  Modeling protective relays is crucial for performing accurate stability
studies as they play a critical role in defining the dynamic responses of power
systems during disturbances. Nevertheless, due to the current limitations of
stability software and the challenges of keeping track of the changes in the
settings information of thousands of protective relays, modeling all the
protective relays in bulk power systems is a challenging task. Distance relays
are among the critical protection schemes, which are not properly modeled in
current practices of stability studies. This paper proposes a machine
learning-based method that uses the results of early-terminated stability
studies to identify the critical distance relays required to be modeled in
those studies. The algorithm used is the random forest (RF) classifier. GE
positive sequence load flow analysis (PSLF) software is used to perform
stability studies. The model is trained and tested on the Western Electricity
Coordinating Council (WECC) system data representing the 2018 summer peak load
under different operating conditions and topologies of the system. The results
show the great performance of the method in identifying the critical distance
relays. The results also show that only modeling the identified critical
distance relays suffices to perform accurate stability studies.
