NDLI: Neural network based dynamic simulation of induction motor drive

Content Provider	IEEE Xplore Digital Library
Author	Menghal, P.M. Laxmi, A.J.
Copyright Year	2013
Description	Author affiliation: Fac. of Electron., Coll. of Electron. & Mech. Eng., Secunderabad, India (Menghal, P.M.) \|\| Dept. of EEE, Jawaharlal Nehru Technol. Univ., Hyderabad, India (Laxmi, A.J.)
Abstract	With the improvement in the technology of Microprocessor and Power Electronics, Induction motor drives with digital control have become more popular. Artificial intelligent controller (AIC) could be the best candidate for Induction Motor control. Over the last two decades researchers have been working to apply AIC for induction motor drives. This is because that AIC possesses advantages as compared to the conventional PI, PID and their adaptive versions. The main advantages are that the designs of these controllers do not depend on accurate system mathematical model and their performances are robust. In recent years, scientists and researchers have acquired significant development on various sorts of control theories and methods. Among these control technologies, intelligent control methods, which are generally regarded as the aggregation of Fuzzy Logic Control, Neural Network Control, Genetic Algorithm, and Expert System, have exhibited particular superiorities. The artificial neural network controller introduced to the system for keeping the motor speed to be constant when the load varies. The speed control scheme of vector controlle d induction motor drive involves decoupling of the speed and ref speed into torque and flux producing components. The performance of artificial neural network based controller's is compared with that of the conventional proportional integral controller. The dynamic modeling of Induction motor is done and the performance of the Induction motor drive has been analyzed for constant and variable loads. By using neuro controller the transient response of induction machine has been improved greatly and the dynamic response of the same has been made faster.
Starting Page	566
Ending Page	571
File Size	809755
Page Count	6
File Format	PDF
ISBN	9781467360272
e-ISBN	9781467360302
DOI	10.1109/ICPEC.2013.6527722
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-02-06
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Induction motor drives Artificial Intelligence (AI) PI Controller Vector Control (VC) Neurons Artificial neural networks Dynamic Simulation Mathematical model Biological neural networks Artificial Neural Network (ANN) Direct
Content Type	Text
Resource Type	Article

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