Electric energy meter abnormity diagnosis method

Abstract

The invention relates to an electric energy meter abnormity diagnosis method comprising the following steps: in a first step, electric energy meter data and event data are obtained via on-site detection and an electric energy information collection system; in a second step, the electric energy meter data and the event data that are obtained in the first step are subjected to single abnormity analysis operation which comprises six types of intelligent diagnosis and analysis models, and each intelligent diagnosis and analysis model comprises a number of intelligent diagnosis themes; in a third step, single abnormity analysis and diagnosis results of the intelligent diagnosis themes obtained in the second step are output to on-site detection personnel, and the on-site detection personnel can be assisted in electric energy meter detection and diagnosis. Via technological means such as data comparison, statistical analysis, data mining and the like, the electric energy meter abnormity diagnosis method can be used for diagnosing and analyzing electric energy meter operation conditions, and whether an electric energy meter is in a normal operation state can be effectively determined.

Description

technical field [0001] The invention belongs to the technical field of power quality detection, and in particular relates to a method for diagnosing abnormality of a power meter. Background technique [0002] In recent years, with the expansion of domestic power demand and grid scale, the voltage level has been continuously improved, and the number of large-capacity and high-voltage users has continued to increase. It has become the focus of social attention. During the detection process, the accuracy of electric energy meter diagnosis is required to be higher. In the existing detection process, electric energy meter diagnosis is mainly carried out manually at the electric energy meter site. There is a lack of data support on site, and illegally passed Quantitative and accurate judgments by procedural methods rely on manual experience data to judge, which is likely to cause inaccurate or misdiagnosed situations, and it is difficult to meet the needs of lean management of mea...

AI Technical Summary

Problems solved by technology

[0002] In recent years, with the expansion of domestic power demand and grid scale, the voltage level has been continuously improved, and the number of large-capacity and high-voltage users has continued to increase. It has become the focus of social attention. During the detection process, the accuracy of electric energy meter diagnosis is required to be higher. In the existing detection process, electric energy meter diagnosis is mainly carried out manually at the electric energy meter site. There is a lack of data support on site, and illegally passed Quantitative and accurate judgments by procedural methods are all based on manual experience data, which can easily lead to inaccurate or misdiagnosed situations, and it is difficult to meet the needs of lean management of measurement

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