Advanced adaptive overcurrent protection scheme for smart distribution networks based on short-term load forecasting

This paper proposes an adaptive Overcurrent (OC) relay protection scheme that combines load forecasting and clustering-based operating state identification to dynamically adjust relay settings. A Long Short-Term Memory (LSTM) model predicts feeder current, allowing the system to proactively select appropriate relay setting groups for different load conditions. The method is validated on the IEEE 33-bus distribution network. Results show that the forecasting model achieves an Mean Absolute Percentage Error (MAPE) of 2.94 %, while the relay settings derived from predicted loads closely match those obtained from actual data. The difference in operating time is negligible between forecast-based and actual coordination and a safety protection mechanism is employed to deal with the forecast or classification errors by switching to a safe backup protection group. These results demonstrate the effectiveness of the proposed approach in maintaining reliable protection performance under changing operating conditions through SCADA, and are activated using IEC 61,850 protocols and GOOSE-based group switching mechanisms. The time-domain analysis shows a worst-case timing deviation below 41 ms, well within the 300 ms Coordination Time Interval (CTI) requirement.