A data conversion method and system based on measurement error analysis of smart energy meters

Abstract

The invention relates to a data transformation method based on the analysis of the measurement error of the smart electric energy meter. Through various data transformation algorithms, the concept of the weighted average value of the measurement relative error is introduced, and then the measurement interval analysis of the intelligent electric energy meter is carried out through the confidence interval analysis method to realize The error result optimization of the electric energy meter at the edge of the station area, the abnormal correlation analysis, the high-order analysis and approximation of the measurement error, etc. realize the error result evaluation and optimization. Through the establishment of a standard algorithm knowledge base for the analysis of measurement errors of smart electric energy meters, the accurate calculation of the relative error of electric energy meters can be realized, and accurate guidance can be made on the maintenance and service life of electric energy meters.

Description

technical field [0001] The invention belongs to the field of measurement error analysis of electric energy meters, in particular to a data transformation method and system based on the analysis of measurement errors of intelligent electric energy meters. Background technique [0002] Since 2009, the State Grid Corporation of China has made great efforts to build an electricity consumption information collection system, which has realized the operation of 450 million electricity meters in the whole region. After years of operation, the system has accumulated a large amount of electricity consumption data. Through data analysis, dig out effective electricity consumption information, such as the operation error of the energy meter, the user's electricity consumption behavior pattern, etc., which can not only develop the potential of massive data, but also greatly reduce operating costs, and provide decision support for power grid companies. [0003] However, among the massive ...

AI Technical Summary

Problems solved by technology

[0003] However, among the massive data collected by the electricity consumption information collection system, there are a large number of various data collected from the real world, and the quality of the original data is affected by diversity, uncertainty and complexity, making the collected The actual data is messy, there are missing and abnormal phenomena, and in many cases it does not meet the normative requirements of data mining tools for knowledge acquisition

Images