Abstract
The rapid development of the smart grid affects every aspect of people’s lives at all times. Among them, the identification accuracy of power metering anomalies is a serious problem that needs to be solved urgently. To further improve the identification accuracy of power metering anomalies, a user identification method for substation area anomalies is designed based on the restricted ridge regression model. First, a clustering algorithm is designed using high-frequency voltage data of users in the substation area to conduct phase separation and terminal branch identification for each user metering point in the substation area. Then establish a substation area energy conservation model equation set, and merge the end branch users based on genetic algorithm clustering results, thereby reducing the unknown quantity dimension. Then, a line resistance-constrained condition is constructed using the topological structure, and the model equation set is solved using quadratic programming based on the constrained ridge regression method to obtain the measurement point error results of users in the substation area. The experimental results show that this recognition method’s clustering effect and recognition accuracy are high, with a recognition rate of 98.21%. It can be seen that this abnormal user identification method has application value.
Published Version
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