A failure detection method based on multivariate variational mode decomposition and skewness for modular DC circuit breakers

Abstract

Modular DC circuit breakers (MDCCBs) are widely used to protect multi-terminal HVDC (MT-HVDC) girds. However, the metal-oxide varistor (MOV) failure detection of MDCCBs still poses a challenge for the reliability of MT-HVDC grids. To this end, this paper analyzes the faulty features of the MDCCB and proposes a failure detection method that uses a multivariate variational mode decomposition (MVMD) and a differential skewness. The proposed method is as follows. First, MVMD decomposes the differential current, which is obtained by the current of the MDCCB energy absorption branch, into its time–frequency components, and then the time–frequency component that has the highest frequency is selected as the feature component. Second, a differential skewness is constructed by the feature component, and the differential skewness is employed to detect the MOV failure of the MDCCB. Finally, the performance of the proposed method is evaluated by a four-terminal symmetric bipolar HVDC system with MDCCBs. The simulation experiment results show that the proposed method can correctly detect the MOV failures of the MDCCB in different faulty conditions, even in the weak failure condition with noise influence.