The Design And Implementation Of Management Device For Removed Electric Energy Meter

With the rapid development of the national economy,the number of electric energy meters managed by the power supply company is increasing year by year.The number of secondhand electric energy meter is huge because of the replacement,removal and fault.The requirement of high efficiency and high quality can not be met according to existing methods of rechecking and sorting.Electric energy meter as a measuring instrument for trade settlement has important significance for both electricity customers and power supply companies.The management of the removed electric energy meter as the last link of the entire life-cycle management,which is of great significance in power recheck,sorting and reuse,data collection and decision-making.According to this situation,based on machine vision,image identification,neural network,and other advanced technology,this paper studies the automatic recognition,automatic rechecking,fault diagnosis and classification of the removed electric energy meter.The main contents of this paper are as follows:Research on the electric energy meter characters identification based on gray scale multi-level method.Create an automatic identification model of the number.In the first,According to the consistent size of the electric energy meter,the camera can take location accurate image.So the number of the image is fixed,the electric energy meter number can be located.Secondly,it can split a single character it can split a single character of the electric energy meter number using the projection method.Finally,the gray-scale image of a single character using multi-level method is processed to extract mesh feature of the character,and the character by the template matching is recognized.This method not only can identify the number quickly,but also can identify blurred or uneven brightness image accurately.According to characteristics of data collection,and based on the nearest neighbor method,Create a model of data screening and addenda.After the number of the meter is identified,the abnormal data can be quickly identified by analyzing the characteristics of the data,and the abnormal number can be added and warning.This method can quickly recheck the number of the meter,and make sure the data is accurate and complete.The paper designs a fault diagnosis model of remove electric energy meter based on the double hidden layer BP network.Firstly,the data of the electric energy meter is collected from the marketing system and the device.This data can be selected and normalized.Then The iterative process is used to solve the optimal parameter solution and initialize the parameter values in multiple groups.The double hidden layer BP network fault diagnosis method has the global optimal neural network computing ability.lt avoids the network to be trapped to a local optimum.The third,this paper constructs the faultdiagnosis system based on the BP network and expert system.The electric energy meter is classified into four types.Finally,simulation with MATLAB is conducted.The result shows that the classification algorithms based on BP neural network for smart meters is effective.The significance of this paper is that,firstly,recheck the energy meter power is to ensure the integrity,accurate and reliable of data.and to provide powerful supporting evidence for disputes.Secondly,the sorting of the electric energy meter is fine,effectively improving the reusing rate of the remove electric energy meter,saving the new purchase cost of the assets,and reducing the pollution of the electronic waste to the environment.Last,it can offer reliable data for the quality evaluation of electric energy meter.Reliability analysis and new test method and standard of electric energy meter through adding up and analyzing faulty data.