Detection and classification of power quality disturbances in distribution networks based on VMD and DFA

In this paper, a novel algorithm is proposed to detect and classify the power quality (PQ) disturbances for distribution networks with distributed generation. First, a distribution system with photovoltaic and wind power generation is built as a test platform. Then, nine types of power quality disturbances in the distribution network are decomposed by variational mode decomposition (VMD) and the noise is filtered. Meanwhile, the mode functions containing characteristic information are extracted as input signals of detrended fluctuation analysis (DFA). Power quality disturbances are classified from the view of their distributed energy operational status, and three types of windows are set up to deal with different frequency disturbances. The two-dimensional and three-dimensional scatter plots of each type under three windows are depicted, and the criteria are determined to distinguish the disturbances under the different operating conditions. The simulations show that the algorithm is simpler, more accurate and feasible. It provides an approach for online real-time detection of embedded systems.