Affiliations: [a] Meteorology Center of Guangdong Power Grid Co. Ltd., Guangzhou, Guangdong, China | [b] School of Electrical Engineering and Automation, Wuhan University, Wuhan, Hubei, China
Correspondence:
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Corresponding author: Zhengmin Kong, School of Electrical Engineering and Automation, Wuhan University, Wuhan, Hubei, China. E-mail: [email protected].
Abstract: At present, the main way for electric power companies to check the accuracy of electric meters is that professionals regularly bring standard electric meters to the site for verification. With the widespread application of smart meters and the development of data processing technology, remote error estimation based on the operating data of smart meters becomes possible. In this paper, an error estimation method of smart meter based on clustering and adaptive gradient descent method is proposed. Firstly, the fuzzy c-means clustering method is used to preprocess the data to classify the operating conditions of each measurement, and then the adaptive gradient descent method is used to establish the error estimation model. The simulation results show that this method has high error estimation accuracy. This method has a small amount of calculation and high reliability and is suitable for large-scale power grids.
