Open AccessUser-transformer relation identification based on power balance model and adaptive AFSA
Author(s) -
Jiuzhou Song,
Yan Jiang,
Xueying Song,
Zhichao Sheng,
Zibing Meng
Publication year - 2022
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 - 10.1088/1742-6596/2195/1/012042
Subject(s) - transformer , computer science , swarm behaviour , relation (database) , voltage , control theory (sociology) , data mining , electronic engineering , engineering , artificial intelligence , electrical engineering , control (management)
User-transformer relation identification plays an important role in the correct management of low-voltage area archives and the improvement of line loss. In order to obtain an accurate user-transformer relation identification, this paper proposes a user-transformer relation identification method in the low-voltage area based on power balance model and adaptive artificial fish swarm algorithm(AFSA). This method uses the summation relationship between the total meter of the transformer and the user’s meter/meter box to fit the coefficients of the power balance equation through the AFSA, then we use the coefficients and related statistical values to judge the user-transformer relation. The main innovations are: this paper proposes a power balance model to solve the problem of user-transformer relation identification, which is simpler than previous methods and has strong operability; AFSA is used to fit the regression coefficients of the power balance equation, which has advantages in calculation accuracy and efficiency compared with the traditional least squares method; an improvement strategy of adaptive step length is proposed to make the ability of AFSA to find superior stronger. By selecting real station data for verification, the result shows that the method in this paper can quickly and accurately identify user’s meters/meter boxes with abnormal user-transformer relationship, the method in this paper has high computational efficiency and recognition accuracy without additional labor and hardware costs.