Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya)

Afifi, Mohamed; Rezk, Hegazy; Ibrahim, Mohamed N.; El-Nemr, Mohamed K.

doi:10.3390/math9101107

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…In this way, the computation times are compared among themselves to see whether they influence each algorithm's performance, since in works such as ref. [47], it has been reported that the increase in iterations negatively affects the optimization of the solutions. This is contrary to what occurs in our research, where the increased iterations and, consequently, high computational cost improved the parametric estimates.…”

Section: Discussionmentioning

confidence: 99%

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Munciño,

Damian-Ramírez,

Cruz-Fernández

et al. 2024

Algorithms

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Direct current motors are widely used in industry applications, and it has become necessary to carry out studies and experiments for their optimization. In this manuscript, a comparison between heuristic and metaheuristic algorithms is presented, specifically, the Steiglitz–McBride, Jaya, Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) algorithms. They were used to estimate the parameters of a dynamic model that approximates the actual responses of current and angular velocity of a DC motor. The inverse of the Euclidean distance between the current and velocity errors was defined as the fitness function for the metaheuristic algorithms. For a more comprehensive comparison between algorithms, other indicators such as mean squared error (MSE), standard deviation, computation time, and key points of the current and velocity responses were used. Simulations were performed with MATLAB/Simulink 2010 using the estimated parameters and compared to the experiments. The results showed that Steiglitz–McBride and GWO are better parametric estimators, performing better than Jaya and GA in real signals and nominal parameters. Indicators say that GWO is more accurate for parametric estimation, with an average MSE of 0.43%, but it requires a high computational cost. On the contrary, Steiglitz–McBride performed with an average MSE of 3.32% but required a much lower computational cost. The GWO presented an error of 1% in the dynamic response using the corresponding indicators. If a more accurate parametric estimation is required, it is recommended to use GWO; however, the heuristic algorithm performed better overall. The performance of the algorithms presented in this paper may change if different error functions are used.

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Section: Discussionmentioning

confidence: 99%

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Munciño,

Damian-Ramírez,

Cruz-Fernández

et al. 2024

Algorithms

View full text Add to dashboard Cite

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“…However, improving only one performance metric at a time may have adverse effects on the other indices. On the other hand, multi-objective optimization provides a set of optimal solutions, which considers the behaviour of more than one performance index [147]. Therefore, multi-objective optimization can provide better SRM designs from different perspectives compared to the single-objective optimization to fit the needs of the different applications [112], [148].…”

Section: D) Multi-objective Optimization Design Of Srmsmentioning

confidence: 99%

Geometry and Topology Optimization of Switched Reluctance Machines: A Review

Abdalmagid

Sayed²,

Bakr

et al. 2022

IEEE Access

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Switched reluctance machines (SRMs) have recently attracted more interest in many applications due to the volatile prices of rare-earth permanent magnets (PMs) used in permanent magnet synchronous machines (PMSMs). They also have rugged construction and can operate at high speeds and high temperatures. However, acoustic noise and high torque ripples, in addition to the relatively low torque density, present significant challenges. Geometry and topology optimization are applied to overcome these challenges and enable SRMs to compete with PMSMs. Key geometric design parameters are optimized to minimize various objective functions within geometry optimization. On the other hand, the material distribution in a particular design space within the machine domain may be optimized using topology optimization. We discuss how these techniques are applied to optimize the geometries and topologies of SRMs to enhance machine performance. As optimizing the machine geometry and material distribution at the design phase is of substantial significance, this work offers a comprehensive literature review on the current state of the art and the possible trends in the optimization techniques of SRMs. The paper also reviews different configurations of SRMs and stochastic and deterministic optimization techniques utilized in optimizing different configurations of the machine.

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“…Design parameters improves average torque and effectiveness with reduced iron weight. In [50] Multi-objective Jaya method is used to optimize SRM design in which greater degree of variety, as seen by the findings and 8/6,6/4 were optimized using the Mo-JAYA algorithm. The Mo-JAYA results are compared to the non-dominated sorting genetic algorithm (NSGA-11).…”

Section: Review Of Previous Workmentioning

confidence: 99%

Recent Trends in Minimization of Torque Ripples in Switched Reluctance Machine

Deepa¹,

Krishnaveni²,

Mallesham³

2022

Preprint

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Increased definite power, reduced cost, and dynamic construction are the important features required by electric motors for various applications. One such motor accommodating all the requisites mentioned above is Switched reluctance. Applications pertaining to small, commercial, and EV find Switched reluctance machine as a developing competitor to the conventional induction and permanent magnet motors. Though SRMS contribute a considerable number of advantages over induction motors and permanent magnet motors, the primary disadvantages pertaining to SRMs are the torque ripple, especially at high-speed operation, and the resulting acoustic noise. These disadvantages may not always be detrimental to the systems in entirely all cases, but depending on the application to be employed, torque ripple and acoustic noise form are detrimental to the system. This paper reviews the technology status and recent trends in the minimization of torque ripples switched reluctance machines.

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Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya)

Cited by 8 publications

References 26 publications

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Geometry and Topology Optimization of Switched Reluctance Machines: A Review

Recent Trends in Minimization of Torque Ripples in Switched Reluctance Machine

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