Enhanced Density Peak-Based Power Grid Reactive Voltage Partitioning

Xingye Deng (), Canwei Liu, Hualiang Liu, Lei Chen, Yuyan Guo and Heding Zhen
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Xingye Deng: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Canwei Liu: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Hualiang Liu: Changde Water Meter Manufacture Co., Ltd., Changde 415000, China
Lei Chen: School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Yuyan Guo: School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Heding Zhen: School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China

Energies, 2023, vol. 16, issue 17, 1-24

Abstract: Clustering-based reactive voltage partitioning is successful in reducing grid cascading faults, by using clustering methods to categorize different power-consuming entities in the power grid into distinct regions. In reality, each power-consuming entity has different electrical characteristics. Additionally, due to the irregular and uneven distribution of the population, the distribution of electricity consumption is also irregular and uneven. However, the existing method neglects the electrical difference among each entity and the irregular and uneven density distribution of electricity consumption, resulting in poor accuracy and adaptability of these methods. To address these problems, an enhanced density peak model-based power grid reactive voltage partitioning method is proposed in this paper, called EDPVP. First, the power grid is modeled as a weighted reactive network to consider entity electrical differences. Second, the novel local density and density following distance are designed to enhance the density peak model to address the problem that the traditional density peak model cannot adapt to weighted networks. Finally, the enhanced density peak model is further equipped with an optimized cluster centers selection strategy and an updated remaining node assignment strategy, to better identify irregular and uneven density distribution of electricity consumption, and to achieve fast and accurate reactive voltage partition. Experiments on two real power grids demonstrate the effectiveness of the EDPVP.

Keywords: reactive voltage partition; weighted reactive network; density peak (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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