T/ZSA 182-2023 Active Group standards

T/ZSA 182-2023 Remote diagnosis technology requirements for operational errors of an electric energy meter

Publish Date: 2023-11-25 Implement Date: 2023-11-26 For services related to genuine standard inquiry, procurement, translation, and other related services in China, please Contact Us

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Basic Information

Standard Code: T/ZSA 182-2023

Standard Type: Group standards

Standard Status: Active

is_force_gb: no

CCS Name: Encoding, character sets, and character recognition

ICS Name: Comprehensive Application of Information Technology

Publish Date: 2023-11-25

Implement Date: 2023-11-26

Pages: 14 pages

Scope

Scope: This document proposes technical requirements for applying big data analysis technology to conduct remote diagnosis of operational errors in electricity meters, including requirements for data sets, construction of big data analysis models, model evaluation and monitoring, and operational error diagnosis. This document is applicable to the online diagnosis of operational errors in single-phase and three-phase electronic AC electricity meters with voltages of 10 kV and below currently in operation in power grids. Main technical content: Remote diagnosis of operational errors in electricity meters is primarily based on the law of conservation of energy and the topological relationship of substation lines, utilizing big data analysis technology to construct a model for analyzing operational errors in electricity meters. By timely collecting and storing real-time data from electricity meters, using the substation total meter as a standard device, and leveraging the synchronized frozen electricity data between the substation total meter and the calibrated electricity meters, an equation system is established and solved on the online electricity meter calibration platform to conduct "point-to-point" real-time calculation of operational errors in electricity meters, thereby achieving remote diagnosis of operational errors in electricity meters in real time. This document outlines the application process of big data analysis models in remote diagnosis of operational errors in electricity meters, specifies necessary technical content and performance requirements, and aims to improve the diagnostic efficiency and accuracy of operational errors in electricity meters. The document primarily includes requirements for data sets, construction of big data analysis models, model evaluation and monitoring, and operational error diagnosis. The document aims to provide technical guidance for remote diagnosis of operational errors in electricity meters and promote the develop