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首页> 外文期刊>Smart Grid, IEEE Transactions on >Data-Driven Islanding Detection Using a Principal Subspace of Voltage Angle Differences
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Data-Driven Islanding Detection Using a Principal Subspace of Voltage Angle Differences

机译：数据驱动孤岛检测使用电压角差的主要子空间

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摘要

The likelihood of an unintentional power system islanding is increased in systems with significant penetration of distributed generation. To mitigate the adverse effects of islanding, a quick and reliable islanding detection method is needed. This paper first analyzes covariance matrices of a linearized power system model, and relates them to the principal component analysis of experimentally obtained covariance matrices. Additionally, a new model-independent islanding detection method is proposed that uses measurements of voltage angle differences between multiple locations in the system. The angle differences are first preprocessed to remove the effects of nonstationarity. Thereafter, a probabilistic model of principal component analysis is trained using the acquired measurements. The principal and residual spaces extracted from the measurements are used to discriminate between islanding and other events in the system. The applicability of the proposed method is demonstrated by using real measurements gathered from several locations in a transmission grid.
机译：在具有显着渗透发电的系统中，无意发电系统岛的可能性增加。为了减轻岛屿的不利影响，需要一种快速可靠的岛屿检测方法。本文首先分析线性化电力系统模型的协方差矩阵，并将它们与实验获得的协方差矩阵的主要成分分析相关。另外，提出了一种新的独立模型的孤岛检测方法，其使用系统中多个位置之间的电压角差的测量值。首先预处理的角度差以消除非间抗性的效果。此后，使用所获取的测量训练主成分分析的概率模型。从测量中提取的主体和残余空间用于区分系统中的岛屿和其他事件。通过使用从传输网格中的若干位置收集的真实测量来证明所提出的方法的适用性。

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