A hybrid Hilbert Huang transform and improved fuzzy decision tree classifier for assessment of power quality disturbances in a grid connected distributed generation system

This paper focuses on discrete Hilbert Huang transform (HHT) and improved fuzzy decision tree (IFDT)-based detection and classification of power quality (PQ) disturbances as a new contribution to the literature. A distributed generation (DG)-based microgrid has been modelled with wind and solar. Different PQ disturbances have been simulated with various wind speed and PV penetration. The PQ signals are passed through empirical mode decomposition (EMD) to obtain the intrinsic mode functions (IMFs). These IMFs are enforced to the Hilbert transform (HT) to extract the instantaneous attributes. These attributes of Hilbert transform (HT) are used for features extraction. Based on these extracted features improved fuzzy rules are formed for classification of the PQ disturbances. Synthetically PQ disturbances are simulated to check the performance of the proposed method. All these signal samples are processed through the proposed algorithm. The proposed method has been found to be capable of accurate detection and classification of PQ disturbances than many other techniques in the literature.