A Machine Learning-Based Strategy For Predicting The Fault Recovery Duration Class In Electric Power Transmission System

Energy security program which becomes the part of energy management must ensure the high reliability of the electric power transmission system so that the customer can be served very well. However, there are several problems that can hinder reliability achievement such as the long duration of fault recovery. On the other side, the prediction of fault recovery duration becomes a very challenging task. Because there are still few machine learning-based solution offer this paper proposes a machine learning-based strategy by using Naive-Bayes Classifier (NBC) and Support Vector Machine (SVM) in predicting the fault recovery duration class. The dataset contains 3398 rows of non-temporary-fault type records, six input features (Substation, Asset Type, Fault Category, Outage Start Time, Outage Day, and Outage Month) and single target feature (Fault Recovery Duration). According to the performance test result, those two methods reach around 97-99% of accuracy, average sensitivity, and average specificity. In addition, one of the advantages obtained in field of fault recovery prediction is increasing the accuracy of likelihood level calculation of the long fault recovery time risk.