Data-Driven Evaluation for Error States of Standard Electricity Meters on Automatic Verification Assembly Line

Abstract

An automatic verification assembly line (AVAL) verifies the reliability and the accuracy of electricity smart meters using standard meters. As time goes by, the standard meters on an AVAL may experience metrological performance degradation, affecting verification results. Thus, the control over the standard meters’ error states is of great significance. Traditionally, their error states can only be acquired at regular intervals and remain unknown during the AVAL's operation. To address this issue, we propose a data-driven method to evaluate standard meters’ error states without interrupting the verification task. Instead of using an additional standard meter with a higher accuracy, a statistics method is applied to the verification data collected from an AVAL to conduct this work. The proposed method consists of four phases: Creating evaluation parameters, identifying the reference meter, calculating deviations, and recognizing error states. A case study on an AVAL verifies the effectiveness of the proposed method.