Real-Time Cascading Failure Risk Evaluation With High Penetration of Renewable Energy Based on a Graph Convolutional Network

Abstract

When renewable energy penetration increases and the power network becomes more complex, vulnerability and security concerns typically also become more prevalent. A proper evaluation method focused on risk prediction and failure identification is required in modern power systems. In our work, a novel evaluation framework with high efficiency and accuracy is proposed, where the influence of over/under-voltage on renewable energy generators is considered during the cascading failure risk evaluation process. A graph convolutional network and a long short-term memory network are applied to describe the complex network topological structure and extract the system's electrical features, respectively. The neural network model trained with offline simulation data can reduce the numerical effort of evaluating cascading failure risk in real-time. Finally, the proposed method is verified based on the benchmark of both an IEEE 39-bus test system and a real-world French power system. The results show that the proposed cascade neural network model can achieve better evaluation results with shorter training and evaluation times.