Evolutionary computation based multi-objective pole shape optimization of switched reluctance machine

Balaji, M.; Kamaraj, V.

doi:10.1016/j.ijepes.2012.05.011

Cited by 36 publications

(10 citation statements)

References 32 publications

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“…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%

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Effect of Geometrical Parameters on Optimal Design of Synchronous Reluctance Motor

Nagarajan¹,

Kamaraj²,

Balaji³

et al. 2016

Journal of Magnetics

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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.

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Section: Ann Based Performance Predictionmentioning

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%

Effect of Geometrical Parameters on Optimal Design of Synchronous Reluctance Motor

Nagarajan¹,

Kamaraj²,

Balaji³

et al. 2016

Journal of Magnetics

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“…Moreover, the FEM solutions give only part of the information needed to fully describe the performance; this necessitates extensive post processing of the results using specialist software. Lengthy FEM solver based design computations resulted in the previous FEM based Differential Evolution (DE) optimization attempts to be rather restricted in terms of the number of objective function calls, with most such investigations taking more than 10 hours to complete and the number of design points explored being limited to about 200 [11] -with simultaneous three objectives to be optimized this number of design points appears to be insufficient. Furthermore, the same difficulty is encountered with the use of Design of Experiments (DoE) based optimizations as the number of objective function calls based on FEM solver is large even if the number of design objectives is less than 3 [12].…”

Section: Introductionmentioning

confidence: 99%

Rapid multi‐objective design optimisation of switched reluctance motors exploiting magnetic flux tubes

Stuikys

Sykulski

2018

IET Science, Measurement & Technology

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The magnetic design of Switched Reluctance (SR) motors is inherently a hierarchical process. The design cycle progresses through distinct stages where the accuracy improves but computing times increase greatly, thus it often becomes impractical to furnish extensive multi-objective optimization required to accomplish the optimal design. In order to enable rapid and accurate optimization of SR motors an improved reduced-order computational method of flux tubes is implemented to complement and practically replace the time consuming 2D finite element based magnetic analysis. The paper demonstrates how the use of the improved flux tubes approach to evaluate objective functions results in substantially faster while still accurate multi-objective optimization of SR motors. IntroductionSwitched Reluctance (SR) machines are often regarded as having the simplest mechanical design [1]- [3] compared to other conventional electric machines, such as permanent magnet dc, synchronous reluctance or induction, as they are brushless and have no permanent magnets (PM) or windings on the rotor. These design features make SR machines well suited to a wide range of applications where variable speed operation is required, such as general traction or pump drives. Moreover, the mechanical robustness of SR machines offers cheaper maintenance and better tolerance to harsh environments in which other types of machines cannot operate.Since SR motors are capable of operating in both constant torque and constant power regimes over a wide speed range [4], [5], this operating feature also makes them suitable for the automotive propulsion applications, where the absence of permanent magnets is considered necessary since this industry is very cost sensitive and insists on immunity from unforeseen material price fluctuations, as experienced by PM materials [6]. However, the absence of PM parts makes the SR machines much more difficult to control [7] due to their nonlinear torque-per-ampere characteristic. This operational nonlinearity is a result of the nonlinear magnetic characteristic as the machine's magnetic circuit becomes heavily saturated even during steady-state operation [8].Considering that SR machines are very nonlinear, they require complex analysis and robust design procedures to adequately predict their performance. Until now a rather limited success has been achieved in terms of accurate analytical design procedures for SR machines [9], [10] to provide satisfactory quantification of the machine performance. Most of the magnetic analysis and design is nowadays performed by numerical simulations based on a finite element method (FEM) in order to capture the detailed shape and allow for magnetic nonlinearity. The FEM based solutions can be accurate; however, they are time consuming and lack the intuitive insight into the cause-and-effect relationships between numerous machine design parameters. Moreover, the FEM solutions give only part of the

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“…The inherent simplicity, ruggedness and low cost of a SRM make it a viable machine for various general purpose adjustable speed drive applications [1], [2]. It has no permanent magnet (PM) or winding on the rotor.…”

Section: Introductionmentioning

confidence: 99%

A Novel Technique for Tuning PI -controller In Switched Reluctance Motor Drive for Transportation Systems

Yaich

Ghariani

2018

IJECE

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<p>This paper presents, an optimal basic speed controller for switched reluctance motor (SRM) based on ant colony optimization (ACO) with the presence of good accuracies and performances. The control mechanism consists of proportional-integral (PI) speed controller in the outer loop and hysteresis current controller in the inner loop for the three phases, 6/4 switched reluctance motor. Because of nonlinear characteristics of a SRM, ACO algorithm is employed to tune coefficients of PI speed controller by minimizing the time domain objective function. Simulations of ACO based control of SRM are carried out using MATLAB /SIMULINK software. The behavior of the proposed ACO has been estimated with the classical Ziegler- Nichols (ZN) method in order to prove the proposed approach is able to improve the parameters of PI chosen by ZN method. Simulations results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on classical Ziegler-Nichols method.</p>

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Evolutionary computation based multi-objective pole shape optimization of switched reluctance machine

Cited by 36 publications

References 32 publications

Effect of Geometrical Parameters on Optimal Design of Synchronous Reluctance Motor

Effect of Geometrical Parameters on Optimal Design of Synchronous Reluctance Motor

Rapid multi‐objective design optimisation of switched reluctance motors exploiting magnetic flux tubes

A Novel Technique for Tuning PI -controller In Switched Reluctance Motor Drive for Transportation Systems

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