SENSORLESS OPERATION OF 8/6 SR MACHINE WITH THE SELECTION OF OPTIMUM CONDUCTION ANGLE BASED ON MULTI OBJECTIVE PARTICLE SWARM OPTIMIZATION

Abstract

This paper presents a sensor less scheme
to obtain an optimum operating period for each
load variation in the 8/6 switched reluctance motor.
The drawbacks of using a position sensor in dusty
environment are eliminated in this approach. Here,
the operating parameters are fed back to the
Adaptive Neural Network (ANN) controller. This
model is developed efficiently and trained under
supervised learning method in which flux-linkage
and phase-current are fed as input and rotor
position is estimated as output. Multi objective
particle swarm optimization (MOPSO) based
optimization technique is used in determining the
optimum operating angle for the SR machine. The
finalized model consist of ANN based position
prediction loop, MOPSO based optimum angle
selection loop with rigid current controller. The
main objective of the model concentrates on
maximizing the average torque with minimum
torque ripple in the optimized operating region
under various loading conditions with removal of
position sensor. This system has the advantages of
robustness, simple construction, reduced
manufacturing cost with the absence of position
sensor etc. The simulation is carried out in
MATLAB/SIMULINK and Hardware is
implemented using dsPIC.