Design optimization of brushless DC motor using Particle Swarm Optimization

Umadevi, N.; Balaji, M.; Kamaraj, V.

doi:10.1109/icees.2014.6924153

Cited by 8 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to minimize this, a feed forward neural network is trained with distinct set of points in the design field. 145 designs are considered using FEA [28][29][30], to train the neural network [22][23][24][25], for determining the average torque and torque ripple with respect to the geometrical parameters, represented in Fig. 25.…”

Section: Ann Based Performance Predictionmentioning

confidence: 99%

“…Finally, a two objective formulation with average torque and torque ripple percentage as the performance requirements is incepted with the optimization routine [14][15][16][17][18] performed using Multi Objective Particle Swarm Optimization (MOPSO) [19][20][21][22]. Surveying the literature, it is apparent that MOPSO not only boosts the convergence towards the true Pareto front but also produces a welldistributed Pareto front [21,22].…”

Section: Introductionmentioning

confidence: 99%

“…Surveying the literature, it is apparent that MOPSO not only boosts the convergence towards the true Pareto front but also produces a welldistributed Pareto front [21,22]. Therefore, by the utilization of MOPSO, an attempt has been made to improve the performance of SynRel motor.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, by the utilization of MOPSO, an attempt has been made to improve the performance of SynRel motor. The search space for the optimization algorithm is established using neural networks [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…The mathematical representations [22] involved with the single objective PSO is represented as follows.…”

Section: Optimization Proceduresmentioning

confidence: 99%

See 4 more Smart Citations

Effect of Geometrical Parameters on Optimal Design of Synchronous Reluctance Motor

Nagarajan¹,

Kamaraj²,

Balaji³

et al. 2016

Journal of Magnetics

Self Cite

View full text Add to dashboard Cite

Torque ripple minimization without decrease in average torque is a vital attribute in the design of Synchronous Reluctance (SynRel) motor. As the design of SynRel motor is an arduous task, which encompasses many design variables, this work first analyses the significance of the effect of varying the geometrical parameters on average torque and torque ripple and then proposes an extensive optimization procedure to obtain configurations with improved average torque and minimized torque ripple. A hardware prototype is fabricated and tested. The Finite Element Analysis (FEA) software tool used for validating the test results is MagNet 7.6.0.8. Multi Objective Particle Swarm Optimization (MOPSO) is used to determine the various designs meeting the requirements of reduced torque ripple and improved torque performance. The results indicate the efficacy of the proposed methodology and substantiate the utilization of MOPSO as a significant tool for solving design problems related to SynRel motor.

show abstract

Section: Ann Based Performance Predictionmentioning

confidence: 99%