Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Energies

DOI: 10.3390/en19173989

Authors: Yiqun Cao, Zenan Zheng, Xiaoming Lin, Yingqi Yi

Institutions: South China University of Technology, China Southern Power Grid (China)

Language: English

Topic: Power System Optimization and Stability

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137