Real‐time risk assessment of cascading failure in power system with high proportion of renewable energy based on fault graph chains

Abstract

Abstract The access of a high proportion of renewable energies has deepened the randomness and complexity of cascading failures (CFs) in power systems. In this regard, a real‐time risk assessment method for CFs in power systems with high proportion of new energy is proposed. First, combined with historical statistical data and relevant national standards, a CF simulation model that considers the off‐grid protection action of renewable energy units in the event of a power grid fault is proposed. The model is based on the continuous steady‐state power flow model, which simulates the spread of CFs via continuous power flow calculations. Second, via introducing the concept of a fault graph chain, the electrical and topological characteristics of the continuous dynamic of the power system in the process of CFs can be described. Then, through continuous CF simulation and replay buffer, a data‐driven method is used to calculate the CF risk index corresponding to the fault graph. Finally, a cascaded graph neural network is employed to fit the nonlinear mapping relationship between fault graphs and CF risk indicators. The simulation results in the IEEE 39‐bus system show that the proposed method can accurately and real‐time evaluate the risk of CFs.

Keywords

• cascading failure
• data driven
• fault chain
• graph neural network
• high proportion of renewable energy
• risk assessment