Data conversion method and system based on intelligent electric energy meter metering error analysis

Abstract

The invention relates to a data conversion method based on intelligent electric energy meter metering error analysis. The concept of metering relative error weighted average is introduced through multiple data transformation algorithms, then intelligent electric energy meter metering interval analysis is performed through a confidence interval analysis method, and error result evaluation and optimization of error result optimization, abnormal correlation analysis, metering error high-order analysis approximation and the like of the transformer area edge electric energy meter are realized. A standard algorithm knowledge base for metering error analysis of the intelligent electric energy meter is established so that relative errors of the electric energy meter are accurately calculated, andmaintenance, service life and the like of the electric energy meter are effectively and accurately guided.

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 ...

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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

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