alexa Abstract | An Improved DTC Scheme Using Fuzzy Logic for FSTPI-Fedinduction Motor Emulating the SSTPI Operation
ISSN ONLINE(2278-8875) PRINT (2320-3765)

International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering
Open Access

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Abstract

Direct Torque Control (DTC) of induction motor drives has become very popular and widely used in industrial applications due to its fast and good dynamic torque response. Induction motors(IM) are simple in construction and are less sensitive to the motor parameters compared to other vector control methods. The conventional DTC is based on flux and torque hysteresis controllers. Induction motor is fed from a Four Switch Three Phase Inverter (FSTPI) generating the voltage vectors of the Six Switch Three Phase Inverter (SSTPI) by emulation. Applying the most optimized voltage vector that produce the largest tangential component to the circular flux locus can accomplish the fastest dynamic torque response during transient states. The dynamic torque performance is very important in traction and electric vehicle application. A method to achieve fastest dynamic performance by modifying the two leg inverter fed DTC of induction motor based on Fuzzy Logic Concept (FLC) is discussed in this paper. FLC is one of the Artificial Intelligence methods have found high application in most of the nonlinear systems like the electric motor drives. FLC can be used as controller for any system without requirements of the system mathematical model unlike that of the conventional electric drive control, which uses the mathematical model. This paper presents a rule-based fuzzy logic controller scheme designed and applied for the speed control of an induction motor fed from a four switch three phase inverter emulating the six switch three phase inverter. Due to the usage of the FLC concept, the efficiency, reliability and performance of ac drive increases. Initial torque peak and torque ripple are minimized in the four switch three phase inverter based DTC using Fuzzy Logic.

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Author(s): Dhaneesh K K, Mary George

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