Self-Organized Criticality and Trend Analysis in Time Series of Blackouts for the China Power Grid

Abstract

This paper proposes effective evidence on the correlation between trend and self-organized criticality (SOC) of the power outage sequence in China. Taking the data series of blackouts from 1981 to 2014 in the China power grid as the research object, the method of V/S is introduced into the analysis of the power system blackout sequence to demonstrate their prominent long-time correlations. It also verifies the probability distribution of load loss about blackout size in the China power grid has a tail feature, which shows that the time series of blackouts in the China power grid is consistent with SOC. Meanwhile, a kind of mathematical statistics analysis is presented to prove that there is a seasonal trend of blackouts, and the blackout frequency and blackout size have not decreased over time but have an upward trend in the China power grid, thereby indicating that blackout risk may be increasing with time. The last 34 years’ data samples of power failure accidents in the China power grid are used to test the proposed method, and the numerical results show that the proposed self-organized criticality and trend analysis method can pave the way for further exploration of the mechanism of power failure in the China power grid.

Technology for Power Outage Research and Judgment-dependent Data Feature Noise Analysis

“…Yu Qun et al (2018) delved into the autocorrelation of power grid blackout incidents, analyzing decades of power outage data and identifying long-range autocorrelations within the loss load sequence. Their research facilitated time-series predictions of blackout incidents and associated loss loads [4]. These studies have tackled the issue of power outages in distribution networks from diverse angles and dimensions [5].…”