Research and Application of Power Grid Data Anomaly Tracing Method Based on Time Series Correlation

Abstract

Abstract Power grid data is a “barometer” that reflects the operational trend of the power grid system, so conducting a traceability study on abnormal data generated by the power grid system is crucial to maintaining the stable operation of the power grid system and preventing further malfunctions. This article proposes a maximum time series correlation ring tracing algorithm based on time series correlation: firstly, for a large amount of time series data collected from measurement points in the power grid system, a correlation coefficient matrix is calculated, and then a time series correlation graph is constructed using graph theory knowledge. The Kruskal algorithm is used to search for the longest spanning tree in the time series correlation graph. Finally, based on the spanning tree, the breadth-first search (BFS) algorithm is further used to obtain the maximum time series correlation ring[1]. By using the time series correlation of each node within the ring and the physical topological relationship between nodes, the abnormal data can be traced back. Distinguish whether the generation of abnormal data is due to a single point failure or system failure. Most of the existing fault tracing techniques are based on machine learning algorithms, which are difficult to widely apply in high-dimensional time series data due to their high complexity.Through experiments on real power grid data, the results verify the effectiveness of this method in anomaly tracing of high-dimensional time series data. Through comparative experiments, this method is superior to machine learning model algorithms in terms of efficiency and stability. At the same time, this method does not require additional calculations of the statistical distribution of the samples to be tested, thus greatly saving computational costs.