Deep Learning-Based Cascade Failure Prediction for Transmission Lines at Risk in an IoT-based Power Grid Monitoring Framework

Under an internet of thing (IoT)-based framework, a power grid monitoring system can collect real-time transmission line data, which can then be used to predict the cascading failure risks within the grid. This study proposed a two-stage cascade failure prediction (CFP) model that integrated deep learning techniques with supervised classification algorithms to identify transmission lines susceptible to cascading failures, thereby preventing large-scale power system collapses. In the first-stage, the CFP model utilizes historical transmission line data to predict lines at risk in the next time step. In the second-stage, it assesses whether the predicted at-risk lines could trigger system instability or power outages. Three deep learning-based algorithms were employed in the first-stage, whereas three binary classification algorithms were utilized in the second-stage. The proposed CFP model was evaluated using an IEEE 39-bus system, where 2,000 cascading failure events were simulated through a real-time digital simulator (RTDS). Experimental results demonstrate that the two-stage CFP model, particularly when combining a deep neural network with a binary classifier, can accurately identify potential risk lines in the test grid with an accuracy ranging from 97.5% to 98.3%. Moreover, the CFP model exhibits strong robustness against noisy and erroneous data. These predictive results provide valuable insights for power companies in formulating preventive safety strategies against cascading failures.