  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.
