NDLI: Ladder network parameters identification of an isolated winding by combining the intelligent optimisation algorithm and GNIA

Content Provider	IET Digital Library
Author	Ren, Fuqiang Xiao, Yao Zhan, Cao Liu, Yong Chao Yang, Fan Ji, Shengchang Zhu, Lingyu
Abstract	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.
Starting Page	296
Ending Page	304
Page Count	9
ISSN	17518687
Volume Number	13
e-ISSN	17518695
Issue Number	Issue 2, Jan (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2018.6414
Journal	IET Generation, Transmission & Distribution
Publisher Date	2018-11-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Combined Algorithm Excellent Global Search Ability Frequency Response Frequency Response Function Data Gauss–Newton Iteration Algorithm Genetic Algorithm GNIA Identification Result Intelligent Algorithm Intelligent Optimisation Algorithm Interpolation And Function Approximation Isolated Winding Show Iterative Method Jacobian Matrix Ladder Network Parameter Identification Least Squares Approximation Newton Method Numerical Analysis Optimisation Optimisation Efficiency Optimisation Technique Parameter Estimation Search Problem Sharply Dropped Calculation Workload Traditional Identification Method Transformer Winding Transformer Windings
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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