Detection of Local Abnormal Points in Three-Phase Watt-Hour Meter Measurement by Integrating the Internet of Things Technology and Local Density Factor

Three-phase watt-hour meter is the key equipment for power supply enterprises to measure electric energy, and it is of great significance to ensure the accuracy of measurement. Therefore, a method for detecting the local abnormal points of a three-phase watt-hour meter based on Internet of Things technology and local density factor is proposed. Firstly, the Internet of Things technology and local density factor are used to preprocess the operation data of a three-phase watt-hour meter. According to the preprocessed results, the normal distribution function equipped with random variables is used to describe the large-scale attenuation characteristics of the channel, and the signal enhancement goal in the process of micro-power communication signal acquisition is completed. The autoregressive moving average model is constructed to realize the detection of local abnormal points in three-phase watt-hour meter measurement. The experimental results show that the proposed method has higher error detection accuracy, more complete detection performance, and strong practicability.