Performance Assessment of Grid Forming Converters Using Different Finite Control Set Model Predictive Control (FCS-MPC) Algorithms

Alhasheem, Mohammed; Blaabjerg, Frede

doi:10.3390/app9173513

Cited by 15 publications

(15 citation statements)

References 23 publications

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“…Two recent papers [29,30] studied the effect of different cost functions on a gridforming converter. These are the most recent studies regarding the switching frequency of FCS-MPC, but both [29,30] miss a more rigorous quantification of switching frequency spread.…”

Section: State-of-the-art Of Fcs-mpc Switching Frequencymentioning

confidence: 99%

Total Frequency Spread: A New Metric to Assess the Switching Frequency Spread of FCS-MPC

et al. 2022

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This work proposes a metric to assess the spread of the switching frequency profile of power converters, focusing on Finite Control Set-Model-Predictive-Control (FCS-MPC)-based converters, and analyses the effect of the filter sizing, sampling period, and cost function on the switching frequency spread. An algorithm that estimates the instantaneous switching frequency is the basis of the proposed metric. The literature review shows that only two metrics, THD and average switching frequency, are used to assess the switching frequency of FCS-MPC, but the findings of this research show that both are insufficient to characterize FCS-MPC. The main contribution of this paper is that now researchers can quantitatively assess the switching frequency spread of FCS-MPC through the proposed metric.

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Section: State-of-the-art Of Fcs-mpc Switching Frequencymentioning

confidence: 99%

Total Frequency Spread: A New Metric to Assess the Switching Frequency Spread of FCS-MPC

et al. 2022

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“…The frequency and voltage error deviations to produce control signals that are given by Equations ( 32) and (33), respectively, for the error correction that is sent to the primary controller in islanded mode, are processed by the secondary control [67],…”

Section: Secondary Controlmentioning

confidence: 99%

“…Model predictive control has received extensive attention from researchers. It is now a well-established method for constrained control, having enough flexibility to include constraints and nonlinearities, including fast dynamic response and simplicity [31][32][33]. MPC includes principles of feedback control and numerical optimisation.…”

Section: Introductionmentioning

confidence: 99%

Predictive Control for Microgrid Applications: A Review Study

et al. 2020

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Microgrids need control and management at different levels to allow the inclusion of renewable energy sources. In this paper, a comprehensive literature review is presented to analyse the latest trends in research and development referring to the applications of predictive control in microgrids. As a result of this review, it was found that the application of predictive control techniques on microgrids is performed for the three control levels and with adaptations of the models in order to include uncertainties to improve their performance and dynamics response. In addition, to ensure system stability, but also, at higher control levels, coordinated operation among the microgrid’s components and synchronised and optimised operation with utility grids and electric power markets. Predictive control appears as a very promising control scheme with several advantages for microgrid applications of different control levels.

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“…The loss of power occurs due to the switching devices used in the circuit, which significantly influence the efficiency of the inverter [53][54][55]. The harmonic content in the output current and heat generated during the conduction of the switching device also provides a significant contribution to the overall loss of the system.…”

Section: Power Loss Analysismentioning

confidence: 99%

Power Loss Analysis of Solar Photovoltaic Integrated Model Predictive Control Based On-Grid Inverter

et al. 2020

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This paper presents a finite control-set model predictive control (FCS-MPC) based technique to reduce the switching loss and frequency of the on-grid PV inverter by incorporating a switching frequency term in the cost function of the model predictive control (MPC). In the proposed MPC, the control objectives (current and switching frequency) select an optimal switching state for the inverter by minimizing a predefined cost function. The two control objectives are combined with a weighting factor. A trade-off between the switching frequency (average) and total harmonic distortion (THD) of the current was utilized to determine the value of the weighting factor. The switching, conduction, and harmonic losses were determined at the selected value of the weighting factor for both the proposed and conventional FCS-MPC and compared. The system was simulated in MATLAB/Simulink, and a small-scale hardware prototype was built to realize the system and verify the proposal. Considering only 0.25% more current THD, the switching frequency and loss per phase were reduced by 20.62% and 19.78%, respectively. The instantaneous overall power loss was also reduced by 2% due to the addition of a switching frequency term in the cost function which ensures a satisfactory empirical result for an on-grid PV inverter.

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Performance Assessment of Grid Forming Converters Using Different Finite Control Set Model Predictive Control (FCS-MPC) Algorithms

Cited by 15 publications

References 23 publications

Total Frequency Spread: A New Metric to Assess the Switching Frequency Spread of FCS-MPC

Total Frequency Spread: A New Metric to Assess the Switching Frequency Spread of FCS-MPC

Predictive Control for Microgrid Applications: A Review Study

Power Loss Analysis of Solar Photovoltaic Integrated Model Predictive Control Based On-Grid Inverter

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