Regularised Learning with Selected Physics for Power System Dynamics

Haiwei Xie, Federica Bellizio, Jochen L. Cremer*, Goran Strbac

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Due to the increasing system stability issues caused by the technological revolutions of power system equipment, the assessment of the dynamic security of the systems for changing operating conditions (OCs) is nowadays crucial. To address the computational time problem of conventional dynamic security assessment tools, many machine learning (ML) approaches have been proposed and well-studied in this context. However, these learned models only rely on data, and thus miss resourceful information offered by the physical system. To this end, this paper focuses on combining the power system dynamical model together with the conventional ML. Going beyond the classic Physics Informed Neural Networks (PINNs), this paper proposes Selected Physics Informed Neural Networks (SPINNs) to predict the system dynamics for varying OCs. A two-level structure of feed-forward NNs is proposed, where the first NN predicts the generator bus rotor angles (system states) and the second NN learns to adapt to varying OCs. We show a case study on an IEEE-9 bus system that considering selected physics in model training reduces the amount of needed training data. Moreover, the trained model effectively predicted long-term dynamics that were beyond the time scale of the collected training dataset (extrapolation).

Original languageEnglish
Title of host publication2023 IEEE Belgrade PowerTech, PowerTech 2023
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
ISBN (Electronic)9781665487788
DOIs
Publication statusPublished - 2023
Event2023 IEEE Belgrade PowerTech, PowerTech 2023 - Belgrade, Serbia
Duration: 25 Jun 202329 Jun 2023

Publication series

Name2023 IEEE Belgrade PowerTech, PowerTech 2023

Conference

Conference2023 IEEE Belgrade PowerTech, PowerTech 2023
Country/TerritorySerbia
CityBelgrade
Period25/06/2329/06/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Dynamic Security Assessment
  • Machine Learning
  • Physics-Informed
  • Transient Prediction

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