Recurrent modified Elman neural network control of permanent magnet synchronous generator system based on wind turbine emulator

2013 ◽  
Vol 5 (5) ◽  
pp. 053103 ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Permanent Magnet ◽  
Synchronous Generator ◽  
Network Control ◽  
Neural Network Control ◽  
Permanent Magnet Synchronous Generator ◽  
Generator System ◽  
Elman Neural Network

scholarly journals Novel Modified Elman Neural Network Control for PMSG System Based on Wind Turbine Emulator

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Generator ◽  
Power Converter ◽  
Network Control ◽  
Elman Neural Network ◽  
Wind Speeds ◽  
Ac Power

The novel modified Elman neural network (NN) controlled permanent magnet synchronous generator (PMSG) system, which is directly driven by a permanent magnet synchronous motor (PMSM) based on wind turbine emulator, is proposed to control output of rectifier (AC/DC power converter) and inverter (DC/AC power converter) in this study. First, a closed loop PMSM drive control based on wind turbine emulator is designed to generate power for the PMSG system according to different wind speeds. Then, the rotor speed of the PMSG, the voltage, and current of the power converter are detected simultaneously to yield better power output of the converter. Because the PMSG system is the nonlinear and time-varying system, two sets online trained modified Elman NN controllers are developed for the tracking controllers of DC bus power and AC power to improve output performance of rectifier and inverter. Finally, experimental results are verified to show the effectiveness of the proposed control scheme.

Direct Drive Permanent Magnet Synchronous Wind Generator Maximum Power Tracking Control

2013 ◽  
Vol 724-725 ◽  
pp. 459-462
Author(s):  
Jia Ying Zhang ◽  
Li Ping Zhang ◽  
Gui Ling Xiao
Keyword(s):  
Wind Turbine ◽  
Permanent Magnet ◽  
Control Strategy ◽  
High Efficiency ◽  
Synchronous Generator ◽  
Direct Drive ◽  
Operating Characteristics ◽  
Permanent Magnet Synchronous Generator ◽  
Generator System ◽  
Wind Generator

Direct drive permanent magnet synchronous generator have the advantages of direct drive, simple structure, high efficiency and so on, in which make it become one of mainstream models within MW wind turbine presently. Making the direct drive permanent magnet wind power generation system as the main research object, based on the principle of the operating characteristics of direct drive permanent magnet synchronous wind generator ( DDPMG ), establish mathematical model of the whole system including wind velocity, wind turbine, direct drive permanent magnet synchronous generator and machine side converter, Appling the method of stator flux orientation to make the study of vector control strategy, to build the simulation model of direct drive permanent magnet synchronous generator system with Matlab and simulate when wind speed changes by step, the results validate the reasonableness of the model and the correctness and feasibility of the control strategy.

Neural network adaptive backstepping control via uncertainty compensation for PMSG-based variable-speed wind turbine: Controller design and stability analysis

2021 ◽  
pp. 0309524X2110312
Author(s):  
Mohsin Beniysa ◽  
Aziz El Janati El Idrissi ◽  
Adel Bouajaj ◽  
Mohammed Réda Britel ◽  
Ezendu Ariwa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stability Analysis ◽  
Wind Turbine ◽  
Permanent Magnet ◽  
Synchronous Generator ◽  
Adaptive Backstepping ◽  
Permanent Magnet Synchronous Generator ◽  
Artificial Neural ◽  
Uncertainty Compensation

This paper proposes a design scheme along with stability analysis of a new adaptive backstepping controller designed for permanent magnet synchronous generator-based wind turbine, by using artificial neural network-based uncertainty compensation. The idea is to control the rotor speed and the mechanical power generated under internal and external nonlinear parametric uncertainties. An uncertain model of permanent magnet synchronous generator is designed. Then, two artificial neural network compensators are built to compensate such uncertainties in the current loops. The stability of the closed-loop system is studied according to the Lyapunov function. Simulations of the dynamic model are performed under both variable step and random wind speeds by using the DEV-C++ software, and the results are plotted with MATLAB. Compared to the classical direct torque control technique, the obtained results show the robustness of the proposed controller despite the presence of different uncertainties.

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