Online parameter identification and generic modeling derivation of a dynamic load model in distribution grids

Theofilos A. Papadopoulos, Georgios A. Barzegkar-Ntovom, Vassilis C. Nikolaidis, Panagiotis N. Papadopoulos, Graeme M. Burt

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

3 Citations (Scopus)

Abstract

The advent of smart grids and the installation of phasor measurement units in the distribution network have renewed the interest on the measurement-based load modeling approach. In this paper, a real-time load modeling and identification procedure of the well-known exponential recovery dynamic load model using synchrophasor data is presented. The performance of the proposed method is evaluated using measurements recorded in a low-voltage laboratory scale test rig. Several parameters of the procedure are investigated to evaluate the applicability of the method under real world conditions, including the impact of filtering techniques, outlier rejection, model optimization algorithms, etc. The findings of this paper verify the validity of the proposed method for realtime applications.

Original languageEnglish
Title of host publication2017 IEEE Manchester PowerTech
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages6
ISBN (Print)9781509042388, 9781509042371
DOIs
Publication statusPublished - 20 Jul 2017
Event2017 IEEE Manchester PowerTech, Powertech 2017 - Manchester, United Kingdom
Duration: 18 Jun 201722 Jun 2017

Conference

Conference2017 IEEE Manchester PowerTech, Powertech 2017
CountryUnited Kingdom
CityManchester
Period18/06/1722/06/17

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Keywords

  • dynamic load modeling
  • exponential recovery model
  • measurement-based approach
  • nonlinear least-square optimization
  • real-time identification

Cite this

Papadopoulos, T. A., Barzegkar-Ntovom, G. A., Nikolaidis, V. C., Papadopoulos, P. N., & Burt, G. M. (2017). Online parameter identification and generic modeling derivation of a dynamic load model in distribution grids. In 2017 IEEE Manchester PowerTech [7980994] Piscataway, NJ: IEEE. https://doi.org/10.1109/PTC.2017.7980994