A novel approach to identify regional fault of urban power grid based on collective anomaly detection
by Xiaodi Huang; Minglun Ren; Zhongfeng Hu
International Journal of Modelling, Identification and Control (IJMIC), Vol. 36, No. 1, 2020

Abstract: As a classical data form, the collective anomaly is used to describe the abnormality which cannot be identified by individual data. According to the data characteristics of current signals in the urban power grid, this paper proposes a novel detection approach, which transforms the diagnosis of regional fault into the detection of collective anomaly from the data of current fluctuation signal. Besides, in the proposed approach, an improved multi-layered clustering algorithm based on fixed point iteration (FPIML-clustering algorithm) is designed to enhance the detection efficiency. The experiment is tested on the power grid operation data of a Chinese city. The results demonstrate that the proposed approach can be used to detect regional faults before they reveal obvious fault characteristics.

Online publication date: Tue, 01-Jun-2021

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