Research on Constructing an Anomaly Detection Model for Power Networks in Peak Electricity Consumption Scenarios Based on Random Matrix Theory
With the development of new power systems,the scale of power networks is expanding day by day,and traditional power network anomaly detection models can no longer meet the current needs.This study proposes a power network anomaly detec-tion model based on random matrix theory.The experimental results show that under low signal-to-noise ratio conditions,when the dataset size is 900,the accuracy of the average spectral radius method,maximum eigenvalue method,and sample covariance matrix are 0.746,0.764,and 0.788,respectively.In the case of high signal-to-noise ratio,the accuracy of the three methods is 0.921,0.934,and 0.947,respectively.Under high signal-to-noise ratio,when the number of abnormal nodes is 5,the response times of the average spectral radius method,maximum eigenvalue method,and sample covariance matrix method are 3.1,2.6,and 2.1,re-spectively.In the case of low signal-to-noise ratio,the response times of the three methods are 4.0,3.7,and 2.8,respectively.The research results indicate that the proposed method can provide better guarantees for the stable operation of the power network.
random matrix theoryabnormal detectionsample covariance matrixmaximum eigenvalue
万方数据
维普
