Title: A novel approach to identify regional fault of urban power grid based on collective anomaly detection

Authors: Xiaodi Huang; Minglun Ren; Zhongfeng Hu

Addresses: School of Economic and Management, Hefei University, 230601, Hefei, China ' School of Management, Hefei University of Technology, 230009, Hefei, China ' School of Economic and Management, Hefei University, 230601, Hefei, China

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.

Keywords: urban power grid; regional fault; collective anomaly; multi-layered clustering; fixed point iteration.

DOI: 10.1504/IJMIC.2020.115389

International Journal of Modelling, Identification and Control, 2020 Vol.36 No.1, pp.78 - 87

Received: 13 Dec 2019
Accepted: 26 Jul 2020

Published online: 27 May 2021 *

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