Linear static parametric models for online parameter identification of power load models

Nouman Ashraf, Alexis Polycarpou, Yiannis Ioannides, Marios Lestas, Michalis A. Michael

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Power load models are significant for power system transient analysis and voltage stability. However, as the load behavior is time varying in nature, the model parameters are also time varying and online parameter estimation algorithms are thus required. In this work we cast static and dynamic load models in linear parametric form and use them as a baseline to develop online parameter identification algorithms. The linear static parametric representation allows the consideration of a wide class of online parameter estimation algorithms. Here we indicatively consider quadratic and integral cost functions. The considered online estimation algorithms are assessed using real data obtained from the Electricity Authority of Cyprus. The simulation results indicate convergence to the expected parameter values.

Original languageEnglish
Title of host publication2017 IEEE Manchester PowerTech, Powertech 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509042371
DOIs
Publication statusPublished - 13 Jul 2017
Externally publishedYes
Event2017 IEEE Manchester PowerTech, Powertech 2017 - Manchester, United Kingdom
Duration: 18 Jun 201722 Jun 2017

Publication series

Name2017 IEEE Manchester PowerTech, Powertech 2017

Conference

Conference2017 IEEE Manchester PowerTech, Powertech 2017
Country/TerritoryUnited Kingdom
CityManchester
Period18/06/201722/06/2017

Keywords

  • Adaptive Control
  • Online Parameter Estimation
  • Power Load Models
  • Wide Area Measurement Systems

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