory.At last,the power distribution of the hybrid energy storage system was modified by the theory of fuzzy control optimization.The results show that the proposed strategy can adaptively realize the reasonable power distribution and grid-connected power smoothing.And the hybrid energy storage system works in the reasonable range of the state of charge,which effectively improves the system stability and service life.

Key words: ensemble empirical mode decomposition (EEMD), energy entropy, sample entropy, fuzzy control, state of charge

中图分类号:

TM731

郑熙东, 江修波. 基于集合经验模态分解的混合储能系统功率分配[J]. 电器与能效管理技术, 2020, 590(5): 21-27.

ZHENG Xidong, JIANG Xiubo. Power Allocation of Hybrid Energy Storage System Based on Ensemble Empirical Mode Decomposition[J]. DIANQI YU NENGXIAO GUANLI JISHU, 2020, 590(5): 21-27.

参考文献

[1] 崔炜,艾斯卡尔.国内外风电技术现状与发展趋势[J].电器与能效管理技术,2019(13):1-5.
[2] 韩志坚,吴光敏,张文斌,等.混合储能在变电站直流系统中的功率分配[J].电器与能效管理技术,2015(22):64-67,72.
[3] 刘卫健,黎淑娟,黄际元,等.储能参与风电并网系统的波动平抑控制研究[J].电器与能效管理技术,2018(16):41-47,66.
[4] ZHAO P,WANG J F,DAI Y P.Capacity allocation of a hybrid energy storage system for power system peak shaving at high wind power penetration level[J]. Renewable Energy,2015,75:541-549.
[5] 孙威,房凯,李颖,等.基于EEMD的微电网混合储能系统规划方法[J].电器与能效管理技术,2018(1):66-74.
[6] LI C,KONG X,WANG X,et al.Short-term wind power forecasting method based on mode decomposition and feature extraction[C]∥2019 22nd International Conference on Electrical Machines and Systems (ICEMS),2019:1-5.
[7] YANG L,ZHANG L.Research on signal detection based on EEMD and energy entropy[C]∥2016 IEEE/OES China Ocean Acoustics (COA),2016:1-4.
[8] 张冠英,曹旺林,李长伟,等.基于集合经验模态分解的串联型故障电弧辨识方法[J].电器与能效管理技术,2016(5):8-12.
[9] 吴杰,丁明.采用自适应小波包分解的混合储能平抑风电波动控制策略[J].电力系统自动化,2017,41(3):7-12.
[10] 韩晓娟,陈跃燕,张浩,等.基于小波包分解的混合储能技术在平抑风电场功率波动中的应用[J].中国电机工程学报,2013,33(19):8-13,24.
[11] FENG X,GOOI H B,CHEN S X.Hybrid energy storage with multimode fuzzy power allocator for PV systems[J].IEEE Transactions on Sustainable Energy,2014,5(2):389-397.
[12] DAS A B,BHUIYAN M I H.Discrimination of focal and non-focal EEG signals using entropy-based features in EEMD and CEEMDAN domains[C]∥2016 9th International Conference on Electrical and Computer Engineering (ICECE),2016:435-438.
[13] 谢超,张建文,李星.基于EMD与模糊控制的混合储能控制策略[J].电测与仪表,2019,56(20):124-129.
[14] 吴振威,蒋小平,马会萌,等.用于混合储能平抑光伏波动的小波包-模糊控制[J].中国电机工程学报,2014,34(3):317-324.
[15] 王洪,卢志涛,王少博,等.基于人工智能的电网用蓄电池健康度评估[J].广东电力,2019,32(4):79-84.
[16] 杨清波,汪娟娟,黄骏翅,等.基于多因素的储能系统剩余电量估计方法[J].广东电力,2018,31(2):32-37.

基于集合经验模态分解的混合储能系统功率分配

Power Allocation of Hybrid Energy Storage System Based on Ensemble Empirical Mode Decomposition

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