IMPROVED WAVELET PACKET METHOD OF SMOOTHING WIND POWER FLUCTUATIONS BASED ON TIME-FREQUENCY ANALYSIS

Qi Xianjun; Zheng Xiwei; Wang Xiaorong; Han Pingping

doi:10.19912/j.0254-0096.tynxb.2020-1238

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (7) : 302-309. DOI: 10.19912/j.0254-0096.tynxb.2020-1238

IMPROVED WAVELET PACKET METHOD OF SMOOTHING WIND POWER FLUCTUATIONS BASED ON TIME-FREQUENCY ANALYSIS

Qi Xianjun^1,2, Zheng Xiwei¹, Wang Xiaorong², Han Pingping¹

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Abstract

Aiming at excessive smoothing in the traditional wavelet packet method to smooth wind power fluctuations, an improved wavelet packet smoothing method based on time-frequency analysis is proposed. Firstly, the set of time pairs when the maximum power-fluctuation range exceeds the fluctuation standard is found out; and the wavelet decomposition based on wavelet packet is performed on wind power to obtain its components in each frequency band. Secondly, starting from the highest frequency component, the local peak-shaving and valley-filling of the components are performed only at these time pairs until the fluctuation standard is satisfied, and thus the grid-connected wind power and energy storage power command are obtained. Finally, the effect of smoothing is evaluated. Results of the case study show that this method can reduce the cumulative power and the charging-discharging switching times of energy storage power command, thus helping reduce the cost of energy storage configuration and prolong the life of energy storage while meeting the standards of wind power fluctuations.

Key words

wind power / energy storage / wavelet decomposition / time-frequency analysis / wavelet packet / fluctuation smoothing

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Qi Xianjun, Zheng Xiwei, Wang Xiaorong, Han Pingping. IMPROVED WAVELET PACKET METHOD OF SMOOTHING WIND POWER FLUCTUATIONS BASED ON TIME-FREQUENCY ANALYSIS[J]. Acta Energiae Solaris Sinica. 2022, 43(7): 302-309 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1238

References

[1] 姚良忠, 朱凌志, 周明, 等. 高比例可再生能源电力系统的协同优化运行技术展望[J]. 电力系统自动化, 2017, 41(9): 36-43.
YAO L Z, ZHU L Z, ZHOU M, et al.Prospects of coordination and optimization for power system with high proportion of renewable energy[J]. Automation of electric power systems, 2017, 41(9): 36-43.
[2] CHENG L L, ZANG H X, DING T, et al.Ensemble recurrent neural network based probabilistic wind speed forecasting approach[J]. Energies, 2018, 11(8): 1958.
[3] 张丽英, 叶廷路, 辛耀中, 等. 大规模风电接入电网的相关问题及措施[J]. 中国电机工程学报, 2010, 30(25): 1-9.
ZHANG L Y, YE T L, XIN Y Z, et al.Problems and measures of power grid accommodating large scale wind power[J]. Proceedings of the CSEE, 2010, 30(25): 1-9.
[4] 徐国栋, 程浩忠, 马紫峰, 等. 用于平滑风电出力的储能系统运行与配置综述[J]. 电网技术, 2017, 41(11): 3470-3479.
XU G D, CHENG H Z, MA Z F, et al.An overview of operation and configuration of energy storage systems for smoothing wind power outputs[J]. Power system technology, 2017, 41(11): 3470-3479.
[5] 张晴, 李欣然, 杨明. 净效益最大的平抑风电功率波动的混合储能容量配置方法[J]. 电工技术学报, 2016, 31(14): 40-48.
ZHANG Q, LI X R, YANG M.Capacity determination of hybrid energy storage system for smoothing wind power fluctuations with maximum net benefit[J]. Transactions of China Electrotechnical Society, 2016, 31(14): 40-48.
[6] LI X.Fuzzy adaptive Kalman filter for wind power output smoothing with battery energy storage system[J]. IET renewable power generation, 2012, 6(5): 340-347.
[7] 郭金金, 吴红斌. 平抑风电波动的混合储能协调优化控制方法[J]. 太阳能学报, 2016, 37(10): 2695-2702.
GUO J J, WU H B.Optimal coordination control method of hybrid energy storage to smooth wind power fluctuation[J]. Acta energiae solaris sinica, 2016, 37(10): 2695-2702.
[8] 王森, 蔺红. 基于变系数ES的混合储能平抑风电波动控制策略[J]. 太阳能学报, 2019, 40(11): 3204-3212.
WANG S, LIN H.Control strategy of hybrid energy storage to stabilize wind power fluctuation based on variable coefficient exponential smoothing method[J]. Acta energiae solaris sinica, 2019, 40(11): 3204-3212.
[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.
WU J, DING M.Wind power fluctuation smoothing strategy of hybrid energy storage system using self-adaptive wavelet packet decomposition[J]. Automation of electric power systems, 2017, 41(3): 7-12.
[10] 吕超贤, 李欣然, 户龙辉, 等. 基于小波分频与双层模糊控制的多类型储能系统平滑策略[J]. 电力系统自动化, 2015, 39(2): 21-29.
LYU C X, LI X R, HU L H, et al.A smoothing strategy for hybrid energy storage system based on wavelet frequency allocation and two-level fuzzy control[J]. Automation of electric power systems, 2015, 39(2): 21-29.
[11] LIU Y B, ZHU Y W, XU W T, et al.Dynamic wavelet decomposition based multi-objective operation model for HESS enabling wind power output smoothing[J]. Energy procedia, 2017, 142: 1462-1467.
[12] JIANG Q Y, HONG H S.Wavelet-based capacity configuration and coordinated control of hybrid energy storage system for smoothing out wind power fluctuations[J]. IEEE transactions on power systems, 2013, 28(2): 1363-1372.
[13] 丁明, 吴杰, 张晶晶. 面向风电平抑的混合储能系统容量配置方法[J]. 太阳能学报, 2019, 40(3): 593-599.
DING M, WU J, ZHANG J J.Capacity optimization method of hybrid energy storage system for wind power smoothing[J]. Acta energiae solaris sinica, 2019, 40(3): 593-599.
[14] IKEGAMI T, URABE C T, SAITOU T, et al.Numerical definitions of wind power output fluctuations for power system operations[J]. Renewable energy, 2018, 115: 6-15.
[15] GAO R X, YAN R.Non-stationary signal processing for bearing health monitoring[J]. International journal of manufacturing research, 2006, 1(1): 18-40.
[16] 罗鹏, 杨天蒙, 娄素华, 等. 基于频谱分析的微网混合储能容量优化配置[J]. 电网技术, 2016, 40(2): 376-381.
LUO P, YANG T M, LOU S H, et al.Spectrum analysis based capacity configuration of hybrid energy storage in microgrid[J]. Power system technology, 2016, 40(2): 376-381.