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Efficient identification of critical parameters affecting the small-disturbance stability of power systems with variable uncertainty

机译:有效识别影响具有可变不确定性的电力系统小扰动稳定性的关键参数

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摘要

This paper implements an efficient sensitivity analysis (SA) technique to identify and rank critically important uncertain parameters that affect the small-disturbance stability of a power system. Identification and ranking of uncertain parameters are vital in modern power system operation due to the adoption of deregulated market structure and integration of intermittent energy resources and new types of loads. Ranking of critical uncertain parameters will facilitate better operation and control with less monitoring (targeted only on the parameters of interest) by system operators and stakeholders. The Morris screening method of sensitivity analysis has been described and implemented in this paper as the most suitable for this study based on comparison with various local and global techniques which highlighted the their comparative computational complexities and simulation time requirements. All methods have been illustrated using a modified version of the 68 bus NET-SNYPS test system. Illustrative results are provided considering varying levels of parameter uncertainties in order to establish not only the impact of system variability on parameter ranking, but also the robustness of the presented technique.
机译:本文实现了一种有效的灵敏度分析(SA)技术,以识别和排列影响电力系统小扰动稳定性的至关重要的不确定参数。由于采用了放松管制的市场结构以及整合了间歇性能源和新型负载,不确定参数的识别和排序在现代电力系统运行中至关重要。关键不确定参数的排名将有助于系统操作员和利益相关者进行更少的监视(仅针对感兴趣的参数),从而更好地进行操作和控制。在与各种本地和全局技术进行比较的基础上,本文描述并实施了敏感性分析的Morris筛选方法,这是最适合本研究的方法,强调了它们的比较计算复杂性和仿真时间要求。所有方法均使用68总线NET-SNYPS测试系统的修改版进行了说明。考虑到参数不确定性的变化水平,提供了说明性结果,不仅可以确定系统可变性对参数排名的影响,还可以确定所提出技术的鲁棒性。

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