A comparative analysis of intelligent classifiers for passive islanding detection in microgrids

This paper proposes a passive islanding detection technique for distributed generations in grid-connected microgrids and presents a comprehensive comparative analysis of intelligent classifiers for passive islanding detection application. The proposed method utilizes pattern recognition techniques in classification of underlying signatures of wide variety of system events on critical system parameters for islanding detection. Case study on a grid-connected microgrid model with different types of distributed generations is performed to evaluate the proposed method and compare the classifier performances. Test results demonstrate the effectiveness of the proposed method in detection of islanding events.