Method for Selecting Abnormal Metering Data of Smart Electric Energy Meters and Assessing the Impact on Line Loss

To address the issue of deviation in line loss calculation caused by abnormal metering data in smart electricity meters, a two-step screening method for abnormal metering data and a quantitative assessment method for the impact of line loss are proposed. Taking a 10kV distribution substation as an example, 92 days of metering data from March to May in 2025 were selected as samples. The abnormal data were identified through the initial screening based on deviation thresholds combined with density clustering refinement. An association calculation model between abnormal electricity consumption and line loss rate was established. The results show that the accuracy rate of identifying abnormal metering data by this method reaches 96.8%. After correcting the abnormal data, the statistical line loss rate of the substation decreased from 8.71% to 4.29%, and the deviation from the theoretical line loss value was reduced to 0.22%. This method can quickly locate abnormal metering data and accurately quantify its impact on line loss, providing technical support for the refined management of line loss in distribution areas.