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Exploiting Compressive System Identification for Multiple Line Outage Detection in Smart Grids

机译:利用压缩系统识别技术在智能电网中进行多线故障检测

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Due to system complexity and structural variations, real time power line outage detection (POD) and localization is a critical and challenging monitoring goal for modern smart grid (SG). Online monitoring of power lines status plays a major role in system-wide cascading blackout prevention. In this work, we aim to address the multiple POD problems by exploiting the compressive system identification (CSI) - a time efficient approach in complex network analysis. We consider a power network (PN) as a single graph and the mathematical formulation of POD is initialized using the DC power-flow model and graph theory concepts. The POD sparse recovery problem (POD-SRP) reported earlier is improved and generalized in case of large-scale multiple outages. The technical challenges from sparse recovery perspective are addressed through developing new SRP solvers. Moreover, a new sparse-based mathematical formulation for POD is termed as `Binary-POD-SRP' to specifically address the signal dynamic range issue. Finally, using the IEEE standard test-beds, it is discussed how the inherent challenges within large-scale multiple-outages can be solved by applying these new techniques and formulations.
机译:由于系统的复杂性和结构上的变化,实时电力线中断检测(POD)和本地化是现代智能电网(SG)的一项至关重要且具有挑战性的监测目标。电力线状态的在线监视在全系统级联停电预防中起着重要作用。在这项工作中,我们旨在通过利用压缩系统识别(CSI)(复杂网络分析中的一种省时方法)来解决多个POD问题。我们将电网(PN)视为单个图形,并使用DC潮流模型和图形理论概念来初始化POD的数学公式。先前报告的POD稀疏恢复问题(POD-SRP)在大规模多次中断的情况下得到了改善和推广。通过开发新的SRP求解器可以解决从稀疏恢复角度来看的技术挑战。此外,一种新的基于稀疏的POD数学公式被称为“二进制POD-SRP”,专门解决信号动态范围问题。最后,使用IEEE标准测试台,讨论了如何通过应用这些新技术和公式来解决大规模多次停机中的固有挑战。

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