Measurement Error Analysis Of Intelligent Electric Energy Meters At All Temperatures And Temperature Compensation

With the construction of the national smart city and the development strategy of the ubiquitous power Internet of Things formulated by the State Grid Corporation,new metering devices such as smart energy meters,acquisition terminals,and electronic transformers have been widely used.The accuracy of the basic equipment in the company's metering equipment is particularly important for meter applications.However,the unstable performance of the smart electric energy meter in different environments and the measurement errors caused not only make it difficult to ensure the safety of the smart electric energy meter during data storage and information transmission,but also seriously affect the overall safe and stable operation of the power system.Therefore,this paper analyzes and researches the measurement error of the smart energy meter under full temperature through experiments,and proposes a measurement error model of the smart energy meter based on the neural network to improve the measurement accuracy of the smart energy meter.First,analyze the structure and measurement principle of the smart electric energy meter,build an overall test platform,and design a test plan.Obtain the situation that the measurement error of single-phase intelligent electric energy meter and three-phase intelligent electric energy meter change with temperature under different working conditions.Then,save the test data in the management workstation through the switch to accumulate the operation data of the intelligent electric energy meter.Secondly,the test data is processed.One is to analyze the test data as a whole and use Origin software to perform basic processing on the test data.The second is based on the analysis of the data obtained from the experiment to establish the overall optimization process of the smart energy meter based on the temperature.Introduce the error model used;then model the error of the smart energymeter machine to obtain the error model of the smart energy meter under different operating conditions and full temperature;on this basis,the correlation coefficient is obtained according to the obtained error model,and the correlation coefficient is used Carry out temperature compensation of intelligent electric energy meter in Matlab.Finally,the measurement error model of intelligent energy meter based on neural network is proposed.The extracted factors that affect the measurement error are taken as the input of neural network,and the measurement error of intelligent energy meter is taken as the output of neural network,so as to build a BP neural network with multiple inputs and single output.The single-phase intelligent energy meter and three-phase intelligent energy meter are selected randomly to verify,which shows that the model has the ability to fit and predict the measurement error data of intelligent energy meter,and the actual application in Matlab is carried out to verify the effectiveness of the algorithm proposed in this paper.