Functional Model Predictive Control Research Articles

Reactive power control for voltage stability of standalone hybrid wind–diesel power system based on functional model predictive control

This study investigates the application of the model predictive control (MPC) approach for voltage stability of an isolated hybrid wind-diesel generation system based on reactive power control. This scheme consists of a synchronous generator (SG) for a diesel-generator (DG) system and an induction generator (IG) for a wind energy conversion system. A static voltage automatic regulator (VAR) compensator (SVC) is connected at terminal bus to stabilise load voltage through compensating of reactive power. Two control paths are used to stabilise load bus voltage based on MPC. The first one by controlling the total reactive power of the system that by controlling the SVC firing angle and hence the load voltage. The second control path by controlling the SG excitation voltage and hence the load bus terminal voltage. The MPC is used to determine the optimal control actions including system constraints. To mitigate calculations effort and to reduce numerical problems, especially in large prediction horizon, an exponentially weighted functional MPC (FMPC) is applied. The proposed controller has been tested through step change in load reactive power plus step increase in input wind power. Also, the performance of the system with FMPC was compared with the classical MPC. Moreover, this scheme is tested against the parameters variations.

Modeling, Simulation and Performance Improvements of a PMSM Based on Functional Model Predictive Control

This paper investigates the application of the model predictive control (MPC) approach to control the speed of a permanent magnet synchronous motor (PMSM) drive system. The MPC is used to calculate the optimal control actions including system constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional model predictive control (FMPC) is employed. In order to validate the effectiveness of the proposed FMPC scheme, the performance of the proposed controller is compared with a classical PI controller through simulation studies. Obtained results show that accurate tracking performance of the PMSM has been achieved.

SPEED CONTROL DESIGN OF A PMSM BASED ON FUNCTIONAL MODEL PREDICTIVE APPROACH

This paper investigates the application of the model predictive control (MPC) approach to control the speed of a permanent magnet synchronous motor (PMSM) drive system. The MPC is used to calculate the optimal control actions including system constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional model predictive control (FMPC) is employed. In order to validate the effectiveness of the proposed FMPC scheme, the performance of the proposed controller is compared with a classical PI controller through simulation studies. Obtained results show that accurate tracking performance of the PMSM has been achieved.

Stand Alone Wind Energy Conversion System Based on Functional Predictive Control

This paper investigates the application of the model predictive control (MPC) approach to control the voltage and frequency of a stand alone wind generation system. This scheme consists of a wind turbine which drives an induction generator feeding an isolated load. A static Var compensator (SVC) is connected at the induction generator terminals to regulate the load voltage. The rotor speed, and thereby the load frequency are controlled via adjusting the mechanical power input using the blade pitch-angle control. The MPC is used to calculate the optimal control actions including system constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional model predictive control (FMPC) is employed. Digital simulations have been carried out in order to validate the effectiveness of the proposed scheme. The proposed controller has been tested through step changes in the wind speed and the load impedance. Simulation resu...

Robust voltage control of a stand alone wind energy conversion system based on functional model predictive approach

This paper investigates the application of the model predictive control (MPC) approach to control the voltage and frequency of a stand alone wind generation system. This scheme consists of a wind turbine which drives an induction generator feeding an isolated load. A static reactive power compensator (SVAR) is connected at the induction generator terminals to regulate the load voltage. The rotor speed, and thereby the load frequency are controlled via adjusting the mechanical power input using the blade pitch-angle control. The MPC is used to calculate the optimal control actions including system constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional model predictive control (FMPC) is employed.Digital simulations have been carried out in order to validate the effectiveness of the proposed scheme. The proposed controller has been tested through step changes in the wind speed and the load impedance. Simulation results show that adequate performance of the proposed wind energy scheme has been achieved. Moreover, this scheme is robust against the parameters variation and eliminates the influence of modeling and measurement errors.

FUNCTIONAL PREDICTIVE CONTROL DESIGN FOR A STAND ALONE WIND ENERGY CONVERSION SYSTEM

This paper investigates the application of the mode l predictive control (MPC) approach to control the voltage and frequency of a stand alone wind generation system. This scheme consists of a w ind turbine which drives an induction generator feeding an isolated l oad. A static reactive power compensator (SVAR) is connected at the induct ion generator terminals to regulate the load voltage. The rotor sand thereby the load frequency are controlled via adjusting the mec hanical power input using the blade pitch-angle control. The MPC is use d to calculate the optimal control actions including system constraint s. To alleviate computational effort and to reduce numerical proble ms, particularly in large prediction horizon, an exponentially weighted functional model predictive control (FMPC) is employed. Digital simulations have been carried out in order to validate the effectiveness of the proposed scheme. The proposed controller has been tested through step changes in the wind speed and t he load impedance. Simulation results show that adequate performance othe proposed wind energy scheme has been achieved. Moreover, this sc heme is robust against the parameters variation and eliminates the influen ce of modeling and measurement errors.

Modeling and control design of a stand alone wind energy conversion system based on functional model predictive control

This paper investigates the application of the model predictive control (MPC) approach to control the voltage and frequency of a stand alone wind generation system. This scheme consists of a wind turbine which drives an induction generator feeding an isolated load. A static VAR compensator is connected at the induction generator terminals to regulate the load voltage. The rotor speed, and thereby the load frequency are controlled via adjusting the mechanical power input using the blade pitch-angle. The MPC is used to calculate the optimal control actions including system constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional model predictive control (FMPC) is employed.

Performance Improvements of a Permanent Magnet Synchronous Machine via Functional Model Predictive Control

This paper investigates the application of the model predictive control (MPC) approach to control the speed of a permanent magnet synchronous motor (PMSM) drive system. The MPC is used to calculate the optimal control actions including system constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional model predictive control (FMPC) is employed. In order to validate the effectiveness of the proposed FMPC scheme, the performance of the proposed controller is compared with a classical PI controller through simulation studies. Obtained results show that accurate tracking performance of the PMSM has been achieved.