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

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

王本斐^1,,
张荣辉^{1, 2,},
冯国栋^1,,
ManandharUjjal^3,,
郭戈^4,

1.
中山大学·深圳智能工程学院深圳 518000
2.
中山大学广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

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

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出版历程
- 收稿日期: 2021-06-28
- 录用日期: 2021-11-02
- 网络出版日期: 2021-12-25

Event-Triggered Deadbeat Predictive Control for DC Microgrid

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

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

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.
- 微电网 /
- 光伏 /
- 混合储能 /
- 事件触发控制 /
- 无差拍预测控制
Abstract: This paper presents an event-triggered deadbeat predictive control (ETDPC) method for the mitigation of power mismatch in a photovoltaic(PV)-battery-supercapacitor microgrid. The proposed ETDPC control method combines the event-triggered control strategy and the deadbeat predictive control strategy and inherits their advantages accordingly. Based on the topology of the DC microgrid, the state-space model can be built for the design of the triggering condition for the energy management: when the triggering condition of ETDPC is activated, the deadbeat control block of ETDPC will be conducted and the optimal control signal can be generated within one control cycle, so that the DC bus voltage ripple can be reduced based on the fast response to the disturbance; When the state of the DC microgrid cannot satisfy the triggering condition, the deadbeat control block of ETDPC will be suspended to eliminate the redundant computations, so that the computational burden of the DC microgrid energy management can be reduced. Therefore, ETDPC can fully utilize of battery-supercapacitor hybrid energy storage system to mitigate the power unbalance between load demand the intermittent photovoltaic power generation and stabilize the bus voltage. To validate the effectiveness of the method, various simulation and hardware-in-loop (HIL) experiments are conducted based on a digital simulation system and the HIL platform, which shows that the computational burden is reduced by 50.63% compared to conventional deadbeat predictive control and the voltage ripple is regulated less than 0.73% of the reference. This work provides a reference of the control strategy for microgrid energy management.
- Microgrid /
- photovoltaic /
- energy storage system /
- event-triggered control /
- deadbeat predictive control
注释:

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

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图 1 微电网系统结构示意图

Fig. 1 Diagram of the microgrid system

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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

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图 3 事件触发无差拍控制框图

Fig. 3 Diagram of ETDB method

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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

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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

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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

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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 v_{bus, ref}

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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

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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

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图 10 微电网硬件在环测试平台

Fig. 10 The HIL test platform for microgrid

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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

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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 experimental waveforms of ETDPC method: v_bus、i_R、i_pv、i_bat and i_sc

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图 13 传统DPC硬件在环波形: v_bus、i_R、i_pv、i_bat和i_sc

Fig. 13 The HIL experimental waveforms of DPC method: v_bus、i_R、i_pv、i_bat and i_sc

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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 experimental power waveforms of ETDPC method: p_pv、p_bat、p_sc and p_R

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图 15 传统DPC硬件在环功率波形: p_pv、p_bat、p_sc和p_R

Fig. 15 The HIL experimental power waveforms of DPC method: p_pv、p_bat、p_sc and p_R

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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		v_bus	300 V
		C	4700 μF
		L (L_b, L_sc)	47 mH
混合储能系统	电池	v_bat	200 V
	电池	Capacity (容量)	65 Ah
	超级电容	v_sc	200 V
	超级电容	Capacitance (容值)	50 F
光伏电池单元		v_oc (开路电压)	30.2 V
光伏电池单元		i_sc (短路电流)	5.0 A
控制方法时间步长		t_s	100 μs
控制方法时间步长		t_et	100 μs

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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
执行次数 (万次)	ETDPC	48.2	98.1	148.2
时间 (s)		400	500	600
执行次数 (万次)	DPC	400	500	600
执行次数 (万次)	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

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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
执行次数 (万次)	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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