Electric energy meter abnormity diagnosis method and system

Abstract

The invention discloses an electric energy meter abnormity diagnosis method and system, and belongs to the technical field of electric energy quality detection. The method comprises the following steps: S1, collecting object data, wherein the collection includes distributed real-time collection, offline data collection and Oracle data quasi-real-time synchronization, and the collected content includes archive data, operation data, event data, working condition data, work order data, scheduling data and environment data; S2, sending the collected data to an abnormality sensing module and an abnormality diagnosis module; S3, carding peripheral services involved in fault diagnosis, performing similarity measurement and group combination in combination with a clustering algorithm, establishinga fault real-time perception model, and outputting a fault pre-judgment result; and S4, based on feature classification after fault original feature extraction and data cleaning, carrying out fault diagnosis engine construction, finding reasons causing faults, and carrying out fault remote repair or providing an on-site repair solution. The electric energy meter abnormity diagnosis method can automatically determine the operation state of the electric energy meter.

Description

technical field [0001] The invention relates to the technical field of electric energy quality detection, and more specifically, relates to a method for diagnosing an abnormality of an electric energy meter and a system thereof. 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 dif...

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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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