Time Series Analysis of Load Loss in Power Outage Accidents of Power Grid in China Based on the Visibility Graph

Attempt to use visual methods to transform the collected historical power outage loss load time series into a complex network, and provide new ideas for mining the spatiotemporal distribution characteristics of power outage accidents by analyzing the importance of accident nodes. Firstly, using the rescaled range method, the Hurst exponent of the time series of the relative values of power outage losses in China from 1981 to 2020 was calculated to be 0.7533, indicating that it has a long-range positive correlation and is a predictable fractal time series; Then, based on the visual graph algorithm, the time series was transformed into a complex network graph and subjected to topological analysis, revealing its typical self-organizing characteristics and small world effect; Finally, the approximate ideal sorting method was applied to rank the importance of the spatiotemporal distribution of power outages in the Chinese power grid. The key nodes were statistically analyzed by time period, month, and region. The results showed that most of the key nodes of power outages in the Chinese power grid were concentrated between 2001 and 2010, in July, and mostly located in the Northeast power grid. This indicates that during the rapid development of power system in China from 2001 to 2010, the stability of the power grid decreased due to the large-scale integration of new energy, frequent extreme weather events, and increased temperature regulation loads.