基于模糊神经网络的储能侧双向DC-DC变换器自抗扰控制策略

马幼捷; 杨清; 周雪松; 陶珑; 王鸿斌; 赵家欣

doi:10.19912/j.0254-0096.tynxb.2022-0822

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (10) : 488-495. DOI: 10.19912/j.0254-0096.tynxb.2022-0822

基于模糊神经网络的储能侧双向DC-DC变换器自抗扰控制策略

马幼捷¹, 杨清¹, 周雪松¹, 陶珑², 王鸿斌³, 赵家欣⁴

作者信息 +

ACTIVE DISTURBANCE REJECTION CONTROL STRATEGY OF BIDIRECTIONAL DC-DC CONVERTER ON ENERGY STORAGE SIDE BASED ON FUZZY NEURAL NETWORK

Ma Youjie¹, Yang Qing¹, Zhou Xuesong¹, Tao Long², Wang Hongbin³, Zhao Jiaxin⁴

Author information +

文章历史 +

摘要

在直流微网中,为确保储能装置与母线间能量流动的平滑稳定,解决由于工况的不确定性而导致控制效果恶化的问题,在建立双向DC-DC变换器小信号数学模型的基础上,设计一种基于模糊神经网络的双向DC-DC变换器自抗扰控制策略,利用线性扩张状态观测器实现对总扰动的估计补偿,结合模糊神经网络实现控制器参数的在线优化。通过分析对比不同工况中该文所提出的控制策略、线性自抗扰控制与比例积分控制下的直流母线侧电压波形,证明了所提自抗扰控制策略的正确性与有效性。最后,通过参数摄动下的蒙特卡洛实验验证,该控制方法具有较好的鲁棒性。

Abstract

In the DC microgrid, in order to ensure the smooth and stable energy flow between the energy storage device and the busbar, and solve the problem of deteriorating the control effect due to the uncertainty of the working conditions, on the basis of establishing the small signal mathematical model of the bidirectional DC-DC converter, a fuzzy neural network-based active disturbance rejection control strategy for the bidirectional DC-DC converter is designed in this paper. The linear extended state observer is used to estimate and compensate the total disturbance. Combined with fuzzy neural network to achieve online optimization of controller parameters. The control strategy proposed in this paper, linear ADRC control and proportional-integral control of the DC bus side voltage waveform under different working conditions are analyzed and compared, which shows the correctness and effectiveness of the ADRC control strategy proposed in this paper. Finally, Monte Carlo experiments under parameter perturbation show that the control method has good robustness.

导出引用

马幼捷, 杨清, 周雪松, 陶珑, 王鸿斌, 赵家欣. 基于模糊神经网络的储能侧双向DC-DC变换器自抗扰控制策略[J]. 太阳能学报. 2023, 44(10): 488-495 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0822

Ma Youjie, Yang Qing, Zhou Xuesong, Tao Long, Wang Hongbin, Zhao Jiaxin. ACTIVE DISTURBANCE REJECTION CONTROL STRATEGY OF BIDIRECTIONAL DC-DC CONVERTER ON ENERGY STORAGE SIDE BASED ON FUZZY NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2023, 44(10): 488-495 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0822

中图分类号： TM46

参考文献

[1] 王成山, 武震, 李鹏. 微电网关键技术研究[J]. 电工技术学报, 2014, 29(2): 1-12.
WANG C S, WU Z, LI P.Research on key technologies of microgrid[J]. Transactions of China Electrotechnical Society, 2014, 29(2): 1-12.
[2] XIAO W D, OZOG N, DUNFORD W G.Topology study of photovoltaic interface for maximum power point tracking[J]. IEEE transactions on industrial electronics, 2007, 54(3): 1696-1704.
[3] 孙宏斌, 郭庆来, 潘昭光. 能源互联网:理念、架构与前沿展望[J]. 电力系统自动化, 2015, 39(19): 1-8.
SUN H B, GUO Q L, PAN Z G.Energy internet: concept, architecture and frontier outlook[J]. Automation of electric power systems, 2015, 39(19): 1-8.
[4] 李霞林, 郭力, 黄迪, 等. 直流配电网运行控制关键技术研究综述[J]. 高电压技术, 2019, 45(10): 3039-3049.
LI X L, GUO L, HUANG D, et al.Research review on operation and control of DC distribution networks[J]. High voltage engineering, 2019, 45(10): 3039-3049.
[5] 蔡国伟, 陈冲, 孔令国, 等. 风电/制氢/燃料电池/超级电容器混合系统控制策略[J]. 电工技术学报, 2017, 32(17): 84-94.
CAI G W, CHEN C, KONG L G, et al.Control of hybrid system of wind/hydrogen/fuel cell/supercapacitor[J]. Transactions of China Electrotechnical Society, 2017, 32(17): 84-94.
[6] 兰征, 刁伟业, 涂春鸣, 等. 含储能和氢燃料电池的孤岛微电网混合运行模式与功率协调策略研究[J]. 电网技术, 2022, 46(1): 156-164.
LAN Z, DIAO W Y, TU C M, et al.Research on hybrid operation mode and power coordination strategy of island microgrid with energy storage and hydrogen fuel cell[J] . Power system technology, 2022, 46(1): 156-164.
[7] 王成山, 李微, 王议锋, 等. 直流微电网母线电压波动分类及抑制方法综述[J]. 中国电机工程学报, 2017, 37(1): 84-98.
WANG C S, LI W, WANG Y F, et al.DC bus voltage fluctuation classification and restraint methods review for DC microgrid[J]. Proceedings of the CSEE, 2017, 37(1): 84-98.
[8] 梅杨, 陈丽莎, 黄伟超, 等. 级联式双向DC-DC变换器的优化控制方法[J]. 电工技术学报, 2017, 32(19): 153-159.
MEI Y, CHEN L S, HUANG W C, et al.Optimized control method of cascaded Bi-directional DC-DC converters[J]. Transactions of China Electrotechnical Society, 2017, 32(19): 153-159.
[9] AMIN, RIYANTO T B, ARIEF S R, et al. Energy manage- ment of fuel cell/battery/supercapacitor hybrid power sources using model predictive control[J]. IEEE Transactions on industrial informatics, 2014, 10(4): 1992-2002.
[10] 黄炼, 王朴, 武建文, 等. 分布式光-储发电系统的双向 DC-DC非线性控制[J]. 电工技术学报, 2016, 31(S2): 10-18.
HUANG L, WANG P, WU J W, et al.Nonlinear Control of bi-directional DC-DC converter in distributed photovoltaic energy storage system[J]. Transactions of China Electrotechnical Society, 2016, 31(S2): 10-18.
[11] 杨惠, 骆姗, 孙向东, 等. 光伏储能双向DC-DC变换器的自抗扰控制方法研究[J]. 太阳能学报, 2018, 39(5): 1342-1350.
YANG H, LUO S, SUN X D, et al.Research on adrc method for bidirectional DC-DC converter of solar energy storage system[J]. Acta energiae solaris sinica, 2018, 39(5): 1342-1350.
[12] 张国驹, 唐西胜, 周龙, 等. 基于互补PWM控制的 Buck/Boost双向变换器在超级电容器储能中的应用[J]. 中国电机工程学报, 2011, 31(6): 15-21.
ZHANG G J, TANG X S, ZHOU L, et al.Research on complementary PWM controlled Buck/Boost bi-directional converter in supercapacitor energy storage[J]. Proceedings of the CSEE, 2011, 31(6): 15-21.
[13] 陈增强, 刘俊杰, 孙明玮. 一种新型控制方法: 自抗扰控制技术及其工程应用综述[J]. 智能系统学报, 2018, 13(6): 865-877.
CHEN Z Q, LIU J J, SUN M W.Overview of a novel control method: active disturbance rejection control technology and its practical applications[J]. CAAI transactions on intelligent systems, 2018, 13(6): 865-877.
[14] 韩京清. 自抗扰控制器及其应用[J]. 控制与决策, 1998, 13(1): 19-23.
HAN J Q.Auto disturbances rejection controller and its applications[J]. Control and decision, 1998, 13(1): 19-23.
[15] 高志强. 自抗扰控制思想探究[J]. 控制理论与应用, 2013, 30(12): 1498-1510
GAO Z Q.On the foundation of active disturbance rejection control[J]. Control theory & applications, 2013, 30(12): 1498-1510.
[16] CHANG X Y, LI Y L, ZHANG W Y, et al.Active disturbance rejection control for a flywheel energy storage system[J]. IEEE transactions on industrial electronics, 2015, 62(2): 991-1001.
[17] JANG J S R. ANFIS: adaptive-network-based fuzzy inference system[J]. IEEE transactions on systems, man, and cybernetics, 1993, 23(3): 665-685.
[18] BURAGOHAIN M, MAHANTA C.A novel approach for ANFIS modelling based on full factorial design[J]. Applied soft computing, 2008, 8(1): 609-625.
[19] 秦贝贝, 陈增强, 孙明玮, 等. 基于自适应神经模糊推理系统的船舶航向自抗扰控制[J]. 智能系统学报, 2020, 15(2): 255-263.
QIN B B, CHEN Z Q, SUN M W, et al.Active disturbance rejection control of ship course based on adaptive-network- based fuzzy inference system[J]. CAAI transactions on intelligent systems, 2020, 15(2): 255-263.
[20] 唐冲, 童仲志, 侯远龙. 改进型LM-RBF神经网络在自抗扰控制器上的应用[J]. 兵工自动化, 2020, 39(7): 11-15, 28.
TANG C, TONG Z Z, HOU Y L.Application of improved LM-RBF neural network in ADRC[J]. Ordnance industry automation, 2020, 39(7): 11-15, 28.