scholarly journals A weighted voltage model predictive control method for a virtual synchronous generator with enhanced parameter robustness

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Leilei Guo ◽  
Zhiye Xu ◽  
Nan Jin ◽  
Yanyan Li ◽  
Wei Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Output Voltage ◽  
Control Method ◽  
Synchronous Generator ◽  
Experimental Tests ◽  
Tracking Accuracy ◽  
The Stability ◽  
Parameter Robustness ◽  
Virtual Synchronous Generator

AbstractTo address the problem of insufficient system inertia and improve the power quality of grid-connected inverters, and to enhance the stability of the power system, a method to control a virtual synchronous generator (VSG) output voltage based on model predictive control (MPC) is proposed. Parameters of the inductors, capacitors and other components of the VSG can vary as the temperature and current changes. Consequently the VSG output voltage and power control accuracy using the conventional MPC method may be reduced. In this paper, to improve the parameter robustness of the MPC method, a new weighted predictive capacitor voltage control method is proposed. Through detailed theoretical analysis, the principle of the proposed method to reduce the influence of parameter errors on voltage tracking accuracy is analyzed. Finally, the effectiveness and feasibility of the proposed method are verified by experimental tests using the Typhoon control hardware-in-the-loop experimental platform.

scholarly journals Model Predictive Control for Indirect Boost Matrix Converter Based on Virtual Synchronous Generator

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 60364-60381 ◽  
Author(s):  
Jonggrist Jongudomkarn ◽  
Jia Liu ◽  
Yuta Yanagisawa ◽  
Hassan Bevrani ◽  
Toshifumi Ise
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Synchronous Generator ◽  
Matrix Converter ◽  
Virtual Synchronous Generator

scholarly journals Speed sensorless model predictive control method for a direct-drive wind energy conversion system

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1394-1402 ◽  
Author(s):  
Shengquan Li ◽  
Juan Li ◽  
Hanwen Wu ◽  
Zhongwen Lin
Keyword(s):  
Model Predictive Control ◽  
Wind Energy ◽  
Predictive Control ◽  
Control Method ◽  
Synchronous Generator ◽  
State Observer ◽  
Extended State Observer ◽  
Direct Drive ◽  
Extended State ◽  
Speed Sensorless

Considering the problems of the internal and external disturbances of wind speed in the direct-drive wind energy conversion system based on a permanent magnet synchronous generator, a novel model predictive control based on the extended state observer method without the accurate mathematical system model is proposed in this paper. First, a model predictive control method is employed as the feedback controller, while the mathematical model of the control system can be adjusted online via the rolling optimization strategy. Second, an extended state observer is introduced to estimate the state variables and lumped disturbances, that is, the internal disturbances including nonlinear characteristic, multi-variety coupling effect, uncertainties of system parameters, and external disturbances including variations of wind speeds and uncertainties of the natural environment. Third, the effect of lumped disturbances can be attenuated by the estimated disturbance value via a feedforward channel. In addition, in order to achieve the real-time speed control performance of the permanent magnet synchronous generator, a speed sensorless algorithm based on a flux observer is proposed to solve the problem of unsuitability of mechanical speed sensor. Finally, the simulation results with several wind speed types show that the proposed sensorless model predictive control with the extended state observer strategy is an effective way to improve the performance of anti-disturbance and ability of tracking maximum wind energy of the wind power control system.

scholarly journals Model Predictive Control of the Input Current and Output Voltage of a Matrix Converter as a Ground Power Unit for Airplane Servicing

2021 ◽  
Vol 13 (17) ◽  
pp. 9715
Author(s):  
Zahra Malekjamshidi ◽  
Mohammad Jafari ◽  
Jianguo Zhu ◽  
Marco Rivera ◽  
Wen Soong
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Power Supply ◽  
Output Voltage ◽  
Harmonic Distortion ◽  
Experimental Tests ◽  
Input Power ◽  
Matrix Converter ◽  
Nonlinear Loads ◽  
Source Current

This paper deals with the design, control, and implementation of a three-phase ac–ac mobile utility power supply using a matrix converter for airplane servicing applications. Using a matrix converter as a compact direct ac-to-ac converter can provide savings in terms of the size and cost of a mobile power supply compared to common back-to-back converters. Furthermore, using the proposed direct matrix converter eliminates the need for bulky electrolytic capacitors and increases the system’s reliability and lifetime. A finite control set model predictive control is used to generate a high-quality 115 V/400 Hz output voltage and a low-harmonic-distortion source current with a unity input power factor for various load conditions, including balanced, unbalanced, linear, and nonlinear loads. The predictive strategy is used to control the output voltage and source current for each possible switching state in order to simultaneously track the references. To achieve a further reduction in the system’s size and cost, an active damping strategy is used to compensate for the instability caused by the input filter in contrast to the passive method. Experimental tests were conducted on a prototype matrix converter to validate the performance of the proposed control strategy.

Application of Neural Network for Nonlinear Predictive Control

2012 ◽  
Vol 562-564 ◽  
pp. 1964-1967 ◽  
Author(s):  
Zhi Cheng Xu ◽  
Bin Zhu ◽  
Qing Bin Jiang
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Control Method ◽  
Nonlinear Modeling ◽  
Nonlinear Process ◽  
Effective Control ◽  
Nonlinear Predictive Control ◽  
The Stability

A novel model predictive control method was proposed for a class of dynamic processes with modest nonlinearities in this paper. In this method, a diagonal recurrent neural network (DRNN) is used to compensate nonlinear modeling error that is caused because linear model is regarded as prediction model of nonlinear process. It is aimed at offsetting the effect of model mismatch on the control performance, strengthening the robustness of predictive control and the stability of control system. Under a certain assumption condition, linear model predictive control method is extended to nonlinear process, which doesn’t need solve nonlinear optimization problem. Consequently, the computational efforts are reduced drastically. The simulation example shows that the proposed method is an effective control strategy with excellent tracing characteristics and strong robustness.

Trajectory control method of stratospheric airships based on model predictive control in wind field

2017 ◽  
Vol 233 (2) ◽  
pp. 418-425 ◽  
Author(s):  
Jiashi Zhang ◽  
Xixiang Yang ◽  
Xiaolong Deng ◽  
Huijing Lin
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wind Field ◽  
Degrees Of Freedom ◽  
Control Method ◽  
Sliding Mode ◽  
Trajectory Control ◽  
Flight Mechanics ◽  
Tracking Accuracy ◽  
Stratospheric Airship

Trajectory control of the stratospheric airship is an important but challenging task due to the characteristics of large inertia, long time delay, and large disturbance with wind field. Based on six-degrees-of-freedom dynamic model and kinetic model, this paper proposes a model predictive control method for the trajectory control of stratospheric airship in wind field. The aerostatics, aerodynamics, and flight mechanics are incorporated into the model with consideration of wind interference. By linearization of the dynamics equations by small perturbations method, a trajectory controller is designed using model predictive control method. A simulation program is developed from the model and is applied to analyze the control response of the high altitude airship. Results show that the method has a fast response and high accuracy trajectory control in wind field. A simulation comparison of airship performance with model predictive control and traditional sliding mode control shows that model predictive control can give a higher tracking accuracy at initial stage.

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