Measurement Error Estimation Method of Field Service Electricity Energy Meters under the Condition of Big Data - DAI Yan-jie; DONG Xian-guang; LIU Ya-qi; LIANG Ya-jie; LIU Xiao; QI Jia; SUN Yong-quan (Journal of Harbin University of Science and Technology, 2022)
In order to better explore the measurement performance of field service electricity energy meters under multi-dimensional influence conditions, and infer measurement error under standard laboratory conditions, a measurement error estimation method of field service electricity energy meters is proposed based on big data analysis technology, which realizes on-line measurement error estimation by combining environmental data and electric factor data of field operation. Firstly, the K-Means clustering algorithm is improved by optimizing the clustering evaluation index, and the field environmental data is analyzed by clustering. Then, LM algorithm is used to optimize the data training method BP neural network, and modeling relationship between electric energy error and the four factors of environmental temperature, humidity, load current and power factor is constructed. Finally, on the basis of field electric energy error data of electric energy meter, the calculation of engineering application example is carried out. The results show that the proposed method can be used to divide the measurement energy error of electric energy meter, and the accuracy of the proposed method is verified.