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

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4669 ◽  
Author(s):  
Amit Kumer Podder ◽  
Md. Habibullah ◽  
Md. Tariquzzaman ◽  
Eklas Hossain ◽  
Sanjeevikumar Padmanaban
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
Predictive Control ◽  
Power Loss ◽  
Weighting Factor ◽  
Small Scale ◽  
Switching Frequency ◽  
Pv Inverter ◽  
The Cost ◽  
Frequency Term

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.

scholarly journals Finite control set model predictive control to balance DC-link capacitor voltage for TType NPC inverter of grid-connected photovoltaic systems

2015 ◽  
Vol 18 (3) ◽  
pp. 5-17
Author(s):  
Dzung Quoc Phan ◽  
Tuyen Dinh Nguyen ◽  
Nhat Minh Nguyen
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
Predictive Control ◽  
Switching Frequency ◽  
Photovoltaic Systems ◽  
Delay Compensation ◽  
Capacitor Voltage ◽  
Finite Control ◽  
Control Set ◽  
The Cost

This paper proposes the Finite control set Model Predictive Control (FCS-MPC) with delay compensation for three-phase threelevel T-Type NPC inverter (T-Type NPC) of grid-connected photovoltaic systems (PV). The proposed FCS-MPC controls the objectives: current tracking control, DC-link capacitor voltage balance, the reduction of switching frequency to ensure issues of the power quality and improve the efficiency of grid-connected of PV system. The cost function of the proposed FCS-MPC uses the 27 possible switching states generated by TType NPC, the optimal switching state is selected in each sampling time that minimizes the cost function. The proposed FCS-MPC has also proposed the delay compensation with two-step prediction horizon at time k+2 to reduce the total harmonic distortion (THD) of the grid current. The proposed FCS-MPC is verified by using Matlab/Simulink.

scholarly journals Total Harmonic Distortion Oriented Finite Control Set Model Predictive Control for Single-Phase Inverters

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3467 ◽  
Author(s):  
Po Li ◽  
Ruiyu Li ◽  
Haifeng Feng
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
Predictive Control ◽  
Single Phase ◽  
Tracking Error ◽  
Harmonic Distortion ◽  
Total Harmonic Distortion ◽  
Finite Control ◽  
Control Set ◽  
The Cost

Inverters are commonly controlled to generate AC current and Total Harmonic Distortion (THD) is the core index in judging the control effect. In this paper, a THD oriented Finite Control Set Model Predictive Control (FCS MPC) scheme is proposed for the single-phase inverter, where a optimization problem is solved to obtain the switching law for realization. Different from the traditional cost function, which focuses on the instantaneous deviation of amplitude between predictive current and its reference, we redesign a cost function that is the linear combination of the current fundamental tracking error, instantaneous THD value and DC component in one fundamental cycle (for 50 Hz, it is 0.02 s). Iterative method is developed for rapid calculation of this cost function. By choosing a switching state from a FCS to minimize the cost function, a FCS MPC is finally constructed. Simulation results in Matlab/Simulink and experimental results on rapid control prototype platform show the effect of this method. Analyses illustrate that, by choosing suitable weight of the cost function, the performance of this THD oriented FCS MPC method is better than the traditional one.

scholarly journals Reduced-Complexity Model Predictive Control with Online Parameter Assessment for a Grid-Connected Single-Phase Multilevel Inverter

2020 ◽  
Vol 12 (19) ◽  
pp. 7997
Author(s):  
Ibrahim Harbi ◽  
Mohamed Abdelrahem ◽  
Mostafa Ahmed ◽  
Ralph Kennel
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
Predictive Control ◽  
Single Phase ◽  
Operating Conditions ◽  
Reference Voltage ◽  
Multilevel Inverter ◽  
Switching Frequency ◽  
Computational Load ◽  
Reduced Complexity

This paper proposes a finite control set model predictive control (FCS-MPC) with a reduced computational burden for a single-phase grid-connected modified packed U-cell multilevel inverter (MPUC-MLI) with two control objectives: reference current tracking and switching frequency minimization. The considered competitive topology consists of two units with six active switches and two DC sources in each unit, allowing the generation of 49 levels in the output voltage, which is considered a significant reduction in the active and passive components compared to the conventional and recently developed topologies of multilevel inverters (MLIs). This topology has 49 different switching states, which means that 49 predictions of the future current and 49 calculations of the cost function are required for each evaluation of the conventional FCS-MPC. Accordingly, the computational load is heavy. Thus, this paper presents two reduced-complexity FCS-MPC methods to reduce the calculation burden. The first technique reduces the computational load almost to half by computing the reference voltage and dividing the states of the MLI into two sets. Based on the reference voltage polarity, one set is defined and evaluated to specify the optimal state, which has a minimal cost function. However, in the second proposed method, only three states of the 49 states are evaluated each iteration, achieving a significant reduction in the execution time and superior control performance compared to the conventional FCS-MPC. A mathematical analysis is conducted based on the reference voltage value to locate the three vectors under evaluation. In the second part of the paper, the sensitivity to parameter variations for the proposed simplified FCS-MPC is investigated and tackled by employing an extended Kalman filter (EKF). In addition, noise related to variable measurement is filtered in the proposed system with the EKF. The simulation investigation was performed using MATLAB/Simulink to validate the system under different operating conditions.

scholarly journals Model Predictive Control for a Recover Water Process

2018 ◽  
Author(s):  
Paolo Mercorelli
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
Computer Simulations ◽  
Lower Bounds ◽  
Predictive Control ◽  
General Model ◽  
Constructive Condition ◽  
The Cost

The goal of this contribution is an application of the Linear General Model Predictive Control (LGMPC). In this paper, stability of the LGMPC is proven by means of a demonstration of a Theorem stating a sufficient and constructive condition. This condition can be applied for calculating the weight matrices of the cost function in the optimisation problem in LGMPC. Lower bounds conditions are found for one of these matrices and then a system with saturation is taken into consideration. The conditions could be interpreted and discussions through physical aspects. The obtained results were tested by means of computer simulations and an example with a recover water process is considered.

scholarly journals Model Predictive Control of Permanent Magnet Synchronous Motor Based on State Transition Constraint Method

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jun Gao ◽  
Jie Zhang ◽  
Mengyang Fan ◽  
Zhiyuan Peng ◽  
Qinghong Chen ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
Permanent Magnet ◽  
Predictive Control ◽  
State Transition ◽  
Transition Probability ◽  
Tracking Error ◽  
Control Period ◽  
Control Performance ◽  
The Cost

Permanent magnet synchronous motors are widely used and have sufficient development prospects in the drive systems of electric vehicles. Traditional model predictive control (MPC) methods are shown to achieve good control performance by tracking the d- and q-axis current as well as limiting the current amplitude. However, the dynamic response performance and current harmonics during the switching process are not considered in the traditional MPC. Therefore, this paper proposes an MPC that can effectively improve control performance, where the switch transfer sequence in the switch constraint module is considered in the improved model. The state transition error is obtained from the switch constraint module according to the current switch state and the transition probability, after which, the integration into the cost function in which the driving error, tracking error, and constraint error are considered. A reinforcement learning (RL) algorithm is used to obtain the weight coefficient of the transition error term in the constraint module for automatically determining the best switch state for the next control period using the cost function. Simulation tests show that the total harmonic distortion of the phase current based on the improved MPC is 978.4%, less than 2843.0% of the traditional MPC method under 20 Nm at 1000 rpm. The torque response time of the motor is reduced by 0.026 s, whereas the simulation results indicate that the 100 km acceleration performance of an electric vehicle is improved by 9.9%.

Road Condition Based Adaptive Model Predictive Control for Autonomous Vehicles

2018 ◽  
Author(s):  
Xin Wang ◽  
Longxiang Guo ◽  
Yunyi Jia
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Fixed Cost ◽  
Control Performance ◽  
Road Condition ◽  
Adaptive Model Predictive Control ◽  
The Cost

Road conditions are of critical importance for motion control problems of the autonomous vehicle. In the existing studies of Model Predictive Control (MPC), road condition is generally modeled with the system dynamics, sometimes simplified as common disturbances, or even ignored based on some assumptions. For most of such MPC formulations, the cost function is usually designed as fixed function and has no relations with the time-varying road conditions. In order to comprehensively deal with the uncertain road conditions and improve the overall control performance, a new model predictive control strategy based on a mechanism of adaptive cost function is proposed in this paper. The relation between the cost function and road conditions is established based on a set of priority policies which reflect the different cost requirements under different road grades and friction coefficients. The adaptive MPC strategy is applied to solve the longitudinal control problem of autonomous vehicles. Simulation studies are conducted on the MPC method with both the fixed cost function and the adaptive cost function. The results show that the proposed adaptive MPC approach can achieve a better overall control performance under different road conditions.

scholarly journals Model Predictive Control with Modulator Applied to Grid Inverter under Voltage Distorted

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4953
Author(s):  
Angelo Lunardi ◽  
Eliomar R. Conde D ◽  
Jefferson de Assis ◽  
Darlan A. Fernandes ◽  
Alfeu J. Sguarezi Filho
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
Reference Frame ◽  
Predictive Control ◽  
System Model ◽  
Electrical Grid ◽  
Voltage Vector ◽  
Stationary Reference Frame ◽  
The Cost ◽  
Positive Results

This research paper presents a model of predictive control with a modulator for the inverter linked to the electrical grid, using the stationary reference frame and operating under grid distorted voltage. The stationary reference frame model for the system is obtained in its fundamental frequency and then the model predictive technique is implemented, which predicts the system actions using the obtained system model without the need of any other harmonic consideration. The controller calculates the voltage vector of the inverter through the minimization of the cost function. Thus, the proposal demonstrates, through experiments, its positive results regarding the low impact of the distorted voltage in the grid current without using any harmonic consideration on the model. Experimental results and comparisons carried out endorse the proposal of this work.

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