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首页> 外文期刊>Smart Grid, IEEE Transactions on >Correlation Clustering Imputation for Diagnosing Attacks and Faults With Missing Power Grid Data
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Correlation Clustering Imputation for Diagnosing Attacks and Faults With Missing Power Grid Data

机译：相关聚类插补，可在缺少电网数据的情况下诊断攻击和故障

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摘要

While the quality of the synchronized measurements is of paramount importance for real-time monitoring and protection of the power grids, collected measurements often contain missing values. This paper proposes a scheme for diagnosing attacks and faults in the presence of missing measurements in power grid data. The proposed scheme contains four modules for clustering, missing data imputation, decision-making, and optimization. This paper develops a novel technique for missing data imputation based on the correlation-connected clusters that consider local correlation among the measurements in estimating missing data, handle high-dimensional data, and tolerate high missing ratios. The optimization module ties the imputation process to diagnostic performance. The proposed novel imputation technique is compared with other state-of-the-art techniques within the diagnostic scheme. The achieved results show that the proposed technique significantly outperforms other competitors.
机译：尽管同步测量的质量对于实时监视和保护电网至关重要，但收集的测量通常包含缺失值。本文提出了一种在电网数据中缺少测量值的情况下诊断攻击和故障的方案。提议的方案包含四个模块，用于聚类，缺失数据插补，决策和优化。本文开发了一种基于相关连接的聚类的缺失数据归因的新技术，该聚类在估计缺失数据，处理高维数据和容忍高缺失率时考虑了度量之间的局部相关性。优化模块将插补过程与诊断性能联系在一起。在诊断方案中，将提出的新颖插补技术与其他最新技术进行了比较。取得的结果表明，所提出的技术明显优于其他竞争者。

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