Open AccessResearch on Unreasonable Parameter Identification Model of Overhead Line Based on k-means Clustering Algorithm
Author(s) -
Yifei Zhang,
Yanfeng Gong,
Jin Luo
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1549/2/022105
Subject(s) - cluster analysis , identification (biology) , overhead (engineering) , computer science , power grid , grid , data mining , power (physics) , rationality , line (geometry) , electric power system , algorithm , mathematical optimization , artificial intelligence , mathematics , botany , physics , geometry , quantum mechanics , political science , law , biology , operating system
The existence of unreasonable in the planning of grid data may influence the economy and rationality of the power grid planning and so on, which may lead to unreasonable structure or potential risk in planning power grid. And it may even threaten the safe operation of the entire power system. This paper summarizes the general characteristics and methods of power grid planning, introduces the theory of data mining, and establishes an unreasonable parameter identification model for overhead transmission lines based on-means clustering algorithm. Finally, an example is given to illustrate the validity of the model and method.