Robust Power Line Outage Detection with Unreliable Phasor Measurements
Robust Power Line Outage Detection with Unreliable Phasor Measurements
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DOI：
10.1109/icde.2017.173
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
Jose Cordova-Garcia;Xin Wang-
中科院分区：
文献类型：
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作者：
Jose Cordova-Garcia;Xin Wang-
相量测量单元（pmu）以高采样率提供高精度数据，以支持智能电网应用。电力线停电检测机制可以帮助电力运营商采取适当的控制措施，从而提高电网的可靠性。尽管pmu提供了潜力，但在利用可用数据更有效地检测中断方面的努力非常有限。传统的停电检测方案大多是基于简化的功率模型设计的，基于数据的检测工作要么假设所有的测量样本都可用，要么忽略缺失的条目。在存在数据缺失的复杂网格条件下，它们的性能会受到影响。在本文中，我们设计了一种考虑不可靠数据的检测机制，以缺失数据样本的形式。检测通过根据节点的数据可用性和学习到的检测能力对节点进行分组来执行。为了实现对电力线中断的鲁棒检测，我们建议学习每个单独节点的中断特征，而不是特定的单线中断场景。结果表明，在不同情况下，检测到的故障与评估的故障高度一致，准确率高，误报率低。此外，检测应用程序对不可靠的数据具有弹性，并且可以正确区分数据问题和物理电源线故障。
Phasor Measurement Units (PMUs) provide high precision data at high sampling rates to support Smart Grid applications. Power Line Outage detection mechanisms can enhance the grid reliability by assisting power operators in taking proper control actions. Despite the potential provided by PMUs, there are very limited efforts on exploiting data available to more effectively detect outages. Conventional outage detection schemes are mostly designed based on simplified power models, and the limited work on detection with data either assume all the measurement samples are available or ignore the missing entries. Their performance suffers in the complex grid conditions in the presence of missing data. In this paper, we design a detection mechanism considering unreliable data, in the form of missing data samples. Detection is performed through the grouping of nodes according to their data availability and their learned detection capabilities. To enable the robust detection of power line outages, we propose learning outage characteristics for each individual node instead of specific single line outage scenarios. Our results show that the outages detected are highly consistent with the evaluated failures under different scenarios, with high accuracy and low false positive rates. Moreover, the detection application is resilient to unreliable data, and can properly differentiate data problems from physical power line failures.