Automatic screening method based on error testing for electric energy meter

Abstract

The invention provides an automatic screening method based on error testing for an electric energy meter. The method comprises the steps that the electric energy meter is energized with N times of rated current, and error testing in a self-heating state is achieved; secondary screening is conducted on the electric energy meter according to the error mean value of the electric energy meter at multiple preset test points; an absolute value of the difference between an error value of the electric energy meter at the new test point and the error value at the corresponding test point in the previous step serves as a voltage transformation error value, and then the electric energy meter is re-screened according to the voltage transformation error values; comprehensive testing of the individual performance of the electric energy meter is achieved; the error mean value of multiple electric energy meters at the same test point is obtained and serves as an average error, and the electric energymeter is re-screened according to the difference value between the error value of the electric energy meter at the corresponding test point and the average error; balance testing on the overall quality of multiple energy meters is achieved.

Description

technical field [0001] The invention relates to the technical field of electric energy meter testing, in particular to an automatic screening method for electric energy meters based on error testing. Background technique [0002] At present, when the electric energy meter manufacturers are submitting the smart electric energy meter for inspection, there is too much manual participation in the error calibration process. Whether the test environment temperature meets the standard is also manually judged, and the selection of the quality of the electric energy meter is also done manually. Disadvantages of this approach include: [0003] 1. Manual participation in error calibration affects efficiency and is prone to errors; [0004] 2. Human labor often cannot detect changes in the test environment temperature in time; [0005] 3. Data comparison during manual screening is not comprehensive enough; [0006] 4. It is cumbersome to add, delete, modify test points and modify the...

AI Technical Summary

Problems solved by technology

[0003] 1. Manual participation in error calibration affects efficiency and is prone to errors;

[0004] 2. Human labor often cannot detect changes in the test environment temperature in time;

[0005] 3. Data comparison during manual screening is not comprehensive enough;

[0006] 4. It is troublesome to add, delete, modify test points and modify the judgment threshold

Images