Machine learning based classifiers for dynamic and transient disturbance classification in smart microgrid system

The smart microgrid system should have the ability to rapidly detect and classify every type of disturbance that happens in the network to operate the protection scheme and maintain the power quality. Both dynamic and transient types of disturbances are considered in this study, and the classification of each type of disturbance has been done using different machine learning-based techniques. A novel ensemble classifier has been proposed after comparing the performance of the decision tree (DT), k nearest neighbor (KNN), support vector machine (SVM), and ensemble classifier. The performances of all the classifiers have been evaluated in terms of accuracy, sensitivity, confusion matrices, and receiver operating characteristics (ROC). A very satisfactory result has been obtained in the ensemble tree classification technique with 99.3% accuracy in 2.1 s of training time. Therefore, it can be suggested to identify and classify every transient as well as steady-state events of the microgrid network.