State Monitoring and Anomaly Detection Algorithms for Electricity Meters Based on IoT Technology

Abstract

In response to the practical application of the electricity consumption information collection system in the online monitoring business of measuring equipment, the author introduces a method for analyzing the abnormal flying away of electricity meters based on the IoT technology LOF local anomaly detection algorithm. This method can effectively determine whether the abnormal energy representation value belongs to accidental or trend anomalies by calculating the abnormal factor of the energy representation value. After excluding the influence of accidental data, perform a secondary judgment on the abnormal flight of the energy meter. The experimental results show that when calculating the LOF factor of the electricity meter, it can be found that the LOF curve data range is mainly concentrated in the range of 0.8 to 1.3, and there is no significant change in the LOF factor near the mutation point. This proves that this method can effectively improve the accuracy of anomaly detection, avoid misjudgment of faults, and improve the efficiency of on-site fault handling.