Prediction of Relay Settings in an Adaptive Protection System

Communication-assisted adaptive protection can improve the speed and selectivity of the protection system. However, in the event, that communication is disrupted to the relays from the centralized adaptive protection system, predicting the local relay protection settings is a viable alternative. This work evaluates the potential for machine learning to overcome these challenges by using the Prophet algorithm programmed into each relay to individually predict the time-dial (TDS) and pickup current (I PICKUP ) settings. A modified IEEE 123 feeder was used to generate the data needed to train and test the Prophet algorithm to individually predict the TDS and I PICKUP settings. The models were evaluated using the mean average percentage error (MAPE) and the root mean squared error (RMSE) as metrics. The results show that the algorithms could accurately predict I PICKUP setting with an average MAPE accuracy of 99.961%, and the TDS setting with a average MAPE accuracy of 94.32% which is sufficient for protection parameter prediction.