Slip-Acceleration Based AI Approach for Islanding Detection

The development of Phasor Measurement Units (PMUs) in power system improves the possibilities of analyzing and monitoring the system dynamics. PMU data with its high sampling rate contains crucial information about the state of power system. Hence effective PMU data analysis can provide solution for most of the power system problems. This work proposes a data analysis method on PMU data. This combines the simplicity of passive islanding detection techniques and the robustness of artificial intelligence methods. PMU measurements are analyzed and classified to detect power system islanding. Artificial Neural Networks (ANN) and Support Vector Machines (SVM) algorithms are used to classify islanding and non-islanding events. Both the models classified the events correctly with good accuracy. The test results also show a better performance for SVM classifier compared to ANN classifier.