A Dual-vector Model Predictive Control Method With Minimum Current THD

2021 ◽  
pp. 1-1
Author(s):  
Xu Zhang ◽  
Xiang Wu ◽  
Guojun Tan ◽  
Weifeng Zhang ◽  
Qiang Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Vector Model ◽  
Dual Vector

scholarly journals Three-Vector-Based Low Complexity Model Predictive Control for Soft Open Point

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Zhengqi Wang ◽  
Haoyu Zhou ◽  
Qunhai Huo ◽  
Sipeng Hao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Low Complexity ◽  
Sampling Time ◽  
Vector Model ◽  
Dynamic State ◽  
Network Systems ◽  
Voltage Vector ◽  
Open Point

Soft open point (SOP) can improve the flexibility and reliability of power supplies; thus, they are widely used in distribution network systems. Traditional single-vector model predictive control (SV-MPC) can quickly and flexibly control the power and current at both ports of the SOP. However, SV-MPC can only select one voltage vector in a sampling time, producing large current ripples, and power fluctuations. In order to solve the above problems, this paper proposes a three-vector-based low complexity model predictive control (TV-MPC). In the proposed control method, two effective voltage vectors and one zero voltage vector are selected in a sampling time. For the two-port SOP, methods are given to judge the sectors on both sides and select the voltage vectors. Furthermore, the calculation method of the distribution time is proposed as well. Finally, the effectiveness of the proposed method is verified by steady-state and dynamic-state simulation results compared with the SV-MPC.

scholarly journals A Dual-Vector Modulated Model Predictive Control Method for Voltage Source Inverters with a New Duty Cycle Calculation Method

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4200
Author(s):  
Lingzhi Cao ◽  
Yanyan Li ◽  
Xiaoying Li ◽  
Leilei Guo ◽  
Nan Jin ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Duty Cycle ◽  
Control Method ◽  
Voltage Source ◽  
Voltage Source Inverters ◽  
Dual Vector ◽  
Large Current ◽  
Relationship Of

Recently, model predictive control (MPC) methods have been widely used to achieve the control of two-level voltage source inverters due to their superiorities. However, only one of the eight basic voltage vectors is applied in every control cycle in the conventional MPC system, resulting in large current ripples and distortions. To address this issue, a dual-vector modulated MPC method is presented, where two voltage vectors are selected and utilized to control the voltage source inverter in every control cycle. The duty cycle of each voltage vector is figured out according to the hypothesis that it is inversely proportional to the square root of its corresponding cost function value, which is the first contribution of this paper. The effectiveness of this assumption is verified for the first time by a detailed theoretical analysis shown in this paper based on the geometrical relationship of the voltage vectors, which is another contribution of this paper. Moreover, further theoretical analysis shows that the proposed dual-vector modulated MPC method can also be extended to control other types of inverters, such as three-phase four-switch inverters. Detailed experimental results validate the effectiveness of the presented strategy.

scholarly journals Common-Mode Voltage Reduction Algorithm for Photovoltaic Grid-Connected Inverters with Virtual-Vector Model Predictive Control

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2607
Author(s):  
Hui Hwang Goh ◽  
Xinyi Li ◽  
Chee Shen Lim ◽  
Dongdong Zhang ◽  
Wei Dai ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pulse Width Modulation ◽  
Control Method ◽  
Dynamic Control ◽  
Single Stage ◽  
Control Technique ◽  
Vector Model ◽  
Common Mode ◽  
Common Mode Voltage

Model predictive control (MPC) has been proven to offer excellent model-based, highly dynamic control performance in grid converters. The increasingly higher power capacity of a PV inverter has led to the industrial preference of adopting higher DC voltage design at the PV array (e.g., 750–1500 V). With high array voltage, a single stage inverter offers advantages of low component count, simpler topology, and requiring less control tuning effort. However, it is typically entailed with the issue of high common-mode voltage (CMV). This work proposes a virtual-vector model predictive control method equipped with an improved common-mode reduction (CMR) space vector pulse width modulation (SVPWM). The modulation technique essentially subdivides the hexagonal voltage vector space into 18 sub-sectors, that can be split into two groups with different CMV properties. The proposal indirectly increases the DC-bus utilization and extends the overall modulation region with improved CMV. The comparison with the virtual-vector MPC scheme equipped with the conventional SVPWM suggests that the proposed technique can effectively suppress 33.33% of the CMV, and reduce the CMV toggling frequency per fundamental cycle from 6 to either 0 or 2 (depending on which sub-sector group). It is believed that the proposed control technique can help to improve the performance of photovoltaic single-stage inverters.

scholarly journals Model predictive control for autonomous ground vehicles: a review

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Shuyou Yu ◽  
Matthias Hirche ◽  
Yanjun Huang ◽  
Hong Chen ◽  
Frank Allgöwer
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Performance ◽  
Control Method ◽  
Theoretical Background ◽  
Future Research ◽  
Centralized Control ◽  
Ground Vehicles ◽  
Uncertain Environments ◽  
Autonomous Ground Vehicles

AbstractThis paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

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