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Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer Variants for Permanent Magnet Synchronous Motor Models Parameters Estimation
Iranian Journal of Science and Technology, Transactions of Electrical Engineering ( IF 1.5 ) Pub Date : 2019-12-06 , DOI: 10.1007/s40998-019-00294-4
Dalia Yousri , Dalia Allam , M. B. Eteiba , Ponnuthurai Nagaratnam Suganthan

Chaotic response in the permanent magnet synchronous motor (PMSM) is an undesirable performance that may affect motor stability. This unexpected behavior occurs due to the disruption in the system parameters and load disturbance. To control this unwanted performance, it’s essential to introduce a robust method to determine the PMSM model parameters efficiently and expeditiously. Two PMSM models depending on the type of its nonlinear differential equations are investigated. The first one is the integer-order PMSM model, while the other is the fractional-order model. In this work, novel developed optimization variants called chaotic heterogeneous comprehensive learning particle swarm optimizer (CHCLPSO) is proposed. In CHCLPSO, the standard heterogeneous comprehensive learning particle swarm optimizer (HCLPSO) is cooperated into ten different chaos maps to adjust some of its parameters. Six CHCLPSO variables are introduced in addition to the standard HCLPSO version to estimate the parameters of the integer-order and the fractional-order PMSM models that are corresponding to the chaotic behavior. A comparison among the results of the introduced variants and the original algorithm is carried out. Moreover, a comprehensive comparison with other recent algorithms is performed. The primary outcome proves that the chaos maps have a remarkable influence in both of the consistency and the accuracy of the results of the HCLPSO with less execution time over the integer and the fractional-order models, especially CHCLPSO-III and CHCLPSO-V with sinusoidal and piecewise maps, respectively.

中文翻译:

永磁同步电机模型参数估计的混沌异构综合学习粒子群优化器变体

永磁同步电机 (PMSM) 中的混沌响应是一种不良性能,可能会影响电机稳定性。这种意外行为的发生是由于系统参数中断和负载扰动所致。为了控制这种不需要的性能,必须引入一种稳健的方法来高效快速地确定 PMSM 模型参数。根据其非线性微分方程的类型研究了两种 PMSM 模型。第一个是整数阶 PMSM 模型,而另一个是分数阶模型。在这项工作中,提出了称为混沌异构综合学习粒子群优化器(CHCLPSO)的新型开发优化变体。在 CHCLPSO 中,标准的异构综合学习粒子群优化器(HCLPSO)被协作成十个不同的混沌图来调整其一些参数。除了标准的 HCLPSO 版本之外,还引入了六个 CHCLPSO 变量来估计与混沌行为相对应的整数阶和分数阶 PMSM 模型的参数。对引入的变体和原始算法的结果进行比较。此外,还与其他最新算法进行了全面比较。主要结果证明,混沌图对具有较少执行时间的 HCLPSO 结果的一致性和准确性都有显着影响,对整数和分数阶模型,尤其是 CHCLPSO-III 和 CHCLPSO-V 正弦模型。和分段映射,
更新日期:2019-12-06
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