Cascading Fault Early Warning and Location Method of Transmission Networks Based on Wide Area Time-Series Power System State
Zeyu Mu, Peidong Xu, Kelly Zhang, Tianlu Gao, Jun Zhang · IEEE Journal of Radio Frequency Identification · 2022
With the promotion of energy security strategy and the access of the high penetration of renewable energy, the related methods of situational awareness in the traditional transmission network may be inapplicable for modern power grids. For the early warning and location of the cascading faults caused by power flow shift, a novel method for security situation prediction in the transmission network is put forward based on Long Short Term Memory. Firstly, the power flow model is used to construct the early warning framework of transmission network security situation based on LSTM, and ADASYN oversampling is used for sample equilibrium. Secondly, the validity of the proposed method is verified by the open-source platform “Grid2Op” in the modified IEEE 36-bus system. Results indicate that the early warning accuracy for cascading failure can achieve around 90% with prediction time less than 1 second. Various simulation results demonstrate that the proposed method can improve the accuracy of security situation warning, shorten the time for identifying abnormality of the transmission network and provide reliable support for the operation and maintenance of the transmission network.