Cuckoo Search Algorithm for Multi-Objective Optimization of Transient Starting Characteristics of a Self-Starting HVPMSM

Wang, Likun; Guo, Hai; Marignetti, Fabrizio; Shaver, Clark D.

doi:10.1109/tec.2020.3046650

Cited by 10 publications

(1 citation statement)

References 38 publications

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“…where t is the number of the iteration, α > 0 is the step size scaling factor of the number, κ(λ) is the Lévy flight coefficient, and x b is the position of the best nest. The random walk coefficient κ(λ) is drawn from a Lévy distribution [26,29] and calculated by Equation (2) [30]:…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Minimization of Torque Ripple in the Brushless DC Motor Using Constrained Cuckoo Search Algorithm

2021

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This paper presents the application of the cuckoo search (CS) algorithm in attempts to the minimization of the commutation torque ripple in the brushless DC motor (BLDC). The optimization algorithm was created based on the cuckoo’s reproductive behavior. The lumped-parameters mathematical model of the BLDC motor was developed. The values of self-inductances, mutual inductances, and back-electromotive force waveforms applied in the mathematical model were calculated by the use of the finite element method. The optimization algorithm was developed in Python 3.8. The CS algorithm was coupled with the static penalty function. During the optimization process, the shape of the voltage supplying the stator windings was determined to minimize the commutation torque ripple. Selected results of computer simulation are presented and discussed.

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Section: Cuckoo Search Algorithmmentioning

confidence: 99%