Small-Signal Refinement of Power System Static Load Modelling Techniques

Gareth McLorn, Seán McLoone*

*Corresponding author for this work

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

1 Citation (Scopus)
170 Downloads (Pure)

Abstract

Loads are often represented as a weighted combination of constant impedance (Z), current (I) and power (P) components, so called ZIP models, by various power systems network simulation tools. However, with the growing need to model nonlinear load types, such as LED lighting, ZIP models are increasingly rendered inadequate in fully representing the voltage dependency of power consumption traits. In this paper we propose the use of small-signal ZIP models, derived from a neural network model of appliance level consumption profiles, to enable better characterizations of voltage dependent load behavior. Direct and indirect approaches to small-signal ZIP model parameter estimation are presented, with the latter method shown to be the most robust to neural network approximation errors. The proposed methodology is demonstrated using both simulation and experimentally collected load data.

Original languageEnglish
Title of host publicationAdvanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration - International Conference on Life System Modeling and Simulation, LSMS 2017 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, Proceedings
PublisherSpringer Verlag
Pages467-476
Number of pages10
Volume763
ISBN (Print)9789811063633
DOIs
Publication statusPublished - 25 Aug 2017
EventInternational Conference on Life System Modeling and Simulation, LSMS 2017 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017 - Nanjing, China
Duration: 22 Sep 201724 Sep 2017

Publication series

NameCommunications in Computer and Information Science
Volume763
ISSN (Print)1865-0929

Conference

ConferenceInternational Conference on Life System Modeling and Simulation, LSMS 2017 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017
Abbreviated titleICSEE 2017
CountryChina
CityNanjing
Period22/09/201724/09/2017

Keywords

  • Exponential models
  • Load modelling
  • Neural networks
  • ZIP models

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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  • Cite this

    McLorn, G., & McLoone, S. (2017). Small-Signal Refinement of Power System Static Load Modelling Techniques. In Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration - International Conference on Life System Modeling and Simulation, LSMS 2017 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, Proceedings (Vol. 763, pp. 467-476). (Communications in Computer and Information Science; Vol. 763). Springer Verlag. https://doi.org/10.1007/978-981-10-6364-0_47