Identification and classification of microgrid disturbances in a hybrid distributed generation system using wavelet transform

Abstract

In this paper, Islanding and Power Quality(PQ) Issues in Hybrid Distributed Generation(DG) System consists of Photovoltaic(PV) system and Wind Power Plant connected to grid through a Point of Common Coupling(PCC), are detected and classified, using Wavelet Transform (WT) and Artificial Neural Networks (ANN). A new effective approach is proposed using WT indices and loading of DG on detection and classification of disturbances in the system. WT indices extracted from the Negative Sequence component of the voltage signal at PCC is used to detect the disturbances. WT indices and loading of DG system are then used to train ANN. The results demonstrate the advantages of WT over THD in classification of disturbances in the system and robustness in application of Machine Learning (ML) Classifier.