Evolutionary Large-Scale Multiobjective Optimization for Ratio Error Estimation of Voltage Transformers

Cheng HE, Ran CHENG, Chuanji ZHANG, Ye TIAN, Qin CHEN, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

65 Citations (Scopus)


Ratio error (RE) estimation of the voltage transformers (VTs) plays an important role in modern power delivery systems. Existing RE estimation methods mainly focus on periodical calibration but ignore the time-varying property. Consequently, it is difficult to efficiently estimate the state of the VTs in real time. To address this issue, we formulate a time-varying RE estimation (TREE) problem into a large-scale multiobjective optimization problem, where the multiple objectives and inequality constraints are formulated by statistical and physical rules extracted from the power delivery systems. Furthermore, a set of TREE problems from different substations is systematically formulated into a benchmark test suite for characterizing their different properties. The formulation of these TREE problems not only transfers an expensive RE estimation task to a relatively cheaper optimization problem but also promotes the research in large-scale multiobjective optimization by providing a real-world benchmark test suite with complex variable interactions and correlations to different objectives. To the best of our knowledge, this is the first time to formulate a real-world problem into a benchmark test suite for large-scale multiobjective optimization, and it is also the first work proposing to solve TREE problems via evolutionary multiobjective optimization. © 1997-2012 IEEE.
Original languageEnglish
Article number8962275
Pages (from-to)868-881
Number of pages14
JournalIEEE Transactions on Evolutionary Computation
Issue number5
Early online date17 Jan 2020
Publication statusPublished - Oct 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 61903178 and Grant 61906081, in part by the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant 2017ZT07X386, in part by the Shenzhen Peacock Plan under Grant KQTD2016112514355531, and in part by the Program for University Key Laboratory of Guangdong Province under Grant 2017KSYS008.


  • Benchmark test suite
  • inequality constraint
  • large-scale multiobjective optimization
  • time-varying ratio error estimation (TREE)
  • voltage transformer (VT)


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