A Graphic Weighting Factor Design Method for Finite Control Set Model Predictive Control of Power Converters

Hu, Linqiang; Lei, Wanjun; Li, Ruixiang; Gao, Ye

doi:10.1109/jestpe.2022.3226765

Cited by 14 publications

(8 citation statements)

References 30 publications

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“…The average switching frequency is calculated by measuring the number of actions of the switches within a certain period of time. This is a common method to evaluate the switching frequency of FCS‐MPC [31,35].…”

Section: Resultsmentioning

confidence: 99%

“…Then, all the predictive results will be taken into the cost function (7) in turn, and the switching state that makes J have the minimum value will be selected and applied to the converter during the whole sampling period. When the next sampling moment, the above process will be repeated [22][23][24][25]. This is the basic principle of traditional FCS-MPC.…”

Section: Fcs-mpc Of Chb Convertermentioning

confidence: 99%

“…This is because when the number of SM of the converter increases, the calculation of FCS‐MPC increases exponentially [31–33]. Besides, with multiple control objectives, the design of weighting factors of FCS‐MPC is also a tricky problem [34,35]. To solve the above problems, this paper proposes an improved sequential MPC (IS‐MPC) control method, which can significantly reduce the computational burden of traditional FCS‐MPC and make full use of the degrees of freedom.…”

Section: Introductionmentioning

confidence: 99%

“…For the above traditional linear control strategies, however, there are too many control parameters that need to be designed, and complex modulating techniques are needed, which brings too many control difficulties [22,23]. Compared with the PI controller-based linear control strategy, finite control set model predictive control (FCS-MPC) is a more simple and effective way to cope with the multiobjective optimization problem of power converters and has been applied more and more in recent years [24,25]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved Sequential Model Predictive Control of Cascaded H‐Bridge Multilevel Converter

Qin,

Kuang,

Jiang

et al. 2024

IEEJ Transactions Elec Engng

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The cascaded H‐bridge (CHB) converter can achieve rich functions, such as active rectification, active power filtering, distributed energy storage, etc. The control of the CHB converter is a multiobjective optimization problem. Finite control set model predictive control (FCS‐MPC) is an effective method developed in recent years to solve this problem. However, because of the large amount of calculation, traditional FCS‐MPC cannot be directly used to control the multilevel CHB converter. It is also difficult to tune the weighting factors in traditional FCS‐MPC. To solve these problems, this paper proposes an improved sequential MPC (IS‐MPC) method for the CHB converter. The proposed method divides the control objectives of the CHB converter into different layers according to their priorities and the control degrees of freedom of each layer can be adjusted according to the system state. Simulation and hardware‐in‐the‐loop (HIL) implementation on a three‐phase 7‐level CHB converter shows that the proposed method can effectively control the ac current and submodule voltage of the CHB converter and reduce the switching loss. Moreover, it significantly reduced the calculation burden of traditional FCS‐MPC, and the use of the weighting factors is also avoided. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Section: Resultsmentioning

confidence: 99%

Section: Fcs-mpc Of Chb Convertermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Sequential Model Predictive Control of Cascaded H‐Bridge Multilevel Converter

Qin,

Kuang,

Jiang

et al. 2024

IEEJ Transactions Elec Engng

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show abstract

“…Meta-heuristic techniques such as simulated annealing (SA) based tuning technique is proposed in 62 . However, online computing of weighting factor by such methods limits the search accuracy and increases the computational complexity 47 . Other meta-heuristic methods include gravitational search algorithm (GSA) 43 , non-dominated sorting genetic algorithm II (NSGA-II) and Particle Swarm Optimization (PSO) [10][11][12] .…”

mentioning

confidence: 99%

Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for electric drive applications

Shahid,

Jin,

Abbasi

et al. 2024

Sci Rep

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In the conventional finite control set model predictive torque control, the cost function consists of different control objectives with varying units of measurements. Due to presence of diverse variables in cost function, weighting factors are used to set the relative importance of these objectives. However, selection of these weighting factors in predictive control of electric drives and power converters still remains an open research challenge. Improper selection of weighting factors can lead to deterioration of the controller performance. This work proposes a novel weighting factor tuning method based on the Multi-Criteria-Decision-Making (MCDM) technique called the Entropy method. This technique has several advantages for multi-objective problem optimization. It provides a quantitive approach and incorporates uncertainties and adaptability to assess the relative importance of different criteria or objectives. This technique performs the online tuning of the weighting factor by forming a data set of the control objectives, i.e., electromagnetic torque and stator flux magnitude. After obtaining the error set of control variables, the objective matrix is normalized, and the entropy technique is applied to design the corresponding weights. An experimental setup based on the dSpace dS1104 controller is used to validate the effectiveness of the proposed method for a two-level, three-phase voltage source inverter (2L-3P) fed induction motor drive. The dynamic response of the proposed technique is compared with the previously proposed MCDM-based weighting factor tuning technique and conventional MPTC. The results reveal that the proposed method provides an improved dynamic response of the drive under changing operating conditions with a reduction of 28% in computational burden and 38% in total harmonic distortion, respectively.

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A new model predictive current control strategy for PMSM based on extended control set and dynamic cost function

Pan,

Gu,

Wang

et al. 2024

Electr Eng

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A Graphic Weighting Factor Design Method for Finite Control Set Model Predictive Control of Power Converters

Cited by 14 publications

References 30 publications

Improved Sequential Model Predictive Control of Cascaded H‐Bridge Multilevel Converter

Improved Sequential Model Predictive Control of Cascaded H‐Bridge Multilevel Converter

Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for electric drive applications

A new model predictive current control strategy for PMSM based on extended control set and dynamic cost function

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