Open AccessIdentification of low-voltage connection relation in distribution platform based on similarity of voltage curve and grey correlation degree of entropy weight
Author(s) -
Yingyi Yan,
Weiliang Bao,
Lijiao Li,
Zongjie Wang
Publication year - 2021
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 - 10.1088/1742-6596/2137/1/012019
Subject(s) - entropy (arrow of time) , pearson product moment correlation coefficient , correctness , voltage , mathematics , data mining , correlation coefficient , degree (music) , similarity (geometry) , grid connection , interconnection , computer science , topology (electrical circuits) , algorithm , statistics , grid , geometry , artificial intelligence , telecommunications , electrical engineering , engineering , physics , quantum mechanics , combinatorics , acoustics , image (mathematics)
The correct low-voltage connection relationship in distribution area is of great significance to the safe operation and efficient management of power grid. As there are many reconstruction projects in the low-voltage platform area, the assets change frequently, and the interconnection perception ability of the low-voltage platform area is weak, which brings great difficulties to the identification of the user connection relationship. Traditional identification methods are heavy and inefficient. This paper proposes a method based on trend similarity and distance measure to identify low-voltage connection relation in distribution platform area. Firstly, Pearson correlation coefficient and discrete Fréchet distance are calculated to measure the trend similarity of voltage curve, and abnormal users are found out. GIS is used to search for adjacent stations, and finally, the correct station area of users is determined by analyzing the entropy weight grey correlation degree. The applicability and correctness of the proposed algorithm are verified by the application results of an example.