08-16-2017, 10:29 PM
Abstract
This paper presents a new technique for induction
motor parameter identification. The proposed technique is based
on a simple startup test using a standard V/F inverter. The
recorded startup currents are compared to that obtained by
simulation of an induction motor model. A Modified PSO
optimization is used to find out the best model parameter that
minimizes the sum square error between the measured and the
simulated currents. The performance of the modified PSO is
compared with other optimization methods including line search,
conventional PSO and Genetic Algorithms. Simulation results
demonstrate the ability of the proposed technique to capture the
true values of the machine parameters and the superiority of the
results obtained using the modified PSO over other optimization
techniques.
I. INTRODUCTION
Induction motors are the most widely used motors in industry
because they are simple to build, rugged, reliable and have
good self-starting capability. The majority of control schemes
of such motor drives require exact knowledge of at least some
of the induction motor parameters. Mismatch between the
actual motor parameter values and that used within the controller
leads to deterioration in the drive performance [1]. In
order to avoid performance degradation, motor drives usually
perform a pre-tuning algorithm during inverter initialization.
The pre-tuning is based on offline parameter estimation using
data available from simple test of motor performance while
supplied by the inverter. Several methods have been proposed
to tackle the problem of offline induction machine parameter
estimation [2].
The rapidly increasing computational power of personal
computers allowed researchers to implement several optimization
algorithms and verify their efficiency. Researchers
developed many algorithms that mimic natural phenomena.
Examples of these algorithms include the Simulated Annealing
[3], Genetics Algorithms (GA) [4], Ant Colony Optimization
[5] algorithms.
Particle Swarm Optimization (PSO) [6] is among these
nature inspired algorithms. It is inspired by the ability of birds
flocking to find food that they have no previous knowledge of
its location. Every member of the swarm is affected by its own
experience and its neighbors experiences. Although the idea
behind PSO is simple and can be implemented by two lines
of programming code, the emergent behavior is complex and
hard to completely understand [7].
In this paper different versions of PSO are used to identify
six parameters of the motor. The results obtained using these
optimizers are presented and discussed.
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