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基于事件触发的直流微电网无差拍预测控制

王本斐 张荣辉 冯国栋 MANANDHARUjjal 郭戈

王本斐, 张荣辉, 冯国栋, Manandhar Ujjal, 郭戈. 基于事件触发的直流微电网无差拍预测控制. 自动化学报, 2024, 50(3): 1−11 doi: 10.16383/j.aas.c210585
引用本文: 王本斐, 张荣辉, 冯国栋, Manandhar Ujjal, 郭戈. 基于事件触发的直流微电网无差拍预测控制. 自动化学报, 2024, 50(3): 1−11 doi: 10.16383/j.aas.c210585
Wang Ben-Fei, Zhang Rong-Hui, Feng Guo-Dong, Manandhar Ujjal, Guo Ge. Event-triggered deadbeat predictive control for DC microgrid. Acta Automatica Sinica, 2024, 50(3): 1−11 doi: 10.16383/j.aas.c210585
Citation: Wang Ben-Fei, Zhang Rong-Hui, Feng Guo-Dong, Manandhar Ujjal, Guo Ge. Event-triggered deadbeat predictive control for DC microgrid. Acta Automatica Sinica, 2024, 50(3): 1−11 doi: 10.16383/j.aas.c210585

基于事件触发的直流微电网无差拍预测控制

doi: 10.16383/j.aas.c210585
基金项目: 国家自然科学基金(52172350, 51775565), 深圳市科技计划(RCBS20200714114920122)资助
详细信息
    作者简介:

    王本斐:中山大学智能工程学院副教授. 2017年获得新加坡南洋理工大学博士学位. 主要研究方向为电力电子先进控制方法, 微电网和电动汽车. E-mail: wangbf8@mail.sysu.edu.cn

    张荣辉:中山大学智能工程学院副教授. 2009年获得中国科学院长春光学精密机械与物理研究所博士学位. 主要研究方向为智能车辆与辅助驾驶, 新能源汽车. 本文通信作者. E-mail: zhangrh25@mail.sysu.edu.cn

    冯国栋:中山大学智能工程学院副教授. 2015年获得中山大学博士学位. 主要研究方向为新能源汽车和动力系统控制. E-mail: fenggd6@mail.sysu.edu.cn

    MANANDHARUjjal:新加坡南洋理工大学博士后. 2019年获得新加坡南洋理工大学博士学位. 主要研究方向为微电网, 储能系统, 硬件在环平台. E-mail: ujjal001@e.ntu.edu.sg

    郭戈:东北大学教授. 1998年获得东北大学博士学位. 主要研究方向为智能交通系统, 运动目标检测跟踪网络. E-mail: geguo@yeah.net

Event-triggered Deadbeat Predictive Control for DC Microgrid

Funds: Supported by National Natural Science Foundation of China (52172350, 51775565) and Shenzhen Science and Technology Program (RCBS20200714114920122)
More Information
    Author Bio:

    WANG Ben-Fei Associate professor at the School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University. He received his Ph.D. degree in electrical and electronic engineering from Nanyang Technological University, Singapore in 2017. His research interest covers advanced control for power electronics, microgrids and electric vehicles

    ZHANG Rong-Hui Associate professor at the School of Intelligent Systems Engineering, Sun Yat-sen University. He received his Ph.D. degree from Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences in 2009. His research interest covers intelligent vehicle and assisted driving, and new energy vehicles. Corresponding author of this paper

    FENG Guo-Dong Associate professor at the School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University. He received his Ph.D. degree from Sun Yat-sen University in 2015. His research interest covers new energy vehicles and electric power train control

    MANANDHAR Ujjal Research fellow at Nanyang Technological University, Singapore. He received his Ph.D. degree in electrical and electronic engineering from Nanyang Technological University, Singapore in 2019. His research interest covers microgrids, energy storage system, and hardware-in-loop platform

    GUO Ge Professor at Northeastern University. He received his Ph.D. degree from Northeastern University in 1998. His research interest covers intelligent transportation system, and moving target detection and tracking with network

  • 摘要: 针对光伏(Photovoltaic, PV)−电池−超级电容直流微电网系统中光伏发电间歇性造成的功率失配问题, 提出一种基于事件触发的无差拍预测控制(Event-triggered deadbeat predictive control, ETDPC)方法, 以实现有效的能量管理. ETDPC控制方法结合事件触发控制策略和无差拍预测控制策略的优点, 根据微电网的拓扑结构构建状态空间模型, 用于设计适用于微电网能量管理的触发条件: 当ETDPC的触发条件满足时, ETDPC中无差拍预测控制模块被激活, 可以在一个控制周期内产生最优控制信号, 实现对于扰动的快速响应, 减小母线电压纹波; 当系统状态不满足ETDPC中的触发条件时, 无差拍预测控制模块被挂起, 从而消除非必要运算, 以减轻实现能量管理的运算负担. 因此, 对于电池−超级电容器混合储能系统(Hybrid energy storage system, HESS), ETDPC控制能够缓解间歇性光伏发电与负荷需求之间的功率失衡, 以稳定母线电压. 最后, 数字仿真和硬件在环实验结果表明, 相较于传统无差拍控制方法, 运算负担减小了50.63%, 母线电压纹波小于0.73%, 验证了ETDPC控制方法的有效性与性能优势, 为直流微电网的能量管理提供了一种参考.
  • 图  1  微电网系统结构示意图

    Fig.  1  Diagram of the microgrid system

    图  2  基于事件触发无差拍控制的微电网能量管理策略框图

    Fig.  2  Diagram of ETDC-based energy management strategy for microgrid

    图  3  事件触发无差拍控制框图

    Fig.  3  Diagram of ETDC method

    图  4  光伏和负载跳变时微电网仿真波形,包括$v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $和$i_{sc} $

    Fig.  4  The simulation results of microgrid under step changes of PV and load, including the waveforms of $v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $, and $i_{sc} $

    图  5  光伏和负载跳变时电池与超级电容电流$i_{bat} $和$i_{sc} $仿真波形及其对应参考值波形$i_{bat,\;ref} $和$i_{sc,\;ref} $

    Fig.  5  The simulation results of $i_{bat} $ and $i_{sc} $, and the corresponding reference $i_{bat,\;ref} $ and $ i_{sc,\;ref}$ respectively under step changes of PV and load

    图  6  电压目标值跳变时微电网仿真波形,包括$v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $和$i_{sc} $

    Fig.  6  The simulation results of microgrid under step change of $v_{bus,\;ref} $ , including the waveforms of $v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $, and $i_{sc} $

    图  7  电压目标值跳变时时电池与超级电容电流$i_{bat} $和$i_{sc} $仿真波形及其对应参考值波形$i_{bat,\;ref} $和$i_{sc,\;ref} $

    Fig.  7  The simulation results of $i_{bat} $ and $i_{sc} $, and the corresponding reference $i_{bat,\;ref} $ and $i_{sc,\;ref} $ respectively under step change of $i_{bus,\;ref} $

    图  8  混合储能系统电流${i_{h}} $波形以及观测器所得观测电流${i_{ob}} $波形对比

    Fig.  8  The comparison between the current ${i_{h}} $ and the observed current ${i_{ob}} $

    图  9  传统无差拍与事件触发无差拍控制信号对比

    Fig.  9  Comparison of traditional deadbeat and event-triggered deadbeat control signals

    图  10  微电网硬件在环测试平台

    Fig.  10  The HIL test platform for microgrid

    图  11  硬件在环实验采用光照强度曲线

    Fig.  11  The irradiance curve adopted in HIL experiment

    图  12  基于ETDPC硬件在环波形: $v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $和$i_{sc} $

    Fig.  12  The HIL waveforms of ETDPC method: $v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $, and $i_{sc} $

    图  13  基于DPC硬件在环波形: $v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $和$i_{sc} $

    Fig.  13  The HIL waveforms of DPC method: $v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $, and $i_{sc} $

    图  14  基于ETDPC硬件在环功率波形:$P_{pv} $, $P_{bat} $, $P_{sc} $和$P_{R} $

    Fig.  14  The HIL power waveforms of ETDPC method: $P_{pv} $, $P_{bat} $, $P_{sc} $, and $P_{R} $

    图  15  基于DPC硬件在环功率波形: $P_{pv} $, $P_{bat} $, $P_{sc} $和$P_{R} $

    Fig.  15  The HIL power waveforms of DPC method: $P_{pv} $, $P_{bat} $, $P_{sc} $, and $P_{R} $

    表  1  仿真参数表

    Table  1  Parameters for the simulation studies

    类别 参数名称 数值
    双向
    半桥
    变换器
    $v_{bus }$ 300 V
    $C $ 4 700 μF
    $L\,(L_{bat},\;L_{sc})$ 47 mH
    混合储能系统 电池 $v_{bat }$ 200 V
    Capacity (容量) 65 Ah
    超级
    电容
    $v_{sc} $ 200 V
    Capacitance (容值) 50 F
    光伏电池单元 $v_{pv }$ (开路电压) 30.2 V
    $i_{pv} $ (短路电流) 5.0 A
    控制方法时间步长 $t_s $ 100 μs
    $t_{et} $ 100 μs
    下载: 导出CSV

    表  2  运算执行次数统计表

    Table  2  Operation times of simulation studies

    时间 (s)执行次数 (万次)
    DPCETDPC
    100100 48.2
    200200 98.1
    300300148.2
    400400197.8
    500500247.2
    600600297.4
    平均执行次数 (万次/百秒) 10049.37
    纹波(V)1.82.2
    下载: 导出CSV

    表  3  硬件在环运算执行次数统计表

    Table  3  Operation times of the HIL experiments

    时间 (s)执行次数 (万次)
    DPCETDPC
    100100 57.9
    200200108.1
    300300158.2
    400400207.6
    500500257.2
    平均执行次数 (万次/百秒) 10052.6
    纹波(V)1.52.0
    下载: 导出CSV
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