Landmark operator inspired artificial bee colony algorithm for optimal vector control of induction motor

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
F. Sharma, S. R. Kapoor
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引用次数: 0

Abstract

In recent years, soft computing strategies have played vital role to solve optimisation problems associated with real world. In this paper, an efficient soft computing strategy namely, artificial bee colony algorithm (ABCalgo) is modified with incorporating landmark operator. The proposed modified ABC algorithm is named as landmark inspired ABC (LMABC). The performance of LMABC is evaluated on benchmark functions. Further, the proposed LMABC is applied for vector control of induction motor (IM) and subsequently to improve its efficiency. The vector control of IM includes control of magnitude and phase of each phase current and voltage. In this research paper the field orientated control, a digital implementation which demonstrates the capability of performing direct torque control, of handling system limitations and of achieving higher power conversion efficiency is considered. The obtained outcomes are significantly better than other state-of-art algorithms available in literature.
基于地标算子的感应电机最优矢量控制人工蜂群算法
近年来,软计算策略在解决与现实世界相关的优化问题中发挥了至关重要的作用。本文对一种有效的软计算策略——人工蜂群算法(ABCalgo)进行了改进,并加入了地标算子。提出的改进ABC算法被命名为地标启发ABC (LMABC)。在基准函数上对LMABC的性能进行了评价。并将该方法应用于异步电动机的矢量控制,提高了异步电动机的效率。IM的矢量控制包括对各相电流和电压的幅值和相位的控制。在本文的研究中,考虑了面向场的控制,一种数字实现,证明了直接转矩控制的能力,处理系统的局限性和实现更高的功率转换效率。所得结果明显优于文献中现有的其他最先进的算法。
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来源期刊
International Journal of Swarm Intelligence Research
International Journal of Swarm Intelligence Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.50
自引率
0.00%
发文量
76
期刊介绍: The mission of the International Journal of Swarm Intelligence Research (IJSIR) is to become a leading international and well-referred journal in swarm intelligence, nature-inspired optimization algorithms, and their applications. This journal publishes original and previously unpublished articles including research papers, survey papers, and application papers, to serve as a platform for facilitating and enhancing the information shared among researchers in swarm intelligence research areas ranging from algorithm developments to real-world applications.
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