A Robust Deadbeat Finite Set Model Predictive Current Control Based on Discrete Space Vector Modulation for a Grid-Connected Voltage Source Inverter

Moon, Hyun-Cheol; Lee, June-Seok; Lee, Kyo‐Beum

doi:10.1109/tec.2018.2830776

Cited by 64 publications

(30 citation statements)

References 30 publications

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“…However, increasing the number of virtual VVs can improve the estimation accuracy and reduce the flux and torque ripples. Hence, an alternative DSVM strategy can be accomplished using the method outlined in [23], in which the SVD comprises concentric hexagonal diagrams (CHDs). Each CHD contains VVs of different sizes, and the number of VVs are expressed as follows: (11), and (c) synthesized VVs using (13).…”

Section: Synthesis Of Virtual Vvsmentioning

confidence: 99%

“…Nevertheless, enumerating all the VVs increases the calculation load significantly, which renders it unsuitable for real applications. The calculation of a reference VV for deadbeat predictive torque control (DB-PTC) with the DSVM strategy has been introduced in [22,23]. Although, good performance is achieved in terms of flux and torque ripple reduction, this method requires a complex derivation for the reference VV, which is highly parameter dependent.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Torque Control Based on Discrete Space Vector Modulation of PMSM without Flux Error-Sign and Voltage-Vector Lookup Table

Alsofyani¹,

Lee²

2020

Electronics

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The conventional finite set–predictive torque control of permanent magnet synchronous motors (PMSMs) suffers from large flux and torque ripples, as well as high current harmonic distortions. Introducing the discrete space vector modulation (DSVM) into the predictive torque control (PTC-DSVM) can improve its steady-state performance; however, the control complexity is further increased owing to the large voltage–vector lookup table that increases the burden of memory. A simplified PTC-DSVM with 73 synthesized voltage vectors (VVs) is proposed herein, for further improving the steady-state performance of the PMSM drives with a significantly lower complexity and without requiring a VV lookup table. The proposed scheme for reducing the computation burden is designed to select an optimal zone of space vector diagram (SVD) in the utilized DSVM based on the torque demand. Hence, only 10 out of 73 admissible VVs will be initiated online upon the optimal SVD zone selection. Additionally, with the proposed algorithm, no flux error is required to control the flux demand. The proposed PTC-DSVM exhibits high performance features, such as low complexity with less memory utilization, reduced torque and flux ripples, and less redundant VVs in the prediction process. The simulation and experimental results for the 11 kW PMSM drive are presented to prove the effectiveness of the proposed control strategy.

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Section: Synthesis Of Virtual Vvsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictive Torque Control Based on Discrete Space Vector Modulation of PMSM without Flux Error-Sign and Voltage-Vector Lookup Table

Alsofyani¹,

Lee²

2020

Electronics

Self Cite

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“…with i za , i zb , i zc given by (13). As a result, we can compensate for the dead-time effect by considering its impact with the modified predictive voltage and current in (12), (13), and (14).…”

Section: Average Voltage and Current Compensations Of Prediction Mmentioning

confidence: 99%

Lyapunov-Induced Model Predictive Power Control for Grid-Tie Three-Level Neutral-Point-Clamped Inverter With Dead-Time Compensation

Ngo

Pham

et al. 2019

IEEE Access

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In this paper, a new power control strategy based on a model predictive power control for the grid-tie three-level neutral point clamped inverter is presented. A dynamical model based on the orientation of grid voltage is used to predict the performance of control variables required for the system. A cost function that includes the tracking power ability, the neutral-point voltage balancing and switching frequency reduction is used to achieve the optimal switching state. A proposed selection scheme of control input is introduced to comprehend the stability of the closed-loop system and reduce the computational cost. At each sampling time, only candidate switching state inputs that guarantee the stability condition through a control Lyapunov function is evaluated in the cost function of the MPC algorithm. Thus, the execution time is remarkably decreased by 26% in comparison with traditional model predictive control. Moreover, the deadtime effect is compensated by incorporating its influence in the proposed prediction model. Simulation and experimental results compared with the traditional model predictive control are used to confirm the effectiveness of the proposed scheme. INDEX TERMS Computational burden, control Lyapunov function, dead-time compensation, finite control set model predictive control, stability conditions, three-level neutral-point-clamped inverter. NOMENCLATURE C 1 , C 2 , C dc DC-link capacitance. i g , u g Grid current and voltage. i z Neutral-point current. K d , K q Positive gains of Lyapunov function. L f , R f Filter inductance and resistance. ω Grid voltage angular frequency. θ Grid voltage angle in the stationary frame. λ uz , λ sw Weighting factor of voltage balance, switching frequency reduction. P g , Q g Grid active and reactive powers. The associate editor coordinating the review of this manuscript and approving it for publication was Alfeu J. Sguarezi Filho. S x Switching state of the 3L-NPC inverter. t d , t on , t off Dead-time, turn-on/off time of IGBT. T s Sampling time of the controller. u inv Inverter output voltage. U dc DC-bus voltage. u z Neutral-point voltage. SUPERSCRIPTS * Reference value. p Predicted value. SUBSCRIPTS d, q d, q axis of grid voltage oriented reference frame. α, β α, β axis of stationary reference frame.

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“…In recent years, MPC has been successfully used in grid-tie converters for renewable energy systems, where it delivers improved dynamic performances, robustness and system stability [3][4][5][6][7][8]. In most of existing applications of MPC for power converters, the control command of next period is directly selected from a finite set of switching states according to the cost function, namely finite-control-set MPC (FCS-MPC) [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Model Predictive Control Based Three-Phase Inverter Current Control Method

et al. 2019

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This paper proposes a novel current control method based on Model Predictive Control (MPC) for three-phase inverters. The proposed method is based on an Adaptive MPC (A-MPC) with a PWM modulation. An innovative model parameter estimation and modification method is also proposed, leading to enhanced control accuracy. Comparing with traditional current control methods, such as PI and PR control, the proposed method has better dynamic performance. The transient dynamics, i.e., recovery time and overshoot, have been considerably improved. Simulation and experimental results are presented to validate the effectiveness of the proposal.

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A Robust Deadbeat Finite Set Model Predictive Current Control Based on Discrete Space Vector Modulation for a Grid-Connected Voltage Source Inverter

Cited by 64 publications

References 30 publications

Predictive Torque Control Based on Discrete Space Vector Modulation of PMSM without Flux Error-Sign and Voltage-Vector Lookup Table

Predictive Torque Control Based on Discrete Space Vector Modulation of PMSM without Flux Error-Sign and Voltage-Vector Lookup Table

Lyapunov-Induced Model Predictive Power Control for Grid-Tie Three-Level Neutral-Point-Clamped Inverter With Dead-Time Compensation

A Novel Adaptive Model Predictive Control Based Three-Phase Inverter Current Control Method

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