Identification algorithm of low voltage user-transformer relationship based on improved spectral clustering
LI Fangshuo, LIU Lina, CHENG Zhijiong, SHEN Jie, ZHOU Yifei, XIONG Siyu
1. State Grid Sichuan Electric Power Company Measurement Center, Chengdu 610045, China;
2. School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
Abstract: The accuracy of user-transformer relationship identification plays a key role in the safe and stable operation of the distribution network, and its results directly affect the accuracy of line loss calculation and comprehensive protection of distribution network. The traditional distribution station recognition algorithm cluster the power frequency zero-crossing sequence, which does not perform well when the sequence is ‘non-convex’. Accordingly, an improved spectral clustering algorithm for arbitrary zero-crossing sequences is proposed. The algorithm takes maximizing the variance of the weight matrix (WM) as the objective function of the adaptive particle swarm optimization algorithm, then adaptively selects the parameter threshold of WM to change the WM into a sparse matrix. Thus, the eigenvectors calculation problem of the traditional spectral clustering algorithm is simplified to find the orthogonal null space of the canonical Laplace matrix. the recognition accuracy of the proposed algorithm with sample data obtained by the simulation software can reach 99.11%, which is better than that of the traditional clustering algorithm, and it can still reach 98.71% when processing the measured data.
Keywords: user-transformer relationship identification;improved spectral clustering;adaptive particle swarm optimization;maximize variance;null space
2023, 49(9):128-134  收稿日期: 2022-12-20;收到修改稿日期: 2023-4-14
基金项目: 国家自然科学基金面上项目(51777173)；四川省科技计划项目(2021YFG0294)；国网四川省电力公司科技项目 (52199720005Z)
作者简介: 李方硕（1986-），男，四川广安市人，高级工程师，硕士，从事电能量采集与电力通信。
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