A6-3 - Sedma Nacionalna Konferencija so Me|unarodno U~estvo...

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INDUCTION MOTOR PARAMETER ESTIMATION USING GENETIC ALGORITHMS WITH EXPERIMENTAL PROOF Zdravko Andonov 1 , Borislav Jeftenic 2 , Slobodan Mircevski 1 1 Faculty of Electrical Engineering – Skopje, Karpoš II bb, PO Box 574, 1000 Skopje, zandonov@etf.ukim.edu.mk 2 Faculty of Electrical Engineering – Beograd, Bulevar Kralja Aleksandra 73, 11000 Beograd, Serbia & Montenegro, jeftenic@etf.bg.ac.yu Abstract – Induction motors are used in electrical drives where simple reliable and robust machine is the first requirement. For the modern adjustable speed drives, induction motor drive model stator, rotor and mutual resistances and inductances must be known. Also, for the motor protection purposes the windings temperature has to known. In this paper is used method based on genetic algorithms for equivalent circuit parameters estimation only with the known catalogue data. The idea is using induction motor steady-state equivalent circuit to estimate motor parameters so that calculated values would be very close to known catalogue data. In this paper the results of the implementation of the purposed method and comparison with the measured values for the one induction drive will be present. Keywords – Induction motor, parameter estimation, genetic algorithms, temperature 1. INTRODUCTION Induction motors are used in electrical drives where simple reliable and robust machine is the first requirement. Therefore nowadays induction motors participate in over 85% of all installed AC drives, in power ranging from few watts to over 10 MW. As result of power electronics development and control systems improvement, in last years induction motor replace DC motor more frequently in adjustable speed drives (ASD). In induction motor adjustable speed drives the accu- rate dynamic model of machine is necessary. For the induction motor drive model stator, rotor and mutual resistances and inductances must be known. They could be determined experimentally or estimated with a help of some motor model based on catalogue data. The genetic algorithm is method, which may be used to solve a system of nonlinear equations. The genetic algorithm uses objective functions based on some performance criterion to calculate an error. However, the genetic algorithm is based on natural selection using random numbers and does not require a good initial estimate. That is, solutions to complex problems could involve from poor initial estimates. Genetic algorithms manipulate strings of numbers and measure each string’s strength with a fitness value. The stronger strings advance and mate with other strong strings to produce offspring. One of the most important advantages of the genetic algorithm over the other techniques is that it is able to find the global minimum, instead of a local minimum, and that the initial estimate need not be close to the actual values.
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This note was uploaded on 02/18/2010 for the course ITK ETF113L07 taught by Professor Popovskiborislav during the Spring '10 term at Pacific.

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A6-3 - Sedma Nacionalna Konferencija so Me|unarodno U~estvo...

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