Novel scientific design of hybrid opposition based—Chaotic little golden-mantled flying fox, White-winged chough search optimization algorithm for real power loss reduction and voltage stability expansion

L. Kanagasabai
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Abstract

In this paper hybrid opposition based—Chaotic little golden-mantled flying fox algorithm and White-winged chough search optimization algorithm (HLFWC) is applied to solve the loss dwindling problem. Key objective of the paper is real power loss reduction, voltage deviation minimization and voltage stability expansion. Proposed little golden-mantled flying fox algorithm is designed based on the deeds of the little golden-mantled flying fox. Maximum classes have single progenies at a period afterwards of prenatal period. This little procreative production means that when populace forfeiture their figures are deliberate to ricochet. In White-winged chough search optimization algorithm magnifying the encumbrance element in a definite assortment will pointedly enlarge the exploration region. In a coiled exploration, the position of any White-winged chough can differ in numerous scopes to cover the exploration region, predominantly in the projected problem. Hybrid opposition based—Chaotic little golden-mantled flying fox algorithm and White-winged chough search optimization algorithm (HLFWC) is accomplished by integrating the actions of little golden-mantled flying fox and White-winged chough. Through the hybridization of both algorithms exploration and exploitation has been balanced throughout the procedure. Proposed hybrid opposition based—Chaotic little golden-mantled flying fox algorithm and White-winged chough search optimization algorithm (HLFWC) is corroborated in IEEE 30 and 57 systems. From the simulation results it has been observed that real power loss reduction, voltage deviation minimization and voltage stability expansion has been achieved.
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科学设计基于混沌小金毛狐、白翅白鸦搜索的混合对抗优化算法,降低实际功率损耗,扩大电压稳定性
本文采用基于混合对抗的混沌小金毛狐算法和白翅鸦搜索优化算法(HLFWC)来解决损失减小问题。本文的主要目标是降低实际功率损耗、减小电压偏差和扩大电压稳定性。基于小金毛飞狐的行为,设计了小金毛飞狐算法。大多数班级在产前期之后的一段时间内只有一个后代。这种小的生产性生产意味着,当民众没收他们的数字是故意反弹。在白翅鸟搜索优化算法中,在确定的分类中,增大妨碍元素,可以有针对性地扩大搜索区域。在盘绕勘探中,任何白翅鸦的位置可以在覆盖勘探区域的许多范围内变化,主要是在预测问题中。基于混合对抗的混沌小金毛狐算法和白翅白嘴鸦搜索优化算法(HLFWC)是将小金毛狐和白翅白嘴鸦的行动结合起来实现的。通过两种算法的杂交,探索和开发在整个过程中得到了平衡。在IEEE 30和57系统中验证了基于混沌小金毛狐算法和白翅鸦搜索优化算法(HLFWC)的混合对抗。仿真结果表明,该方法降低了实际功率损耗,减小了电压偏差,扩大了电压稳定性。
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25
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