Evaluation of islanding detection techniques for inverter-based distributed generation

In this paper; four islanding detection techniques for inverter-based distributed generator (DG) are presented. The techniques are: decision tree (DT), support vector machine (SVM), radial basis function network (RBF), and probabilistic neural network (PNN). In literature, these techniques were proposed as islanding detection methods. However, the proposed techniques face various limitations such as the size and type of the used distribution network and the limitation of the extracted features. This paper overcomes these limitations and gives a very accurate comparison between these techniques by extracting seven features from damped-sinusoid model of the voltage and frequency waveforms using the MATLAB/SIMULINK and also using the IEEE 34-bus distribution system. The results show that out of the four tested techniques, PNN technique can accurately detect islanding for inverter based DG.