Model Predictive Control of Power Converters for Robust and Fast Operation of AC Microgrids

Dragičević, Tomislav

doi:10.1109/tpel.2017.2744986

Cited by 274 publications

(221 citation statements)

References 42 publications

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“…Therefore, the regulator should track both the voltage reference and its derivative as described mathematically in (22). Further details can be found in [13,15].…”

Section: B Improved Model Predictive Control Algorithmmentioning

confidence: 99%

Performance assessment of the VSC using two model predictive control schemes

Alhasheem

Abdelhakim

Dragičević

et al. 2018

2018 IEEE Applied Power Electronics Conference and Exposition (APEC)

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Finite control set model predictive control (FCS-MPC) methods in different power electronics application are gaining high attention due to their simplicity and fast dynamics. This paper introduces an experimental assessment of the two-level three-phase voltage source converter (2L-VSC) using two FCS-MPC algorithms. In order to perform such comparative evaluation, the 2L-VSC efficiency and total harmonics distortion voltage (THDv) have been measured where considering a linear load and non-linear load. The new algorithm gives better results than the conventional algorithm in terms of the THD and 2L-VSC efficiency. The results also demonstrate the performance of the system using carrier based pulse width modulation (CB-PWM). These findings have been validated for both linear and non-linear loads through experimental verification on 4 kW 2L-VSC prototype. It can be concluded that a comparable performance is achieved by using the conventional FCS-MPC, Improved FCS-MPC, and CB-PWM algorithms.

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“…Therefore, the regulator should track both the voltage reference and its derivative as described mathematically in (22). Further details can be found in [13,15].…”

Section: B Improved Model Predictive Control Algorithmmentioning

confidence: 99%

Performance assessment of the VSC using two model predictive control schemes

Alhasheem

Abdelhakim

Dragičević

et al. 2018

2018 IEEE Applied Power Electronics Conference and Exposition (APEC)

View full text Add to dashboard Cite

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“…3) The proposed strategy exhibits very low computational cost compared to [34], [35], with much faster dynamic performance and significantly improved steady-state performance compared to conventional methods.…”

Section: Introductionmentioning

confidence: 97%

“…However, this results in a significant increase in computational cost. To mitigate and tackle this problem, an improvement of the finite-set FS-MPC strategy, using only a single step prediction horizon, is introduced in [35]. This improvement is mainly based on defining a new cost function, which not only tracks the voltage reference but it also simultaneously tracks its derivative.…”

Section: Introductionmentioning

confidence: 99%

A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output $LC$ Filter

et al. 2019

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Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output LC filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Although it is an intuitive controller, easy to understand and implement, it has the significant disadvantage of requiring a large number of online calculations for solving the optimization problem. On the other hand, the application of model-free approaches such as those based on artificial neural networks approaches is currently growing rapidly in the area of power electronics and drives. This paper presents a new control scheme for a two-level converter based on combining MPC and feed-forward ANN, with the aim of getting lower THD and improving the steady and dynamic performance of the system for different types of loads. First, MPC is used, as an expert, in the training phase to generate data required for training the proposed neural network. Then, once the neural network is fine-tuned, it can be successfully used online for voltage tracking purpose, without the need of using MPC. The proposed ANN-based control strategy is validated through simulation, using MATLAB/Simulink tools, taking into account different loads conditions. Moreover, the performance of the ANN-based controller is evaluated, on several samples of linear and non-linear loads under various operating conditions, and compared to that of MPC, demonstrating the excellent steady-state and dynamic performance of the proposed ANNbased control strategy.Index Terms-Three-phase inverter, model predictive control, artificial neural network, UPS systems.

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“…Cheng et al designed model predictive control for DC-DC boost converters with reduced-prediction horizon and constant switching frequency [25]. Model predictive control of power converters for robust and fast operation of ac microgrids was analysed by Dragičević [26]. Sajadian et al, Panda et al, and Ouammi et al also reported about MPC application in islanded and grid-connected condition of microgrid [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

“…Despite many developments in MPC, some problems are still exist. New theoretical methods have proven that MPC is better than many other control strategies with an intermediate computational complexity [24][25][26][27][28][29]. The non-linear dynamics were approximated as a piecewise linear (INTEGRAL CONSTANT) 13.62 model [30], and then the MPC is applied.…”

Section: Introductionmentioning

confidence: 99%

A Computational Analysis of Interfacing Converters with Advanced Control Methodologies for Microgrid Application

Panda

Ghosh

2020

Technol Econ Smart Grids Sustain Energy

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Power electronics converter and inverter systems for microgrid operations are always stochastic. When the system is operating in grid-tied mode, the attached load is not constant, so the current which drawn from the DC grid also varies leads to a voltage drop and that causes steady-state error in between the actual and desired output voltage in microgrid. DC-DC bidirectional converters are widely used in microgrid application that facilitates bidirectional energy convertion which having boost mode operation in one direction and bucking mode in other operation. The Interfacing converter is controlled by using the Maximum Power Point Tracking (MPPT) control technique for obtaining maximum power during different atmospheric conditions. In this work, a Model Predictive Control (MPC) strategy is proposed which having an improved cost function, single prediction horizon, and an observer to keep tracking the output voltage. Here, MPC is proposed for cumulative control in between the DC grid side converters and AC grid side inverter, and for improving the power quality and reliability of the utility grid. Both continuous time and discrete time domain models are formed for the whole system for applying different control strategies. Along with MPC, the system has been controlled by using other controllers like as PI controller, Sliding Mode Controller (SMC). Luenberger observer is used to check the DC grid voltage when disturbances are implemented. Here, all the computational results are compared for justifying and validate a better controlling method for microgrid application. These kind of work is first time reported in this manuscript.

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Model Predictive Control of Power Converters for Robust and Fast Operation of AC Microgrids

Cited by 274 publications

References 42 publications

Performance assessment of the VSC using two model predictive control schemes

Performance assessment of the VSC using two model predictive control schemes

A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output $LC$ Filter

A Computational Analysis of Interfacing Converters with Advanced Control Methodologies for Microgrid Application

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