Open AccessLadder network parameters identification of an isolated winding by combining the intelligent optimisation algorithm and GNIA
Author(s) -
Ren Fuqiang,
Xiao Yao,
Zhan Cao,
Liu Yong,
Yang Fan,
Ji Shengchang,
Zhu Lingyu
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.6414
Subject(s) - algorithm , jacobian matrix and determinant , computation , computer science , identification (biology) , newton's method , key (lock) , transformer , mathematical optimization , mathematics , voltage , engineering , botany , biology , physics , computer security , nonlinear system , quantum mechanics , electrical engineering
The ladder network parameter identification for transformer winding is crucial for the interpretation of the frequency response function data. The traditional identification method, mainly based on intelligent optimisation algorithm, is generally very time‐consuming due to a large amount of computation. This study proposes to combine the intelligent algorithm and Gauss–Newton iteration algorithm (GNIA) to improve the optimisation efficiency notably with a sharply dropped calculation workload. These two methods are well‐complementary since the intelligent algorithm holds excellent global search ability while the search of the GNIA is directional and quantitative. This study solves three key problems for the combined algorithms. The first problem is the calculation of the least‐square correction solution to the network parameters in the iteration algorithm. The treatment of the ill‐conditioned Jacobian matrix in the iteration algorithm is the second challenge. Another issue is the determination of the network parameter with zero sensitivity. The identification results on an isolated winding show that the combined algorithms can obtain a more precise solution with far less amount of computation.