Application of higher order dynamic mode decomposition to modal analysis and prediction of power systems with renewable sources of energy

Chloe Jones, Sergey Utyuzhnikov

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Abstract

Concern for climate change is driving a vastly increased use of electricity and variable renewable energy supply encourages larger and evermore interconnected power systems. Stability analysis and short-term prediction of power system output has never been more urgent or more complex. Many distributed and renewable generators contribute zero inertia to the system and increase the risk of poorly damped oscillations leading to cascading outage. Data-driven techniques, higher order dynamic mode decomposition (HODMD) and total-least-squares higher-order dynamic mode decomposition (THDMD) are applied to modal analysis and short-term prediction of frequency and power exchange deviations. The decomposition uses multiple and randomized sampling windows of historical measurements. Dominant THDMD and HODMD modes can be used to show the contribution of renewable generation, such as wind power, to wide-area oscillations. The developed techniques are applied to the analysis of blackouts in Europe (2006) and the UK (2019), as well as the separation event in Australia (2018). The obtained results demonstrate that the damping of some HODMD modes can be overestimated. Although selected HODMD modes can reconstruct and predict power system output, the results are not always reliable. In turn, THDMD can predict dominant oscillations, with reduction of noise bias error in modal analysis of noisy measurements. With low noise data both techniques can produce very similar modal results.
Original languageEnglish
Article number107925
Pages (from-to)1-14
Number of pages14
JournalInternational Journal of Electrical Power & Energy Systems
Volume138
Issue number6
Early online date19 Jan 2022
DOIs
Publication statusPublished - 1 Jun 2022

Keywords

  • higher order dynamic mode decomposition
  • oscillatory modes
  • power system stability analysis
  • prediction
  • total-least-squares higher-order dynamic mode decomposition
  • wide-area monitoring

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