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

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.