Estimation Of Reliability Based On Bayesian ZINB-GLM Intelligent Energy Meter

As the nerve ending of the smart grid,the intelligent electric energy meter affects the safety of electricity consumption of thousands of households.In the actual operation process,the distribution of smart energy meters is extremely wide and the operating environment varies widely.Under natural operating conditions,the same type of smart energy meter has significant differences in reliability due to environmental factors.Based on the research background,this paper analyzes the relationship between the reliability data distribution of smart energy meter and environmental stress in harsh environment,and selects the operation data of smart electric energy meter in typical provinces such as Xinjiang,Heilongjiang and Fujian.Aiming at the problems of traditional reliability algorithms,such as large sample size,inability to make full use of prior information and sensitivity to noise points,this paper establishes a dynamic energy meter level Bayesian zero expansion negative binomial by studying the reliability related theory(Zero-Inflated Negative Binomial,ZINB)Generalized Linear Model(GLM).Based on the Bayesian hierarchical ZINB-GLM reliability analysis model of intelligent electric energy meter,quantitative analysis of the influence of environmental stress on the reliability of smart energy meter,predicting the short-term operating state of smart energy meter,and providing a study for the reliability of smart energy meter A new solution.This thesis firstly expounds the significance of the research on the reliability of smart energy meter,introduces the traditional reliability research method,summarizes the research status of reliability at home and abroad,and introduces the important application of Bayesian theory in reliability.Secondly,it reveals the failure mechanism of smart energy meter.And from the unit level level to analyze the relationship with environmental factors,to achieve sample data from typical environmental areas,introduced the general steps of reliability analysis;Then,study the detection method of small sample fault data object outliers of smart energy meter.For the problem that the Outliers Factor(LOF)algorithm determines the failure of small sample data outliers,by introducing the average window and the Grubbs test double standard,the accuracy of the outlier determination in the fault data is improved.The LOF factor is integrated into the model through the joint function,and the hidden information of the small sample outliers is deeply explored.Then,the selection method and evaluation criteria of the prior distribution in the Bayesian model framework are studied,and the composition and e valuation criteria of the generalized linear regression model are introduced.And the definition and conversion scheme of environmental factors.By establishing a Bayesian hierarchical Weibull model,the reliability of smart energy meter in a certain area is estimated in the short-term without the integration of environmental factors.In addition,for the zero-expansion characteristics of the intelligent energy meter fault data,zero expansion is introduced for the first time.The analysis process,for the problem of excessive zero value and discretization in the fault data,constructs a generalized hierarchical regression structure through the environmental factor conversion algorithm,fully integrates the environmental stress characteristics,and obtains t he influence degree of environmental factors on the failure rate,and accurately estimates the intelligence.Energy meter reliability.Finally,the analysis of the actual fault data shows that the Bayesian ZINB regression model can better reflect the chang ing characteristics of the smart energy meter in the harsh environment compared to the Poisson distribution,the Gaussian distribution and th e maximum likelihood estimation.