Open AccessSpeed control of sensorless induction generator by artificial neural network in wind energy conversion system
Author(s) -
Merabet Adel,
Tanvir Aman A.,
Beddek Karim
Publication year - 2016
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2016.0285
Subject(s) - control theory (sociology) , rotor (electric) , torque , stator , induction generator , multivariable calculus , matlab , artificial neural network , control engineering , vector control , extended kalman filter , kalman filter , mras , engineering , computer science , wind power , induction motor , voltage , artificial intelligence , physics , control (management) , electrical engineering , mechanical engineering , thermodynamics , operating system
This study presents a new strategy for estimating the states (rotor flux and speed) and the load torque to implement a multivariable controller for a sensorless three‐phase squirrel‐cage induction machine in wind energy conversion systems. The multivariable control is carried out using input–output feedback law and its objective is to track profiles of the rotational speed and the rotor flux amplitude. The state estimation considerably improves the performance of rotor flux based model reference adaptive system in the variable speed region of operation. The technique uses Kalman filter as a rotor flux observer and an artificial neural network adaption mechanism to estimate the rotor speed. The state estimation requires only the measurements of the stator voltages and currents. The estimation method, for both states and torque, is not invasive as no mechanical sensors are needed. The wind energy conversion system and the proposed control‐estimation techniques are simulated in Matlab/Simulink software platform and tested using the OPAL‐RT real‐time simulator (OP5600) to verify the accuracy of the proposed control‐estimation method.