Some aspects concerning the reluctance machine’s
laminated rotor optimization
, Alin Argeseanu
, Krisztina Leban
Romanian Academy Timisoara Branch, Timisoara, Romania
Tel: +40 256 491823, Fax: +40 256 491816, E
Dpt. of Electrical Engineering, “Politehnica” University of Timisoara, Romania
Tel: +40 256 403457, E
litehnica” University of Timisoara, Romania
reluctance machine, axially
laminated rotor, genetic algorithms, optimization method
As it is known, the performance indexes of the synchronous reluctance motor with axially
laminated rotor (such as torque per
unit volume, power factor, the maximum value of the torque) are
essentially dependent on the magnetic dissymmetry of its rotor, r
espectively on the saliency ratio,
. Figure 1 shows an example of rotor shape for the two
In order to find the appropriate values of rotor
can improve the motor performance, the
“classical” optimal design supposes to analyze
a large combination of rotor data (width and number of
laminations respectively of insulation sheets, and width of the clamping plates).
1. lamination; 2. insulation sheet 3. clamping plates 4. non
magnetic stainless steel bolt
Example of rotor shape for a two
THE ROTOR OPTIMISATION STRATEGY
For the axially
laminated rotor optimisation, as variable geometric rotor data one considers the
width and the number of insulation sheets.
Obviously, the rotor dissymmetry
depends on all his non
magnetic domains: the insulation sheets
of the axially laminated area (which is defined by the number n, and width of the insulation sheet,
and clamping plates (defined by the width,
It is not possible to estimate the influence of every non
magnetic rotor area on the saliency ratio as
function of a single independent variable. Usually, the width of the clamping plate is obtained fro
mechanical strength conditions, and lower values for
cannot be accepted.
Therefore, one can assume that the rotor dissymmetry depends only on the axially laminated area,
(which is defined by the number n, and width of the insulation sheet,
e basic mechanism of the genetic algorithm
The genetic algorithm method codes parameters of the problem’s search area as a finite length
string. It works with the code of the parameter set, not with the parameter themselves. The objective
s the quality of the solution coded as a string. This result is used to search for a better
The encoding is one of the most important stages of the genetic algorithm. By encoding the proper
quantities and user the proper encoding one in
fluences significantly the algorithm efficiency.
The genetic operators
The genetic algorithm uses the genetic operator of selection, crossover and mutation to manipulate
the strings in a population. One combines the strings in different arrangements to fin
d the string that
optimise the objective function.
The search space is
The objective functions are the synchronous inductance L
The selection operator is used to obtain a new gen
The size of the chromosome is 16 bits string.
The initially population is 50 individuals, the crossover probability is 0.85 and two points
crossover. The mutation is 0.12 and the number of generation is 200.
The optimum value of salie
insulation sheets and for
A large combination of rotor data can be faster investigated with a computing program including an
adequate optimization subroutine, because the running time is significantly shorter in comparison with
te elements method based program.