LAUSR

Abstract In order to improve the effect and timeliness of ground fault monitoring and improve the effect of fault early warning, a DC ground fault monitoring method for electrical equipment in 110 kV smart substations based on improved rough sets is proposed. The characteristics of the DC ground fault current are analyzed, and the equivalent model of the DC system is constructed according to the analysis results. The low-frequency signal is enhanced according to the fuzzy rough set, and the low-frequency sampled transient zero-sequence current signal is enhanced preprocessing and wavelet packet decomposition according to the signal enhancement proportional coefficient, and the fault line is selected according to the principle of maximum energy. The wavelet packet transform method is used to decompose and analyze the waveform of the leakage current generated when a single-point grounding fault occurs in a branch of the DC system, and the grounding fault location of the DC system of the substation is realized by relative entropy operation. With the help of the cloud platform, a DC ground fault monitoring platform is built, and the ground fault monitoring is completed by using this platform. The experimental results show that the method can obtain the characteristic harmonics of the ground fault, accurately monitor whether the mutation occurs, and can realize the early warning of the ground fault of the electrical equipment in a faster time, indicating that it can realize the accurate and timely monitoring of the ground fault.