An anomaly identifying method for metering of standard electricity meters in an automatic verification assembly line based on correlated time-invariant
Under the automated verification mode, the metering performance of standard electricity meter may degrade or even exceed in the long-term operation, while the existing regular manual inspections are offline that cannot detect the abnormal working condition of the standard meter in real time, and the...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.
2025-02-01
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| Series: | Diance yu yibiao |
| Subjects: | |
| Online Access: | http://www.emijournal.net/dcyyben/ch/reader/create_pdf.aspx?file_no=20220409003&flag=1&journal_id=dcyyben&year_id=2025 |
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| Summary: | Under the automated verification mode, the metering performance of standard electricity meter may degrade or even exceed in the long-term operation, while the existing regular manual inspections are offline that cannot detect the abnormal working condition of the standard meter in real time, and there is a risk of credibility losing in the error test results of smart electricity meters. Therefore, based on the correlation between results of regular verification and error test, this paper proposes an anomaly identifying method for standard electricity meters based on correlated time-invariant, in order to check the metering performance of standard electricity meters in real time. Firstly, according to the group characteristics of the error test results, the characteristic parameters of error variable in standard electricity meters are constructed. Then, the correlation between the variable values and periodic verification results is quantified by the Pearson correlation coefficient. Secondly, anomaly detection algorithm based on improved box plot is used to locate the anomaly standard electricity meters. Finally, the method is applied to practice, and the effectiveness of the proposed method is verified by the comparison of periodic detection results. |
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| ISSN: | 1001-1390 |