Mean Shift Test-Based Identification of Severe Missing Events in Low-Voltage Distribution Networks

To identify severe missing events in low-voltage distribution network (LVDN) measurements under coexisting errors and asynchrony, a label-free severe missing event detection method based on the mean shift test is proposed. First, mathematical models for the error rate, asynchrony rate, and missing rate are established to describe typical gray measurement data scenarios in LVDNs, where errors and asynchrony are treated as background disturbances. Second, a phase-specific current regression model for severe missing-event detection is constructed based on the multiple linear relationship between the phase-line head-end current and the customer-side currents. Then, the mean shift test is introduced to detect abnormal time snapshots caused by severe missing events through statistical hypothesis testing. Simulation results show that the proposed method achieves a high TPR while maintaining a low FPR in the main comparative experiments, and it achieves the best overall TPR–FPR trade-off among the evaluated methods. The results demonstrate that the proposed method provides an effective means for label-free detection of severe missing events in LVDNs under background measurement disturbances and the operating conditions considered in this study.