Time & Location:
Auditorium (G01011, KU Main Campus )
Thursday 13th June 2024 (02:00 pm 鈥� 02:30 pm)
Abstract
This seminar discusses advancements in direct torque control (DTC) algorithms for open-end winding induction motor (OEWIM) sensorless drives, focusing on the integration of artificial neural networks (ANNs). The traditional lookup table in DTC schemes is substituted by an ANN, which acts as an effective data storage and processing unit. This novel approach enables optimal voltage vector selection based on minute variations in torque and flux errors across various angular positions of the stator flux vector. Notably, the substitution of hysteresis controllers with the ANN enhances both torque and flux tracking, thereby improving the transient response of the motor. The presentation will cover detailed experimental outcomes and comparative analyses, underlining the benefits and practical implications of the improved DTC methodology.
Biography
Kaif Ahmed Lodi received the B.Tech. and M.Tech. degrees in electrical engineering from Aligarh Muslim University, Aligarh, India, in 2016 and 2019, respectively. He is currently working toward the Ph.D. degree in electrical engineering with Khalifa University, Abu Dhabi, UAE. His research interests lie in the areas of electrical drives, soft computing, power converters, and grid interface of converters.
