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Under Voltage Load Shedding using Hybrid Metaheuristic Algorithms for Voltage Stability Enhancement: A Review

机译:使用混合元启发式算法提高电压稳定性的欠压减载:综述

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Power blackouts are experienced globally, more so with increasing load demand and ageing infrastructure. The high failure rate of conventional and adaptive load shedding techniques is prevalent during multiple contingencies. This paper analyses existing UVLS tools, and the potential of hybrid computational intelligence techniques (CIT) to optimally solve the voltage instability problem. Researchers have implemented UVLS with single-solution and population-based algorithms, bringing out strengths and limitations of different methods. Features like: (1) accuracy in load shedding amount, (2) ease of handling of multi-objective functions, and (3) speed of convergence are desired in modern power systems to maintain voltage stability. This paper, therefore, explores the implication of hybridizing metaheuristic algorithms to achieve optimal solutions, while enhancing voltage stability post-contingency.
机译:全球都有断电的情况,随着负载需求的增加和基础设施的老化,情况尤其如此。在多种意外情况下,传统和自适应减载技术的高故障率非常普遍。本文分析了现有的UVLS工具,以及利用混合计算智能技术(CIT)来最佳解决电压不稳定性问题的潜力。研究人员已通过单一解决方案和基于人群的算法实现了UVLS,从而展示了不同方法的优势和局限性。诸如以下的功能:(1)减载量的准确性;(2)易于处理的多目标函数;以及(3)在现代电力系统中,为了保持电压稳定性,需要收敛速度。因此,本文探讨了混合元启发式算法对实现最佳解决方案的意义,同时增强了意外情况后的电压稳定性。

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