Research on anomaly detection and correction of power metering data based on machine learning algorithm

Electric energy measurement is the basis of marketization of electric energy. If the power metering device is abnormal, it will directly affect the economic interests of both sides. At present, the electric energy measurement data of power grid enterprises has generally adopted the mode of remote centralized collection. The existing methods of abnormal detection and location of electric energy metering are mainly through the analysis of the abnormal data alarm issued by the electric energy acquisition system and the on-site inspection of the metering device. With the continuous expansion of the scale of electric power data, the existing methods highlight the shortcomings of low accuracy and low efficiency. In order to explore the optimal solution to the above problems, this paper constructs a multi-model fusion anomaly detection method of electric energy measurement data based on machine learning, and gives the anomaly correction scheme of electric energy measurement data. The results show that the fusion model has the best performance in the actual situation, with Area Under Curve (AUC) reaching 0.9653 and True Positive Rate (TPR) exceeding 0.64 under the condition of zero False Positive Threshold (FPT). The comprehensive performance is better than that of other single models.