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Position Sensorless Control of 4S3P Inverter Fed PMSM Drive Based on Artificial Neural Network

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dc.contributor.advisor Rafiq, Prof. Dr. Md. Abdur
dc.contributor.author Halder, Kalyan Kumar
dc.date.accessioned 2018-08-09T06:59:26Z
dc.date.available 2018-08-09T06:59:26Z
dc.date.copyright 2012
dc.date.issued 2012-05
dc.identifier.other ID 0000000
dc.identifier.uri http://hdl.handle.net/20.500.12228/248
dc.description This thesis is submitted to the Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Electrical and Electronic Engineering, May 2012. en_US
dc.description Cataloged from PDF Version of Thesis.
dc.description Includes bibliographical references (pages 62-65).
dc.description.abstract A position sensorless Permanent Magnet Synchronous Motor (PMSM) drive based on single layer Recurrent Neural Network (RNN) is presented in this research work. In the proposed control methodology, instead of a usual 6-Switch 3-Phase (6S3P) inverter a 4- Switch 3-Phase (453P) inverter is used. This reduces the cost of the inverter, the switching losses, and the complexity of the control board for generating six Pulse Width Modulated (PWM) signals. A simulation model of the drive system is developed and used in this study. The motor equations are written in rotor fixed d-q reference frame. A Proportional plus Integral (Pt) controller is used to process the speed error to generate the reference torque current. The RNN estimator is used to estimate stator flux components along the stator fixed stationary axes (a-fl). In this study, the Correlated Real Time Recurrent Learning (CRTRL) algorithm is used for training the neural network. The rotor angle is used in vector rotator to generate the reference phase currents. Hysteresis current controller block controls the switching of the three phase inverter to apply voltage to the motor stator. Numerical simulation is carried out in order to verify the effectiveness of the proposed control system. Simulation studies show that the proposed RNN estimator can be used to accurately measure the motor fluxes and rotor angle over a wide speed range. The robustness of the drive system is tested for different operating conditions, i.e., sudden load torque change, parameter deviation, speed reversal, ramp change of speed, load disturbance, presence of computational error, etc. The control system is found to work acceptably under these conditions. It is also simple and low cost to implement in a practical environment. en_US
dc.description.statementofresponsibility Kalyan Kumar Halder
dc.format.extent 66 pages
dc.language.iso en_US en_US
dc.publisher Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh. en_US
dc.rights Khulna University of Engineering & Technology (KUET) thesis/ dissertation/internship reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subject Position Sensorless Control en_US
dc.subject Inverter en_US
dc.subject Artificial Neural Network en_US
dc.title Position Sensorless Control of 4S3P Inverter Fed PMSM Drive Based on Artificial Neural Network en_US
dc.type Thesis en_US
dc.description.degree Master of Science in Electrical and Electronic Engineering
dc.contributor.department Department of Electrical and Electronic Engineering

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