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              基于事件觸發的直流微電網無差拍預測控制

              王本斐 張榮輝 馮國棟 ManandharUjjal 郭戈

              王本斐, 張榮輝, 馮國棟, Manandhar Ujjal, 郭戈. 基于事件觸發的直流微電網無差拍預測控制. 自動化學報, 2024, 50(3): 475?485 doi: 10.16383/j.aas.c210585
              引用本文: 王本斐, 張榮輝, 馮國棟, Manandhar Ujjal, 郭戈. 基于事件觸發的直流微電網無差拍預測控制. 自動化學報, 2024, 50(3): 475?485 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): 475?485 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): 475?485 doi: 10.16383/j.aas.c210585

              基于事件觸發的直流微電網無差拍預測控制

              doi: 10.16383/j.aas.c210585
              基金項目: 國家自然科學基金(52172350, 51775565), 深圳市科技計劃(RCBS20200714114920122), 廣州市科技計劃項目(2024B01W0079)資助
              詳細信息
                作者簡介:

                王本斐:中山大學智能工程學院副教授. 2017年獲得新加坡南洋理工大學博士學位. 主要研究方向為電力電子先進控制方法, 微電網和電動汽車. E-mail: wangbf8@mail.sysu.edu.cn

                張榮輝:中山大學智能工程學院副教授. 2009年獲得中國科學院長春光學精密機械與物理研究所博士學位. 主要研究方向為智能車輛與輔助駕駛, 新能源汽車. 本文通信作者. E-mail: zhangrh25@mail.sysu.edu.cn

                馮國棟:中山大學智能工程學院副教授. 2015年獲得中山大學博士學位. 主要研究方向為新能源汽車和電動動力系統控制. E-mail: fenggd6@mail.sysu.edu.cn

                ManandharUjjal:新加坡南洋理工大學博士后. 2019年獲得新加坡南洋理工大學博士學位. 主要研究方向為微電網, 儲能系統, 硬件在環平臺. E-mail: ujjal001@e.ntu.edu.sg

                郭戈:東北大學教授. 1998年獲得東北大學博士學位. 主要研究方向為智能交通系統, 運動目標檢測跟蹤網絡. E-mail: geguo@yeah.net

              • 中圖分類號: Y

              Event-triggered Deadbeat Predictive Control for DC Microgrid

              Funds: Supported by National Natural Science Foundation of China (52172350, 51775565), Shenzhen Science and Technology Program (RCBS20200714114920122), and Guangzhou Science and Technology Plan Project (2024B01W0079)
              More Information
                Author Bio:

                WANG Ben-Fei Associate professor at the School of Intelligent Systems Engineering, Sun Yat-sen University. He received his Ph.D. degree 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, 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 Postdoctor at Nanyang Technological University, Singapore. He received his Ph.D. degree 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: 針對光伏(Photovoltaic, PV)?電池?超級電容直流微電網系統中光伏發電間歇性造成的功率失配問題, 提出一種基于事件觸發的無差拍預測控制(Event-triggered deadbeat predictive control, ETDPC)方法, 以實現有效的能量管理. ETDPC方法結合事件觸發控制策略和無差拍預測控制策略(Deadbeat predictive control, DPC)的優點, 根據微電網的拓撲結構構建狀態空間模型, 用于設計適用于微電網能量管理的觸發條件: 當ETDPC的觸發條件滿足時, ETDPC中無差拍預測控制模塊被激活, 可以在一個控制周期內產生最優控制信號, 實現對于擾動的快速響應, 減小母線電壓紋波; 當系統狀態不滿足ETDPC中的觸發條件時, 無差拍預測控制模塊被掛起, 從而消除非必要運算, 以減輕實現能量管理的運算負擔. 因此, 對于電池?超級電容器混合儲能系統(Hybrid energy storage system, HESS), ETDPC能夠緩解間歇性光伏發電與負荷需求之間的功率失衡, 以穩定母線電壓. 最后, 數字仿真和硬件在環(Hardware-in-loop, HIL)實驗結果表明, 相較于傳統無差拍控制方法, 運算負擔減小了50.63%, 母線電壓紋波小于0.73%, 驗證了ETDPC方法的有效性與性能優勢, 為直流微電網的能量管理提供了一種參考.
              • 圖  1  微電網系統結構示意圖

                Fig.  1  Diagram of the microgrid system

                圖  2  基于事件觸發無差拍控制的微電網能量管理策略框圖

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

                圖  3  事件觸發無差拍控制框圖

                Fig.  3  Diagram of ETDPC method

                圖  4  光伏和負載跳變時微電網仿真波形,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} $

                圖  5  光伏和負載跳變時電池與超級電容電流$i_{bat} $和$i_{sc} $仿真波形及其對應參考值波形$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

                圖  6  $v_{bus,ref} $跳變時微電網仿真結果,包括$v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $和$i_{sc} $波形

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

                圖  7  $i_{bus,ref}$跳變時$i_{bat} $和$i_{sc} $仿真結果及其對應參考值$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 $i_{sc,ref}$ under step changes of $i_{bus,ref}$

                圖  8  電流$i_h $以及觀測所得電流$i_{ob} $對比

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

                圖  9  傳統無差拍與事件觸發無差拍控制信號對比

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

                圖  10  微電網硬件在環測試平臺

                Fig.  10  The HIL test platform for microgrid

                圖  11  硬件在環實驗采用光照強度曲線

                Fig.  11  The irradiance curve adopted in HIL experiment

                圖  12  基于ETDPC硬件在環波形: $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} $

                圖  13  基于DPC硬件在環波形: $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} $

                圖  14  基于ETDPC硬件在環功率波形:$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} $

                圖  15  基于DPC硬件在環功率波形: $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  仿真參數表

                Table  1  Parameters for the simulation studies

                類別 參數名稱 數值
                雙向
                半橋
                變換器
                $v_{bus }$ 300 V
                $C $ 4 700 μF
                $L\,(L_{bat},\;L_{sc})$ 47 mH
                混合儲能系統 電池 $v_{bat }$ 200 V
                Capacity (容量) 65 Ah
                超級
                電容
                $v_{sc} $ 200 V
                Capacitance (容值) 50 F
                光伏電池單元 $v_{pv }$ (開路電壓) 30.2 V
                $i_{pv} $ (短路電流) 5.0 A
                控制方法時間步長 $t_s $ 100 μs
                $t_{et} $ 100 μs
                下載: 導出CSV

                表  2  運算執行次數統計表

                Table  2  Statistics table of the number of operation times

                時間 (s)執行次數 (萬次)
                DPCETDPC
                100100 48.2
                200200 98.1
                300300148.2
                400400197.8
                500500247.2
                600600297.4
                平均執行次數 (萬次/百秒) 10049.37
                紋波(V)1.82.2
                下載: 導出CSV

                表  3  硬件在環運算執行次數統計表

                Table  3  Operation times of the HIL experiments

                時間 (s)執行次數 (萬次)
                DPCETDPC
                100100 57.9
                200200108.1
                300300158.2
                400400207.6
                500500257.2
                平均執行次數 (萬次/百秒) 10052.6
                紋波(V)1.52.0
                下載: 導出CSV
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                      1. [1] 梅生偉, 朱建全. 智能電網中的若干數學與控制科學問題及其展望. 自動化學報, 2013, 39(02): 119-131 doi: 10.1016/S1874-1029(13)60014-2

                        Mei S, Zhu J. Mathematical and control scientiflc issues of smart grid and its prospects. Acta Automatica Sinica, 2013, 39(02): 119-131. doi: 10.1016/S1874-1029(13)60014-2
                        [2] Dragi?evi? T, Lu X, Vasquez J C, Guerrero JM. DC microgrids—Part II: A review of power architectures, applications, and standardization issues. IEEE Transactions on Power Electronics, 2016, 31(5): 3528-3549. doi: 10.1109/TPEL.2015.2464277
                        [3] 王澄, 劉德榮, 魏慶來, 趙冬斌, 夏振超. 有儲能設備的智能電網電能迭代自適應動態規劃最優控制. 自動化學報, 2014, 40(9): 1984-1990

                        Wang C, Liu D, Wei Q, Zhao D, Xia Z. Iterative adaptive dynamic programming approach to power optimal control for smart grid with energy storage devices. Acta Automatica Sinica, 2014, 40(9): 1984-1990.
                        [4] Xu Q et al.. A decentralized dynamic power sharing strategy for hybrid energy storage system in autonomous DC Microgrid. IEEE Transactions on Industrial Electronics, 2017, 64(7): 5930-5941. doi: 10.1109/TIE.2016.2608880
                        [5] 盧自寶, 鐘尚鵬, 郭戈. 基于分布式策略的直流微電網下垂控制器設計. 自動化學報, 2021, 47(10): 2472?2483

                        Lu Zi-Bao, Zhong Shang-Peng, Guo Ge. Design of droop controller for DC microgrid based on distributed strategy. Acta Automatica Sinica, 2021, 47(10): 2472?2483
                        [6] 劉建剛, 楊勝杰. 具有容性負載的直流微電網系統分布式協同控制. 自動化學報, 2020, 46(06): 1283-1290

                        Liu J, Yang S. Distributed cooperative control of DC micro-grid systems with capacitive load. Acta Automatica Sinica, 2020, 46(06): 1283-1290.
                        [7] Baros S, Ili? M D. A consensus approach to real-time distributed control of energy storage systems in wind farms. IEEE Transactions on Smart Grid, 2020, 10(1): 613-625.
                        [8] Wang B. et al.. Consensus-based control of hybrid energy storage system with a cascaded multiport converter in DC microgrids. IEEE Transactions on Sustainable Energy, 2020, 11(4): 2356-2366. doi: 10.1109/TSTE.2019.2956054
                        [9] Teleke S, Baran M E, Bhattacharya S, Huang A Q. Rule-based control of battery energy storage for dispatching intermittent renewable sources. IEEE Transactions on Sustain Energy, 2010, 1(3): 117-124. doi: 10.1109/TSTE.2010.2061880
                        [10] Tummuru N R, Mishra M K, Srinivas S. Dynamic energy management of renewable grid integrated hybrid energy storage system. IEEE Transactions on Industrial Electronics, 2015, 62(12): 7728–7737. doi: 10.1109/TIE.2015.2455063
                        [11] Manandhar U. et al. Energy management and control for grid connected hybrid energy storage system under different operating modes, IEEE Transactions on Smart Grid, 2019, 10(2): 1626-1636. doi: 10.1109/TSG.2017.2773643
                        [12] Xiao J, Wang Pe, Setyawan L. Hierarchical control of hybrid energy storage system in DC microgrids. IEEE Transactions on Industrial Electronics, 2015, 62(8): 4915-4924. doi: 10.1109/TIE.2015.2400419
                        [13] Xiao J, Wang Pe, Setyawan L. Multilevel energy management system for hybridization of energy storages in DC microgrids. IEEE Transactions on Smart Grid, 2016, 7(2): 847-856.
                        [14] Abeywardana D B W, Hredzak B, Agelidis V G. A fixed frequency sliding mode controller for a boost-inverter-based battery supercapacitor hybrid energy storage system. IEEE Transactions on Power Electronics, 2017, 32(1): 668–680. doi: 10.1109/TPEL.2016.2527051
                        [15] Wang B, Xu J, Wai R J, Cao B. Adaptive sliding-mode with hysteresis control strategy for simple multimode hybrid energy storage system in electric vehicles. IEEE Transactions on Industrial Electronics, 2017, 64(2): 1404–1414. doi: 10.1109/TIE.2016.2618778
                        [16] Kouro S, Perez M A, Rodriguez J, Llor A M, Young H A. Model predictive control: MPC's role in the evolution of power electronics. IEEE Industrial Electronics Magazine, 2015, 9(4): 8-21. doi: 10.1109/MIE.2015.2478920
                        [17] Zhang X, Wang B, Manandhar U, Gooi H B, Foo G. A model predictive current controlled bidirectional three-level DC/DC converter for hybrid energy storage system in DC microgrids, IEEE Transactions on Power Electronics, 2019, 34(5): 4025-4030. doi: 10.1109/TPEL.2018.2873765
                        [18] 張彥, 張濤, 王銳, 劉亞杰, 郭波. 基于模型預測控制的含多微電網的能源互聯網分布式協同優化(英文). 自動化學報, 2017, 43(08): 1443-1456

                        Zhang Y, Zhang T, Wang R, Liu Y, Guo B. A model predictive control based distributed coordination of multi-microgrids in energy internet. Acta Automatica Sinica, 2017, 43(8): 1443-1456.
                        [19] Shan Y, Hu J, Chan K W, Fu Q, Guerrero J M. Model predictive control of bidirectional DC-DC converters and AC/DC interlinking converters—a new control method for PV-wind-battery microgrids. IEEE Transactions on Sustainable Energy, 2019, 10 (4): 1823-1833. doi: 10.1109/TSTE.2018.2873390
                        [20] B. Wang, V. R. K. Kanamarlapudi, L. Xian, X. Peng, K. T. Tan and P. L. So. Model predictive voltage control for single-inductor multiple-output DC–DC converter with reduced cross regulation. IEEE Transactions on Industrial Electronics, Jul. 2016, 63(7): 4187-4197. doi: 10.1109/TIE.2016.2532846
                        [21] Wang B, Manandhar U, Zhang X, Gooi H B, Ukil A. Deadbeat control for hybrid energy storage systems in DC microgrids, IEEE Transactions on Sustainable Energy, 2018, 10(4): 1867-1877.
                        [22] Wang B et al.. Event-Triggered Model Predictive Control for Power Converters, IEEE Transactions on Industrial Electronics, 2020, 68(1): 715-720.
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                        • 收稿日期:  2021-06-28
                        • 錄用日期:  2021-11-02
                        • 網絡出版日期:  2021-12-25
                        • 刊出日期:  2024-03-29

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