CN-122020493-A - Life assessment method based on historical fault data and operation data of electric energy meter

CN122020493ACN 122020493 ACN122020493 ACN 122020493ACN-122020493-A

Abstract

The invention discloses a life assessment method based on historical fault data and operation data of an electric energy meter, and relates to the technical field of life assessment of the electric energy meter; according to the invention, by means of modeling of historical operation data and historical fault data and fitting a single stress fault loss function under standard and nonstandard working conditions, normal and abnormal life loss caused by multi-stress coupling is comprehensively quantized, the defect that a laboratory acceleration test cannot simulate multi-stress cooperation is avoided, the problem that the traditional field statistics evaluation does not consider working condition difference and single stress evaluation coverage is incomplete is solved, and the full-period aging state of the intelligent electric energy meter is accurately reflected.

Inventors

LIU XIANJIN
YIN NING
WANG XINLEI
LU MINGHUI
CHEN HAOJIE
ZHANG JINLIANG

Assignees

安徽融兆智能有限公司

Dates

Publication Date: 20260512
Application Date: 20260415

Claims (10)

1. The service life assessment method based on the historical fault data and the operation data of the electric energy meter is characterized by comprising the following steps of: Calculating the basic service life of the electric energy meter in the same batch under the standard working condition through a Weibull distribution model; Constructing a multi-dimension association data set, and constructing a multi-stress coupling model of the electric energy meter of the same batch based on the association data set, and calculating the life real-time loss of the intelligent electric energy meter by combining the multi-stress coupling model and real-time operation data, wherein the multi-stress coupling model is a linear weighted coupling model; and the intelligent electric energy meter early warning evaluation is completed according to the basic service life and the service life loss.
2. The life assessment method based on historical fault data and operation data of an electric energy meter according to claim 1, wherein calculating the basic life under standard working conditions of the electric energy meter of the same batch through a weibull distribution model comprises: the method comprises the steps of reading historical operation data and historical fault data of the same batch of electric energy meter under standard working conditions, and constructing a basic sample set according to the historical operation data and the historical fault data; and calculating the basic service life of the electric energy meters in the same batch by combining the basic sample set and the Weibull distribution model.
3. The method for lifetime assessment based on electrical energy meter historical fault data and operational data of claim 2, wherein constructing a base sample set from the historical operational data and the historical fault data comprises: The method comprises the steps of selecting effective operation data of the intelligent meters in the same batch from historical operation data to serve as non-failure samples, extracting core characteristic parameters from the historical failure data to serve as failure samples, wherein the core characteristic parameters comprise installation time, failure time and standard working condition accumulated operation time length and failure type in failure; And carrying out sample labeling and integration on the non-failure samples and the failure samples to establish a basic sample set, wherein the sample labeling comprises operation time length, operation parameters and failure states.
4. The life assessment method based on historical fault data and operation data of an electric energy meter according to claim 2, wherein calculating the base life of the electric energy meter of the same batch by combining the base sample set and the weibull distribution model comprises: establishing a likelihood function according to a probability density function and a reliability function of the Weibull distribution model, solving the likelihood function by combining a Newton-Laporton method on the basis of a basic sample set to obtain the Weibull distribution model; And calculating the basic service life of the electric energy meters in the same batch based on the reliability function and the preset reliability, wherein the preset reliability is set to be 0.95.
5. The lifetime assessment method based on electric energy meter history fault data and operation data according to claim 4, wherein correcting the weibull distribution model by using a bayesian correction method comprises: Setting the prior distribution of the shape parameters as gamma distribution and the prior distribution of the scale parameters as lognormal distribution; On the basis of a basic sample set, a Markov chain Monte Carlo algorithm is combined to solve a posterior distribution function, so that corrected shape parameters and scale parameters are obtained, and the correction of the Weibull distribution model is completed.
6. The method for lifetime assessment based on ammeter historical fault data and operation data according to claim 4 or 5, wherein introducing a batch optimization coefficient optimization weibull distribution model comprises: setting a batch optimization coefficient by sampling the quality of components of the electric energy meter in the same batch, wherein the value range of the batch optimization coefficient is 0.95-1.05; multiplying the batch optimization coefficient by the scale parameter of the Weibull distribution model to obtain the optimized scale parameter so as to complete the optimization of the Weibull distribution model.
7. The method for lifetime assessment based on ammeter historical fault data and operational data of claim 1, wherein constructing a multi-dimensional correlated data set comprises: Dividing historical operation data and historical fault data into a plurality of first sub-data sets according to preset stress factors, and dividing the first sub-data sets into second sub-data sets according to standard working conditions and non-standard working conditions; And marking the fault type and the life loss of each historical fault data in the sub-data set II, and generating a multi-dimensional associated data set according to the sub-data set I, the sub-data set II and the marking result, wherein the life loss is calculated by combining the historical operation data.
8. The life assessment method based on historical fault data and operation data of an electric energy meter according to claim 1, wherein calculating the life real-time loss of the intelligent electric energy meter by combining the multi-stress coupling model and the real-time operation data comprises: Fitting the multidimensional associated data set by using a regression analysis method to obtain a fault loss function of single stress; and constructing a multi-stress coupling model according to the fault loss function of the single stress, bringing real-time operation data into the multi-stress coupling model, and calculating the life real-time loss of the intelligent electric energy meter.
9. The method for life assessment based on historical fault data and operational data of an electric energy meter according to claim 8, wherein obtaining a fault loss function for a single stress comprises: Extracting a sub-data set II corresponding to the sub-data set I from the multi-dimensional associated data set; Fitting the sub-data set II by adopting a regression analysis method to obtain a fault loss function of single stress, wherein the fault loss function comprises a fault loss function under a standard working condition and a non-standard working condition.
10. The life assessment method based on the historical fault data and the operation data of the electric energy meter according to claim 1, wherein the early warning assessment of the intelligent electric energy meter is completed according to the basic life and the life loss, and the life assessment method comprises the following steps: when the instantaneous speed-up of the life real-time loss exceeds a preset speed-up threshold or the life loss reaches a preset proportion of the basic life, generating an early warning signal, wherein the preset speed-up threshold is set according to a fault loss rule, and the preset proportion is 80%.

Description

Life assessment method based on historical fault data and operation data of electric energy meter Technical Field The invention belongs to the technical field of life assessment of electric energy meters, and particularly relates to a life assessment method based on historical fault data and operation data of an electric energy meter. Background The intelligent electric energy meter is used as a core metering terminal of the intelligent power grid, the operation reliability and the service life of the intelligent electric energy meter are directly related to the accuracy of electric energy metering and the operation stability of the power grid, various technical routes are formed for the service life evaluation of the intelligent electric energy meter in the industry, and the main stream comprises an evaluation scheme based on a laboratory acceleration test, a statistical evaluation scheme based on field historical data and a loss evaluation scheme based on single or partial stress. The existing intelligent electric energy meter service life assessment technology still has a plurality of problems to be solved, firstly, part of assessment schemes do not distinguish standard operation conditions and nonstandard operation conditions of the electric energy meter, the average service life is counted by directly utilizing field historical data, the interference of extreme operation data is easy to occur, hidden service life loss information in the operation data is not fully excavated, the full-period aging state of the intelligent electric energy meter cannot be accurately reflected, secondly, laboratory acceleration test schemes can only simulate an ideal single extreme stress environment, the field actual multi-stress synergistic operation conditions cannot be reproduced, the aging mechanism is inconsistent with the actual conditions, and the service life prediction result deviation is large. The invention provides a life assessment method based on historical fault data and operation data of an electric energy meter, which aims to solve the technical problems. Disclosure of Invention The invention aims to at least solve one of the technical problems in the prior art, and therefore, the invention provides a life assessment method based on historical fault data and operation data of an electric energy meter. To achieve the above object, a first aspect of the present invention provides a lifetime assessment method based on historical fault data and operation data of an electric energy meter, including: Calculating the basic service life of the electric energy meter in the same batch under the standard working condition through a Weibull distribution model; Constructing a multi-dimension association data set, and constructing a multi-stress coupling model of the electric energy meter of the same batch based on the association data set, and calculating the life real-time loss of the intelligent electric energy meter by combining the multi-stress coupling model and real-time operation data, wherein the multi-stress coupling model is a linear weighted coupling model; and the intelligent electric energy meter early warning evaluation is completed according to the basic service life and the service life loss. In one possible implementation manner, calculating the basic service life of the electric energy meter in the same batch under the standard working condition through the weibull distribution model includes: the method comprises the steps of reading historical operation data and historical fault data of the same batch of electric energy meter under standard working conditions, and constructing a basic sample set according to the historical operation data and the historical fault data; and calculating the basic service life of the electric energy meters in the same batch by combining the basic sample set and the Weibull distribution model. In one possible implementation, constructing a base sample set from historical operational data and historical fault data includes: The method comprises the steps of selecting effective operation data of the intelligent meters in the same batch from historical operation data to serve as non-failure samples, extracting core characteristic parameters from the historical failure data to serve as failure samples, wherein the core characteristic parameters comprise installation time, failure time and standard working condition accumulated operation time length and failure type in failure; And carrying out sample labeling and integration on the non-failure samples and the failure samples to establish a basic sample set, wherein the sample labeling comprises operation time length, operation parameters and failure states. In one possible implementation, calculating the base lifetime of a same batch of electric energy meters in combination with a base sample set and a weibull distribution model comprises: establishing a likelihood function according to a probability density function and a