Using SHAP values and machine learning to understand trends in the transient stability limit

Machine learning (ML) for transient stability assessment has gained traction due to the significant increase in computational requirements as renewables connect to power systems. To achieve a high degree of accuracy; black-box ML models are often required – inhibiting interpretation of predictions and consequently reducing confidence in the use of such methods. This paper proposes the use of SHapley Additive exPlanations (SHAP) – a unifying interpretability framework based on Shapley values from cooperative game theory – to provide insights into ML models that are trained to predict critical clearing time (CCT). We use SHAP to obtain explanations of location-specific ML models trained to predict CCT at each busbar on the network. This can provide unique insights into power system variables influencing the entire stability boundary under increasing system complexity and uncertainty. Subsequently, the covariance between a variable of interest and the corresponding SHAP values from each location-specific ML model – can reveal how a change in that variable impacts the stability boundary throughout the network. Such insights can inform planning and/or operational decisions. The case study provided demonstrates the method using a highly accurate opaque ML algorithm in the IEEE 39-bus test network with Type IV wind generation.