Detection of islanding using empirical mode decomposition and support vector machine

Abstrak

Accurate detection of islanding remains to be a challenge for grid connected microgrid system. An effective method to identify the islanding of microgrid has been presented which uses only the voltage at point of common coupling (PCC). Accurate islanding detection is necessary to impose appropriate control for the microgrid operation. Following the islanding of microgrid the intrinsic mode functions (IMF’s) of voltage at PCC obtained by empirical mode decomposition (EMD) will be analyzed by support vector machine (SVM) model which identifies the islanding of the microgrid. SVM model learns through the training data set. As many as 150 simulated cases have been used to train the SVM. A practical microgrid system has been simulated for various operating conditions and the data generation has been carried out by series of simulations for various islanding and non-islanding events using MATLAB Simulink. The proposed method gives optimistic results with high accuracy, zero non detection zone (NDZ) and detection time as low as 63.11 ms. Accurate islanding detection leads to smooth transition of microgrid control essential for operators.