Method for Real-Time Abnormal State Detection of a Distribution Network Based on Maximum and Minimum Eigenvalues

The state analysis method of a traditional distribution network operation is strictly dependent on the physical model of itself, but it varies as the geography changes, and it is difficult to find the abnormal state of a district network on real-time, especially the sudden change caused by the distributed energy and EV load. So, a method of the abnormal state detecting for the distribution network is proposed based on the maximum and minimum eigenvalues. Firstly, a high-dimensional random matrix is established by the big data from the distribution network management system to take abnormal state detection through a real-time sliding window. Then, the maximum and minimum eigenvalues of the distribution network are gained by calculating the sample covariance matrix of the random matrix and determining the maximum and minimum eigenvalues of the latter matrix. Finally, an 1177-node testing system was taken as an example, and the simulation results showed that the proposed method could detect the abnormal state in real-time without depending on the physical model and fault type of the grid.