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Machine learning (ML) for transient stability assessment has gained tractiondue to the significant increase in computational requirements as renewablesconnect to power systems. To achieve a high degree of accuracy; black-box MLmodels are often required - inhibiting interpretation of predictions andconsequently reducing confidence in the use of such methods. This paperproposes the use of SHapley Additive exPlanations (SHAP) - a unifyinginterpretability framework based on Shapley values from cooperative game theory- to provide insights into ML models that are trained to predict criticalclearing time (CCT). We use SHAP to obtain explanations of location-specific MLmodels trained to predict CCT at each busbar on the network. This can provideunique insights into power system variables influencing the entire stabilityboundary under increasing system complexity and uncertainty. Subsequently, thecovariance between a variable of interest and the corresponding SHAP valuesfrom each location-specific ML model - can reveal how a change in that variableimpacts the stability boundary throughout the network. Such insights can informplanning and/or operational decisions. The case study provided demonstrates themethod using a highly accurate opaque ML algorithm in the IEEE 39-bus testnetwork with Type IV wind generation.

Using SHAP Values and Machine Learning to Understand Trends in the Transient Stability Limit