Date of Graduation

2015

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Lane Department of Computer Science and Electrical Engineering

Committee Chair

Muhammad A Choudhry

Committee Co-Chair

Hany H Ammar

Committee Member

Natalia A Schmid

Abstract

Numerous recent techniques of induction motor parameters calculating are hard to be done and expensive. Accurate calculations of the parameters of these motors would allow savings in different prospective like energy and cost. The major problem in calculating induction motor parameters is that it's hard to measure output power precisely and without harm during the operation of the machines. It will be better to find other way to find out the output power with certain amount of inputs like input voltage and current.;Particle swarm optimization (PSO) and genetic algorithms (GAs) are often used to estimate quantities from limited information. They belong to a class of "weak" search procedures, that is, they do not provide the "best" solution, but one close to it. It is a randomized process in which follows the principles of evolution.;In this thesis genetic algorithm and partial swarm optimization are used to identify induction motor parameters. The inputs used to estimate electrical and mechanical parameters are measured stator voltages and currents. The estimated parameters compare well with the actual parameters. Data Acquisition (DAQ) is used to obtain these variable with the help of LABVIEW software. The induction motor used is a 7.5-hp with a constant frequency and in free acceleration. IEEE standard test of 7.5-hp induction motor is used to compare with performance of the simulated and measured data obtained. According to the output results, method of optimizing induction machine can be used in different models of induction motor.

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