scholarly journals Direct Phase Angle and Voltage Amplitude Model Predictive Control of a Power Converter for Microgrid Applications

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2254
Author(s):  
Thai-Thanh Nguyen ◽  
Hyeong-Jun Yoo ◽  
Hak-Man Kim ◽  
Huy Nguyen-Duc
Keyword(s):  
Model Predictive Control ◽  
Power Control ◽  
Phase Angle ◽  
Predictive Control ◽  
Control Strategy ◽  
Control Strategies ◽  
Operation Mode ◽  
Power Converter ◽  
Current Control ◽  
Voltage Amplitude

Several control strategies of the finite control set model predictive controls (FCS-MPC) have been proposed for power converters, such as predictive current control (PCC), direct predictive power control (DPPC), and predictive voltage control (PVC). However, for microgrid (MG) applications, the control strategy of the FCS-MPC for a power converter might be changed according to the operation mode of the MG system, which results in a transient response in the system voltage or current during the mode transition. This study proposes a new control strategy of FCS-MPC for use in both islanded and grid-connected operation modes of an MG system. Considering the characteristic of a synchronous generator, a direct phase angle and voltage amplitude model predictive control (PAC) of a power converter is proposed in this study for MG applications. In the islanded mode, the system frequency is directly controlled through the phase angle of the output voltage. In the grid-connected mode, a proportional-integral (PI) regulator is used to compensate for the phase angle and voltage amplitude of the power converter for constant power control. The phase angle of the system voltage can be easily adjusted for the synchronization process of an MG system. A comparison study on the proposed PAC method and existing predictive methods is carried out to show the effectiveness of the proposed method. The feasibility of the proposed PAC strategy is evaluated in a simulation-based system by using the MATLAB/Simulink environment.

scholarly journals A Control Method for IPMSM Based on Active Disturbance Rejection Control and Model Predictive Control

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 760
Author(s):  
Fang Liu ◽  
Haotian Li ◽  
Ling Liu ◽  
Runmin Zou ◽  
Kangzhi Liu
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Duty Cycle ◽  
Control Strategy ◽  
Disturbance Rejection ◽  
Torque Ripple ◽  
Current Control ◽  
Active Disturbance Rejection Control ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control

In this paper, the speed tracking problem of the interior permanent magnet synchronous motor (IPMSM) of an electric vehicle is studied. A cascade speed control strategy based on active disturbance rejection control (ADRC) and a current control strategy based on improved duty cycle finite control set model predictive control (FCSMPC) are proposed, both of which can reduce torque ripple and current ripple as well as the computational burden. First of all, in the linearization process, some nonlinear terms are added into the control signal for voltage compensation, which can reduce the order of the prediction model. Then, the dq-axis currents are selected by maximum torque per ampere (MTPA). Six virtual vectors are employed to FCSMPC, and a novel way to calculate the duty cycle is adopted. Finally, the simulation results show the validity and superiority of the proposed method.

scholarly journals Model Predictive Control with a Relaxed Cost Function for Constrained Linear Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
David Sotelo ◽  
Antonio Favela-Contreras ◽  
Viacheslav V. Kalashnikov ◽  
Carlos Sotelo
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
State Space ◽  
Predictive Control ◽  
Discrete Time ◽  
Control Strategy ◽  
Control Strategies ◽  
State Space Model ◽  
Control Technique ◽  
Space Model

The Model Predictive Control technique is widely used for optimizing the performance of constrained multi-input multi-output processes. However, due to its mathematical complexity and heavy computation effort, it is mainly suitable in processes with slow dynamics. Based on the Exact Penalization Theorem, this paper presents a discrete-time state-space Model Predictive Control strategy with a relaxed performance index, where the constraints are implicitly defined in the weighting matrices, computed at each sampling time. The performance validation for the Model Predictive Control strategy with the proposed relaxed cost function uses the simulation of a tape transport system and a jet transport aircraft during cruise flight. Without affecting the tracking performance, numerical results show that the execution time is notably decreased compared with two well-known discrete-time state-space Model Predictive Control strategies. This makes the proposed Model Predictive Control mainly suitable for constrained multivariable processes with fast dynamics.

scholarly journals Predictive-Fixed Switching Current Control Strategy Applied to Six-Phase Induction Machine

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2294 ◽  
Author(s):  
Osvaldo Gonzalez ◽  
Magno Ayala ◽  
Jesus Doval-Gandoy ◽  
Jorge Rodas ◽  
Raul Gregor ◽  
...  
Keyword(s):  
Predictive Control ◽  
Control Strategy ◽  
Control Strategies ◽  
High Harmonic ◽  
Induction Machine ◽  
Experimental Tests ◽  
Sampling Period ◽  
Current Control ◽  
Switching Frequency ◽  
Multiphase Motor

In applications such as multiphase motor drives, classical predictive control strategies are characterized by a variable switching frequency which adds high harmonic content and ripple in the stator currents. This paper proposes a model predictive current control adding a modulation stage based on a switching pattern with the aim of generating a fixed switching frequency. Hence, the proposed controller takes into account the prediction of the two adjacent active vectors and null vector in the ( α - β ) frame defined by space vector modulation in order to reduce the (x-y) currents according to a defined cost function at each sampling period. Both simulation and experimental tests for a six-phase induction motor drive are provided and compared to the classical predictive control to validate the feasibility of the proposed control strategy.

scholarly journals Continuous Control Set Model Predictive Control of a Switch Reluctance Drive Using Lookup Tables

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3317 ◽  
Author(s):  
Alecksey Anuchin ◽  
Galina L. Demidova ◽  
Chen Hao ◽  
Alexandr Zharkov ◽  
Andrei Bogdanov ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Power Converter ◽  
Lookup Table ◽  
Continuous Control ◽  
Fast Evaluation ◽  
Switched Reluctance ◽  
Torque Pulsations ◽  
Control Set

A problem of the switched reluctance drive is its natural torque pulsations, which are partially solved with finite control set model predictive control strategies. However, the continuous control set model predictive control, required for precise torque stabilization and predictable power converter behavior, needs sufficient computation resources, thus limiting its practical implementation. The proposed model predictive control strategy utilizes offline processing of the magnetization surface of the switched reluctance motor. This helps to obtain precalculated current references for each torque command and rotor angular position in the offline mode. In online mode, the model predictive control strategy implements the current commands using the magnetization surface for fast evaluation of the required voltage command for the power converter. The proposed strategy needs only two lookup table operations requiring very small computation time, making instant execution of the whole control system possible and thereby minimizing the control delay. The proposed solution was examined using a simulation model, which showed precise and rapid torque stabilization below rated speed.

scholarly journals The Active Frequency Control Strategy of the Wind Power Based on Model Predictive Control

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ya-ling Chen ◽  
Yin-peng Liu ◽  
Xiao-fei Sun
Keyword(s):  
Model Predictive Control ◽  
Power Control ◽  
Wind Turbine ◽  
Output Power ◽  
Predictive Control ◽  
Wind Turbines ◽  
Control Strategy ◽  
Frequency Control ◽  
Frequency Deviation ◽  
Load Shedding

In this paper, an active frequency control strategy of wind turbines based on model predictive control is proposed by using the power margin of wind turbines operating in load shedding mode. The frequency response model of the microgrid system with the load shedding of the wind turbines is used to predict the output power and system frequency deviation of the wind turbine. According to the prediction information, the output power control signal of the model predictive controller in the wind turbine can be optimized. On this basis, a wind turbine active participation frequency control strategy based on model predictive control is designed by rolling prediction and optimization. The wind turbine power control signal after the strategy is used to adjust the output power of the wind turbine and balance the change of the active power of the system to reduce the frequency deviation.

scholarly journals Finite Control Set Model Predictive Control of Six-Phase Asymmetrical Machines—An Overview

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4693 ◽  
Author(s):  
Pedro Gonçalves ◽  
Sérgio Cruz ◽  
André Mendes
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Performance ◽  
Control Strategies ◽  
Current Control ◽  
Torque Control ◽  
Current Harmonics ◽  
Electric Drives ◽  
Finite Control ◽  
Control Set

Recently, the control of multiphase electric drives has been a hot research topic due to the advantages of multiphase machines, namely the reduced phase ratings, improved fault tolerance and lesser torque harmonics. Finite control set model predictive control (FCS-MPC) is one of the most promising high performance control strategies due to its good dynamic behaviour and flexibility in the definition of control objectives. Although several FCS-MPC strategies have already been proposed for multiphase drives, a comparative study that assembles all these strategies in a single reference is still missing. Hence, this paper aims to provide an overview and a critical comparison of all available FCS-MPC techniques for electric drives based on six-phase machines, focusing mainly on predictive current control (PCC) and predictive torque control (PTC) strategies. The performance of an asymmetrical six-phase permanent magnet synchronous machine is compared side-by-side for a total of thirteen PCC and five PTC strategies, with the aid of simulation and experimental results. Finally, in order to determine the best and the worst performing control strategies, each strategy is evaluated according to distinct features, such as ease of implementation, minimization of current harmonics, tuning requirements, computational burden, among others.

scholarly journals Distributed Input-Delay Model Predictive Control of Inland Waterways

10.29007/p2cx ◽  
2018 ◽  
Author(s):  
Pau Segovia ◽  
Lala Rajaoarisoa ◽  
Fatiha Nejjari ◽  
Eric Duviella ◽  
Vicenç Puig
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategies ◽  
Current Control ◽  
Distributed Model ◽  
Centralized Control ◽  
Delay Model ◽  
Inland Waterways ◽  
Control Actions ◽  
System Partitioning

Inland waterways are large, complex systems composed of interconnected navigation reaches dedicated mainly to navigation. These reaches are generally characterized by negligible bottom slopes and large time delays. The latter requires ensuring the coordination of the current control actions and their delayed effects in the network. Centralized control strategies are often impractical to implement due to the size of the system. To overcome this issue, a distributed Model Predictive Control (MPC) approach is proposed. The system partitioning is based on a reordering of the optimality conditions matrix, and the control actions are coordinated by means of the Optimality Condition Decomposition (OCD) methodology. The case study is inspired by a real inland waterways system and shows the performance of the approach.

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