Shiraz University
Iranian Journal of Science and Technology Transactions of Electrical Engineering
2228-6179
37
2
2013
12
30
THE EFFECT OF NON-UNIFORM AIR-GAP ON THE NOISE IN SWITCHED RELUCTANCE MOTORS
183
191
EN
10.22099/ijste.2013.1887
The major problems in switched reluctance motors (SRMs) are radial force and torqueripple which cause increased undesirable acoustic noise. This paper describes an approach todetermine optimum magnetic circuit parameters to minimize both radial force and torque ripple forsuch motors. There is no publication for simultaneous reduction of both radial force and torqueripple. In previous works, torque ripple was decreased without any research on the radial force orcounter. In this paper, a procedure for radial force and torque ripple reduction in SR motors isproposed. To decrease the acoustic noise, the air gap width is increased while the radial force ismaximized. On the other hand, by increasing the air gap width, torque decreases. By varying theangular interval and consequently the air gap width, the optimum angular interval is achieved. Inthe optimum angular interval, the radial force decreases while the torque remains constant. A twodimensional(2-D) finite element (FE) analysis carried out on the 6/4 SRM. By using the methodof the compensated current, the ripple torque can be reduced to zero, radial force decreases 3.7%,and the acoustic noise power decreases 7.3% in the non-uniform air gap in comparison with thestatic case. Radial force decreases 5.6% and the acoustic noise power decreases 10.9% in theuniform air gap in comparison with the static case.
Switched reluctance motor,radial force,torque ripple,acoustic noise
http://ijste.shirazu.ac.ir/article_1887.html
http://ijste.shirazu.ac.ir/article_1887_2e97bdd46a60817fde7a45b46b62a41b.pdf
Shiraz University
Iranian Journal of Science and Technology Transactions of Electrical Engineering
2228-6179
37
2
2013
12
30
COMPARING EVOLUTIONARY ALGORITHMS ON TUNING THE PARAMETERS OF FUZZY WAVELET NEURAL NETWORK
193
198
EN
10.22099/ijste.2013.1888
In recent years Fuzzy Wavelet Neural Networks (FWNNs) have been used in manyareas. Function approximation is an important application of FWNNs. One of the main problemsin effective usage of FWNN is tuning of its parameters. In this paper several different evolutionaryalgorithms including Genetic Algorithm (GA), Gravitational Search Algorithm (GSA),Evolutionary Strategy (ES), Fast Evolutionary Strategy (FES) and variants of DifferentialEvolutionary algorithms (DE) are used for adjusting these parameters on five test functions. Theobtained results are compared based on some measures by using multiple non-parametricstatistical tests. The comparison reveals the superiority of some variants of DE in terms ofconvergence behavior and the ability of function approximation.
Fuzzy wavelet neural networks,function approximation,evolutionary algorithms,nonparametric
statistical test
http://ijste.shirazu.ac.ir/article_1888.html
http://ijste.shirazu.ac.ir/article_1888_72ff271533dd73b4414448587d27c534.pdf