In low-voltage distribution substations, users′ neutral and ground wire wiring errors often cause leakage faults, which can easily lead to electric shock casualties. Although multivariate regression analysis has been used to locate users with wiring errors and leakage faults, it is limited by the insufficient sampling frequency of the current monitoring equipment in the substation area, and has the inherent defect of poor positioning timeliness. Therefore, a fast localization method based on super-resolution reconstruction of metering data is proposed. By reconstructing low-resolution data, this approach overcomes the time-resolution limitations of traditional methods. First, the composition of the residual current in the substation area during wiring errors and leakage faults is analyzed, and the correlation characteristics between the residual current and the user load currents are clarified. Then, the generalization performance of traditional multivariate linear regression, Lasso regression, ridge regression and elastic network regression models is systematically evaluated, revealing the impact of independent variable collinearity on the stability of parameter estimation. The time series current data is further mapped into a two-dimensional feature image, and the enhanced super-resolution generative adversarial network (ESRGAN) model is used for super-resolution reconstruction. The data reconstruction quality is verified by root mean square error, peak signal-to-noise ratio and structural similarity index. Finally, the reconstructed high-resolution data was used to establish an elastic network regression model to locate users with wiring errors and leakage. The comparative analysis based on the laboratory simulation platform and the field measured data showed that the proposed method has higher data reconstruction quality, higher model fitting degree and higher accuracy in locating users with wiring errors and leakage. Moreover, the fault localization time is reduced by several multiples compared to traditional methods.