Design of an automated system for detection and classification of power quality disturbances

Power quality (PQ) is one of the most significant issues in power monitoring systems and smart grids in recent years. Identifying disturbances has an important role in improving PQ. The intention of this paper is to improve the accuracy of the detection step in PQ disturbances. To do so an adaptive method called CEEMD (complete ensemble empirical mode decomposition) is used here for the first time. Here a new modified version of Hilbert Huang Transform (HHT) has been proposed for feature extraction. This version is combination of CEEMD and Hilbert Transform. The performance of the proposed method is compared with classical algorithms like HHT and MHHT (Modified HHT). Experimental results demonstrate the efficiency of the proposed method.