Augmenting Distance Protection With Information Derived From A Data-Based Approach To Fault Classification Under Uncertainty

—The increasing use of grid-connected converters challenges the dependability and security of distance protection. Unlike synchronous machines, converters provide limited and control-dependent fault currents distorting apparent impedance trajectories. In combination with low short-circuit strength and varying pre-fault operating states, this increases the risk of misoperation of conventional distance relays, particularly near the Zone 1 boundary. This work applies Random Forest classification to the IEEE 14-bus system under varying system and operating conditions. The classifier reaches 93.6% accuracy for 3ph-gnd faults and reveals systematic misclassifications. SHAP analysis identifies apparent impedance, pre-fault power flow and system strength as dominant drivers, linking data-driven results to classical protection concepts. Threshold and receiver–operating characteristic analyses show that optimal operating points deviate from fixed reach settings and shift the balance between dependability and security. A 90.08% reach performs best for 3ph-gnd faults, while 77.58% improves 1ph-gnd performance, indicating that static geometric zones cannot ensure balanced protection performance.