This paper proposes a new islanding detection technique based on the combination of a wavelet packet transform (WPT) and a probabilistic neural network (PNN) for grid-tied photovoltaic systems. The point of common coupling (PCC) voltage is measured and processed by the WPT to find the normalized Shannon entropy (NSE) and the normalized logarithmic energy entropy (NLEE). Subsequently, the yield feature vectors are fed to the PNN classifier to classify the disturbances. The PNN is trained with different spread factors to obtain better classification accuracy. For the best performance of the proposed method, the precise analysis is done for the selection of the type of input data for the PNN, the type of mother wavelet, and the required transform level which is based on the accuracy, simplicity, specificity, speed, and cost parameters. The results show that, by using normalized Shannon entropy and the normalized logarithmic energy entropy, not only it offers simplicity, specificity and reduced costs, it also has better accuracy compared to other smart and passive methods. Based on the results, the proposed islanding detection technique is highly accurate and does not mal-operate during islanding and non-islanding events.
An Islanding Detection Technique for Inverter-Based Distributed Generation in Microgrids
