Robust Model Predictive Current Control of Three-Phase Voltage Source PWM Rectifier With Online Disturbance Observation

Xia, Changliang; Wang, Meng; Song, Zhanfeng; Liu, Tao

doi:10.1109/tii.2012.2187912

Cited by 246 publications

(119 citation statements)

References 35 publications

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“…extended-state observers are used to avoid the effects of system parameter uncertainties [37], [42], [43]. A drawback of the MPC strategies is the exponential increase of the computational burden if the prediction horizon, N, is longer than one and, in the case of FCS-MPC, if the number of switching states to be studied, J, is high.…”

Section: Past Present and Future Challenges Of Mpc For Power Convermentioning

confidence: 99%

Model Predictive Control: A Review of Its Applications in Power Electronics

Vázquez

Leon

Franquelo

et al. 2014

EEE Ind. Electron. Mag.

973

374

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Section: Past Present and Future Challenges Of Mpc For Power Convermentioning

confidence: 99%

Model Predictive Control: A Review of Its Applications in Power Electronics

Vázquez

Leon

Franquelo

et al. 2014

EEE Ind. Electron. Mag.

973

374

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“…Reference [29] shown that the current or power ripple could be affected by the parameter variations, whereas the dynamic response was almost unchanged. In case of extreme variations in the model parameters, an online parameter estimation algorithm should be included in the MPC strategy [30,31]. However, MPC can effectively handle the small change in inductive filter parameters.…”

Section: Discrete-time Model Of Convertermentioning

confidence: 99%

Application of Model Predictive Control to BESS for Microgrid Control

Nguyen¹,

Yoo²,

Kim³

2015

Energies

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Battery energy storage systems (BESSs) have been widely used for microgrid control. Generally, BESS control systems are based on proportional-integral (PI) control techniques with the outer and inner control loops based on PI regulators. Recently, model predictive control (MPC) has attracted attention for application to future energy processing and control systems because it can easily deal with multivariable cases, system constraints, and nonlinearities. This study considers the application of MPC-based BESSs to microgrid control. Two types of MPC are presented in this study: MPC based on predictive power control (PPC) and MPC based on PI control in the outer and predictive current control (PCC) in the inner control loops. In particular, the effective application of MPC for microgrids with multiple BESSs should be considered because of the differences in their control performance. In this study, microgrids with two BESSs based on two MPC techniques are considered as an example. The control performance of the MPC used for the control microgrid is compared to that of the PI control. The proposed control strategy is investigated through simulations using MATLAB/Simulink software. The simulation results show that the response time, power and voltage ripples, and frequency spectrum could be improved significantly by using MPC.

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“…However, even though the desired switching patterns and spectrum distribution can both be achieved, transient performances were deteriorated due to the low-pass filter characteristics possessed by these methods. In [27,28], two active vectors were firstly selected during each sampling interval. An optimal vector sequence was then symmetrically built up on the basis of one zero vector and two selected vectors.…”

Section: Introductionmentioning

confidence: 99%

“…This is quite different from the working principle of finite set model predictive control techniques, in which the optimal switching action is selected and, in particular, remains fixed within each sampling interval once it is determined. Note that when the method proposed in [27,28] is adopted, the predicted application times of each vector element in the optimal sequence might be smaller than zero. In reality, the application times of voltage vectors or switching states should never be negative.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Predictive Current Control of Three-Phase Grid-Tied Reversible Converters with Improved Switching Patterns

et al. 2016

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A predictive current control strategy can realize flexible regulation of three-phase grid-tied converters based on system behaviour prediction and cost function minimization. However, when the predictive current control strategy with conventional switching patterns is adopted, the predicted duration time for voltage vectors turns out to be negative in some cases, especially under the conditions of bidirectional power flows and transient situations, leading to system performance deteriorations. This paper aims to clarify the real reason for this phenomenon under bidirectional power flows, i.e., rectifier mode and inverter mode, and, furthermore, seeks to propose effective solutions. A detailed analysis of instantaneous current variations under different conditions was conducted. An enhanced predictive current control strategy with improved switching patterns was then proposed. An experimental platform was built based on a commercial converter produced by Danfoss, and moreover, relative experiments were carried out, confirming the superiority of the proposed scheme.

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Robust Model Predictive Current Control of Three-Phase Voltage Source PWM Rectifier With Online Disturbance Observation

Cited by 246 publications

References 35 publications

Model Predictive Control: A Review of Its Applications in Power Electronics

Model Predictive Control: A Review of Its Applications in Power Electronics

Application of Model Predictive Control to BESS for Microgrid Control

Enhanced Predictive Current Control of Three-Phase Grid-Tied Reversible Converters with Improved Switching Patterns

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