High impedance fault detection method based on improved complete ensemble empirical mode decomposition for DC distribution network

Aiming at DC distribution network, we proposed a novel high impedance fault detection method in the paper, it main procedures are as follows: Firstly, used the algorithm of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to extract the first intrinsic mode function (IMF) of the characteristic mode. Secondly, calculated the singular point of mutation and the cumulative slope by the acquisition of the first order difference operation, and then, achieved to distinguish the fault state and the normal state by the comparison between the slope and the starting threshold. Thirdly, identified the first IMF with Prony algorithm to obtain the parameters of characteristic frequency components (CFC) and direct current components (DC), and calculated the energy ratio between them, and then, distinguished small impedance fault (SIF), medium impedance fault (MIF), high impedance fault (HIF) and load switching (LS) by different values of energy ratio. A large number of experiments show that the proposed method is accurate and effective. Compared with other methods, this method shows its merits in feature extraction accuracy, detection accuracy and calculation speed.