Augmenting distance protection with a data-based approach to fault classification under uncertainty
Year: 2026
Type: article
Abstract: 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.
Source: Electric Power Systems Research
Authors: Sarah Anhaus, Arlind Damjani, Willem Leterme
Institution: RWTH Aachen University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,810
OpenAlex ID: W7166331944