Autoencoder and Kernel Density Estimation-Based Anomaly Detection Method for Power Consumption among Distribution Network Customers

Detecting anomalies in power consumption among distribution network customers is a critical aspect of maintaining the efficiency and reliability of distribution networks. Current research in anomaly detection primarily relies on user profile clustering, which involves subjective outlier detection-based criteria sensitive to input parameters, making it difficult to obtain objective and reasonable detection results. Therefore, in this work, an anomaly detection method for power consumption among distribution network customers based on autoencoder and kernel density estimation is proposed to objectively quantify anomaly levels. First, a processing method for load increment curves based on autoencoder and seasonal decomposition is proposed to detect abnormal patterns within these curves. Second, a quantification method for anomaly levels of power consumption based on kernel density estimation is proposed on the basis of the abnormal patterns detected, which enables the objective differentiation of anomaly levels among different customers and time periods. Finally, case studies on the actual load data from distribution networks are performed to verify the effectiveness of the proposed methods. The simulation results demonstrate that the proposed methods can effectively detect anomalies in power consumption and objectively quantify anomaly levels among different customers in distribution networks.