基于事件觸發(fā)的直流微電網(wǎng)無(wú)差拍預測控制
doi: 10.16383/j.aas.c210585
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中山大學(xué)智能工程學(xué)院 深圳 518000 中國
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中山大學(xué)廣東省智能交通系統重點(diǎn)實(shí)驗室 廣州 510275 中國
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南洋理工大學(xué)電氣與電子工程學(xué)院 新加坡 308232 新加坡
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東北大學(xué)流程工業(yè)綜合自動(dòng)化國家重點(diǎn)實(shí)驗室 沈陽(yáng) 110004 中國
Event-triggered Deadbeat Predictive Control for DC Microgrid
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School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518000, China
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Guangdong Provincial Key Laboratory of Intelligent Transport System, Sun Yat-sen University, Guangzhou 510275, China
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School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 308232, Singapore
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State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110004, China
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摘要: 針對光伏(Photovoltaic, PV)?電池?超級電容直流微電網(wǎng)系統中光伏發(fā)電間歇性造成的功率失配問(wèn)題, 提出一種基于事件觸發(fā)的無(wú)差拍預測控制(Event-triggered deadbeat predictive control, ETDPC)方法, 以實(shí)現有效的能量管理. ETDPC方法結合事件觸發(fā)控制策略和無(wú)差拍預測控制策略(Deadbeat predictive control, DPC)的優(yōu)點(diǎn), 根據微電網(wǎng)的拓撲結構構建狀態(tài)空間模型, 用于設計適用于微電網(wǎng)能量管理的觸發(fā)條件: 當ETDPC的觸發(fā)條件滿(mǎn)足時(shí), ETDPC中無(wú)差拍預測控制模塊被激活, 可以在一個(gè)控制周期內產(chǎn)生最優(yōu)控制信號, 實(shí)現對于擾動(dòng)的快速響應, 減小母線(xiàn)電壓紋波; 當系統狀態(tài)不滿(mǎn)足ETDPC中的觸發(fā)條件時(shí), 無(wú)差拍預測控制模塊被掛起, 從而消除非必要運算, 以減輕實(shí)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(fā)電與負荷需求之間的功率失衡, 以穩定母線(xiàn)電壓. 最后, 數字仿真和硬件在環(huán)(Hardware-in-loop, HIL)實(shí)驗結果表明, 相較于傳統無(wú)差拍控制方法, 運算負擔減小了50.63%, 母線(xiàn)電壓紋波小于0.73%, 驗證了ETDPC方法的有效性與性能優(yōu)勢, 為直流微電網(wǎng)的能量管理提供了一種參考.
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關(guān)鍵詞:
- 微電網(wǎng) /
- 光伏 /
- 混合儲能系統 /
- 事件觸發(fā)控制 /
- 無(wú)差拍預測控制
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 method combines the event-triggered control strategy and the deadbeat predictive control (DPC) 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 be fully utilized for battery-supercapacitor hybrid energy storage system (HESS) to mitigate the power unbalance between the load demand and the intermittent photovoltaic power generation and stabilize the bus voltage. To validate the effectiveness of the method, various simulations and hardware-in-loop (HIL) experiments are conducted based on a digital simulation system and the HIL platform, which show that the computational burden is reduced by 50.63% compared to the 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. -
圖 2 基于事件觸發(fā)無(wú)差拍控制的微電網(wǎng)能量管理策略框圖
Fig. 2 Diagram of ETDPC-based energy management strategy for microgrid
圖 4 光伏和負載跳變時(shí)微電網(wǎng)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(shí)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} $跳變時(shí)微電網(wǎng)仿真結果,包括$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(shí)$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 $以及觀(guān)測所得電流$i_{ob} $對比
Fig. 8 The comparison between the current ${i_{h}} $ and the observed current ${i_{ob}} $
圖 9 傳統無(wú)差拍與事件觸發(fā)無(wú)差拍控制信號對比
Fig. 9 Comparison of traditional deadbeat and event-triggered deadbeat control signals
圖 11 硬件在環(huán)實(shí)驗采用光照強度曲線(xiàn)
Fig. 11 The irradiance curve adopted in HIL experiment
圖 12 基于ETDPC硬件在環(huán)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(huán)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(huán)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(huán)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(lèi)別 參數名稱(chēng) 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(kāi)路電壓) 30.2 V $i_{pv} $ (短路電流) 5.0 A 控制方法時(shí)間步長(cháng) $t_s $ 100 μs $t_{et} $ 100 μs 
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表 2 運算執行次數統計表
Table 2 Statistics table of the number of operation times
時(shí)間 (s) 執行次數 (萬(wàn)次) DPC ETDPC 100 100 48.2 200 200 98.1 300 300 148.2 400 400 197.8 500 500 247.2 600 600 297.4 平均執行次數 (萬(wàn)次/百秒) 100 49.37 紋波(V) 1.8 2.2
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表 3 硬件在環(huán)運算執行次數統計表
Table 3 Operation times of the HIL experiments
時(shí)間 (s) 執行次數 (萬(wàn)次) DPC ETDPC 100 100 57.9 200 200 108.1 300 300 158.2 400 400 207.6 500 500 257.2 平均執行次數 (萬(wàn)次/百秒) 100 52.6 紋波(V) 1.5 2.0
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