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      Incipient Fault Detection and Location in Distribution Networks: A Data-Driven Approach

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          Abstract

          In this paper, a data-driven Methodology is developed for the incipient fault detection and location in distribution networks. First, the online monitoring data in the distribution network is analyzed and processed. This makes an amplification of observed sudden changes and abnormal fluctuations in data. Based on the random matrix theory for the spectrum analyzes of the spatio-temporal data matrices among multiple-time-instant monitoring devices, a real-time incipient fault detection and location algorithm is designed. During which, the linear eigenvalue statistics are defined to indicate data behavior, and the fault latencies are analyzed and located. As for those low-dimensional data matrices formed in the distribution network, by using tensor product, an increasing data dimension approach is developed to meet the prerequisites for using random matrix theory. Theoretical justifications for the algorithm is provided through Marchenko-Pastur Law and Ring Law. Case studies on the synthetic data from IEEE standard bus system and real-world online monitoring data in a power grid corroborate the feasibility and effectiveness of the proposed methodology.

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          Model-order selection

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            The single ring theorem

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              Dimensionality Reduction of Synchrophasor Data for Early Event Detection: Linearized Analysis

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                Author and article information

                Journal
                05 January 2018
                Article
                1801.01669
                46776559-02a7-4087-ab4b-91e77accb1e0

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                11 pages
                stat.AP stat.ME

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