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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, 2021, 48(x): 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, 2021, 48(x): 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, 2021, 48(x): 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, 2021, 48(x): 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: 国家自然科学基金项目(52172350, 51775565)深圳市科技计划资助(RCBS20200714114920122)的资助
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年获得新加坡洋理工大学博士学位. 主要研究方向包括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. 主要研究方向包括智能车辆与辅助驾驶、新能源汽车等. 本文通讯作者. 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. 主要研究方向包括新能源汽车和动力系统控制等. E-mail: fenggd6@mail.sysu.edu.cn

    ManandharUjjal:新加坡南洋理工大学博士后. 2019年获得南洋理工大学博士学位. 研究方向包括微电网, 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 P. R. China (52172350, 51775565), Shenzhen Science and Technology Program (RCBS20200714114920122)
More Information
    Author Bio:

    WANG Ben-Fei Associate Professor of School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, China. He received the PhD degree from School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, in 2017. His research interests include advanced control for power electronics, microgrids and electric vehicles

    ZHANG Rong-Hui Associate Professor of School of Intelligent Systems Engineering, Sun Yat-sen University, China. He received the PhD degree from Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, China, in 2009. His research interests include intelligent vehicle, ADAS and new energy vehicles. Corresponding author of this paper

    FENG Guo-Dong Associate Professor of School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, China. He received the PhD degree from Sun Yat-sen University, China, in 2015. His research interests include new energy vehicles and electric power train control

    MANANDHAR Ujjal Research Fellow of Nanyang Technological University, Singapore. He received the PhD degree from School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, in 2019. His research interests include 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, moving target detection and tracking with network

  • 摘要: 本文针对光伏-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, 提出了一种基于事件触发的无差拍预测控制(Event-triggered deadbeat predictive control, ETDPC)方法, 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, 该方法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, 基于电池-超级电容器混合储能系统, ETDPC控制能够缓解间歇性光伏发电同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, 相较于传统事件触发无差拍控制方法, 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.
    1)  收稿日期 2021-06-28 录用日期 2021-11-02 Manuscript received June 28, 2021; accepted November 2, 2021 国家自然科学基金项目 (52172350, 51775565) 深圳市科技计划资助 (RCBS20200714114920122) 的资助 Supported by National Natural Science Foundation of P. R. China (52172350, 51775565), Shenzhen Science and Technology Program (RCBS20200714114920122) 本文责任编委 梅生伟 Recommended by Associate Editor MEI Sheng-Wei 1. 中山大学 深圳 智能工程学院 深圳 518000 2. 中山大学 广
    2)  东省智能交通系统重点实验室 广州 510275 3. 南洋理工大学 新加坡 308232 4. 东北大学 流程工业综合自动化国家重点实验室沈阳 110004 1. School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shen Zhen, 518000 2. Guangdong Provincial Key Laboratory of Intelligent Transport System, Sun Yat-sen University, Guangzhou 510275 3. Nanyang Technological University, Singapore, 3082324 4. State Key Laboratory of Synthetical Automation for Industrial Process, Northeastern University, Shenyang 110004
  • 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 ETDB-based energy management strategy for microgrid

    图  3  事件触发无差拍控制框图

    Fig.  3  Diagram of ETDB method

    图  4  光伏和负载跳变时微电网仿真波形,包括vBus, iR, ipv, ibatisc

    Fig.  4  The simulation results of microgrid under step changes of PV and load, including the waveforms of vBus, iR, ipv, ibat and isc

    图  5  光伏和负载跳变时电池与超级电容电流ibatisc仿真波形及其对应参考值波形ibat, refisc, ref

    Fig.  5  The simulation results of ibat and isc, and the corresponding reference ibat, ref and isc, 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vBus, iR, ipv, ibatisc

    Fig.  6  The simulation results of microgrid under step change of vbus, ref, including the waveforms of vBus, iR, ipv, ibat and isc

    图  7  电压目标值跳变时时电池与超级电容电流ibatisc仿真波形及其对应参考值波形ibat, refisc, ref

    Fig.  7  The simulation results of ibat and isc, and the corresponding reference ibat, ref and isc, ref respectively under step change of vbus, ref

    图  8  混合储能系统电流ih波形以及观测器所得观测电流iob波形对比

    Fig.  8  The comparison between the current ih and the observed current iob

    图  9  传统无差拍与事件触发无差拍控制信号对比

    Fig.  9  The comparison of 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: vbusiRipvibatisc

    Fig.  12  The HIL experimental waveforms of ETDPC method: vbusiRipvibat and isc

    图  13  传统DPC硬件在环波形: vbusiRipvibatisc

    Fig.  13  The HIL experimental waveforms of DPC method: vbusiRipvibat and isc

    图  14  基于ETDPC硬件在环功率波形:ppvpbatpscpR

    Fig.  14  The HIL experimental power waveforms of ETDPC method: ppvpbatpsc and pR

    图  15  传统DPC硬件在环功率波形: ppvpbatpscpR

    Fig.  15  The HIL experimental power waveforms of DPC method: ppvpbatpsc and pR

    表  1  仿真参数表

    Table  1  Parameters for the simulation studies

    参数 数值
    双向半桥变换器 vbus 300 V
    C 4700 μF
    L (Lb, Lsc) 47 mH
    混合储能系统 电池 vbat 200 V
    Capacity (容量) 65 Ah
    超级电容 vsc 200 V
    Capacitance (容值) 50 F
    光伏电池单元 voc (开路电压) 30.2 V
    isc (短路电流) 5.0 A
    控制方法时间步长 ts 100 μs
    tet 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) 100 200 300
    执行次数 (万次) DPC 100 200 300
    ETDPC 48.2 98.1 148.2
    时间 (s) 400 500 600
    执行次数 (万次) DPC 400 500 600
    ETDPC 197.8 247.2 297.4
    参数 平均执行次数 (万次/百秒) 纹波(V)
    DPC 100 1.8
    ETDPC 49.37 2.2
    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) 100 200 300 400 500
    执行次数 (万次) DPC 100 200 300 400 500
    ETDPC 57.9 108.1 158.2 207.6 257.2
    参数 平均执行次数 (万次/百秒) 纹波(V)
    DPC 100 1.5
    ETDPC 52.6 2.0
    下载: 导出CSV
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