Identification of low-voltage distribution substation topology based on user profile - Li, Jiang; Yao, Xinlong; Liu, Bo (Frontiers in Energy Research, 2024)
The accurate identification of low-voltage distribution substation topology plays a crucial role in research applications such as line loss management, fault location, imbalance correction, and intelligent operation and maintenance of substations. In response to the challenges posed by the large scale of users and the complex connection relationships in low-voltage distribution substations, which complicate the identification of their topology, a method for identifying low-voltage distribution substation topology based on user profiling technology is proposed. This method is supported by big data technology and introduces the concept of user profiling into the research of substation topology identification. Firstly, by deeply studying the theoretical knowledge of the relationship between the supply of distribution transformers and the power consumption of substation users, as well as the voltage similarity, we establish the feature labels for electricity coefficient and voltage similarity. Then, we use the continuous relaxation method and branch-and-bound method to solve the electricity coefficient matrix; and use the Gaussian kernel function to solve the voltage similarity matrix. Finally, by constructing a comprehensive attribution matrix using the electricity coefficient matrix and the voltage similarity matrix, employing a convolutional neural network to cluster and solve the comprehensive attribution matrix, outputting user-area attribution information, forming user-area attribution profiles, and completing the identification of area attribution relationships in low-voltage distribution systems. Simulation results demonstrate that the proposed method not only effectively identifies the area information to which low-voltage users belong but also discerns the connection relationships between users and area transformers.