Detection and classification of low-frequency power disturbances using a morphological max-lifting scheme

This paper presents a morphological max-lifting scheme for the detection and classification of low-frequency power disturbances. In order to extract waveform features of low-frequency disturbances, the proposed scheme employs mathematical morphology (MM) for its advantage in noise removing and max-lifting for its ability of information preserving. Afterwards, two aided variables are constructed to assist the classification of low-frequency disturbances. A variety of low-frequency power disturbances have been included in simulation studies and simulation results have demonstrated the effectiveness and feasibility of the proposed scheme.