Robust transformer tap estimation

Yuzhang Lin, Ali Abur

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The methods currently used for transformer tap estimation are not robust against measurement errors, while the well-documented Least Absolute Value (LAV) State Estimator (SE) is not robust against transformer tap errors. This paper addresses these shortcomings by introducing the so-called Sparse Extended Least Absolute Value (SELAV) SE. By strategically modifying the formulation of LAV SE, the "sparse" nature of l1 optimization is exploited for the tap estimation problem. The transformer tap positions can be reliably estimated, while the simultaneously produced state estimates (bus voltage angles and magnitudes) remain robust against tap errors. Case studies done using IEEE 57-bus test system are provided to illustrate the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publication2017 IEEE Manchester PowerTech, Powertech 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509042371
StatePublished - Jul 13 2017
Event2017 IEEE Manchester PowerTech, Powertech 2017 - Manchester, United Kingdom
Duration: Jun 18 2017Jun 22 2017

Publication series

Name2017 IEEE Manchester PowerTech, Powertech 2017


Conference2017 IEEE Manchester PowerTech, Powertech 2017
Country/TerritoryUnited Kingdom


  • Least absolute value
  • state estimation
  • transformer tap estimation

ASJC Scopus subject areas

  • Control and Optimization
  • Modeling and Simulation
  • Computer Networks and Communications
  • Energy Engineering and Power Technology


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