Abstract
The purpose is to promote equipment automation in the smart distribution network and improve the electricity detection accuracy and reliability of power equipment. Here, electric meters in the smart distribution network are studied. Firstly, the current electricity detection methods are discussed. Secondly, the common electric meter fault mechanisms are summarized and analyzed. Finally, the GM (Grey Mole) (1,1) is optimized through ACO (Ant Colony Optimization) algorithm and is used for detection and investigation of the real-time electric meter monitoring system. Then, simulation experiments are conducted based on the user’s electricity consumption data. The results indicate that the load correction value optimized by the ACO algorithm is closer to the actual load than the predicted value, and the absolute residual value is greatly reduced compared with the predicted value. At the same time, the prediction trend is more similar. This shows that the ACO-optimized GM can effectively improve the accuracy of the electric meter monitoring system.
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