Big data deduction based electric energy metering device error detection method and system

Abstract

The invention discloses a big data deduction based electric energy metering device error detection method and system. The method includes the steps: acquiring multiple sets of electric energy data of each circuit, calculating bus electricity quantity non-balance ratio, building a mapping model of electric energy and bus electricity quantity non-balance ratio, calculating close degree of electric energy data and a virtual load model, selecting a plurality of sets of electric energy data according to the close degree to form a training sample set, taking the bus electricity quantity non-balance ratio as a teacher sample set to train the artificial neural network, and performing deduction and reconfiguration to obtain whole error. The system comprises an electric energy data acquisition unit, a bus electricity quantity non-balance ratio calculation unit, a virtual load model building unit, a close degree calculation unit, a sample set generation unit, an artificial neural network training unit and a whole error calculation unit. The method and system can monitor whole metering error of electric energy metering devices in real time at the same time, and has the advantages of avoidance of field test, simplicity in realization method, real-time performance and quickness in detection, high efficiency and safety.

Description

technical field [0001] The invention relates to a state evaluation technology of an electric energy metering device in electric power equipment, and specifically discloses an error detection method and system for an electric energy metering device based on big data deduction. Background technique [0002] The accurate measurement of electric energy is the basis for ensuring the fairness of electricity billing. Every year, the error electricity caused by the error of the metering device is quite considerable. On the one hand, there are currently many technical means for detecting the error of the metering device. Traditional methods include: laboratory verification and on-site inspection of electric energy meters, laboratory verification and on-site inspection of transformers, on-site inspection of secondary voltage drop of voltage transformers, etc. These tasks require personnel to disassemble the metering device to the laboratory, or require personnel to bring the standard...

AI Technical Summary

Problems solved by technology

Because the error of the metering device is relatively small, the amount of error information contained in the electric energy data is also very weak. In the past, it was difficult to detect the defective state of error out of tolerance through a small amount of electric energy data

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