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Transient Stability Prediction for Load Flow Cascading Models Using Random Forests

Many cascading methodologies tend to be steady-state load flow analyses to avoid the computational burden of examining cascades from a dynamic standpoint. Load flow, however, has the drawback of losing information about generator transient angular stability. On the other hand, time-domain simulations can accurately identify angular stability issues at the cost of extra computational burden. This work aims to incorporate time-domain angular stability in load-flow through machine learning. A Random Forest (RF) machine learning classifier is trained on generator transient angular instability data obtained from time-domain simulations with machine features derived from steady-state load-flow analysis. RFs require very little computation once trained and can have high predictive accuracy, resulting in a classifier that can be used in steady-state cascading methodologies to predict transient stability.