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Persistence of excitation in an online monitoring of transformer: A system identification perspective

PUBLISHED June 19, 2023 (DOI: https://doi.org/10.54985/peeref.2306p6632433)

NOT PEER REVIEWED

Authors

Shadab Nayyer Syed1
  1. Veermata Jijabai Technological Institute (VJTI), Mumbai, India

Conference / event

Power & Energy Society General Meeting (PESGM-22) 2022, July 2022 (Virtual)

Poster summary

For effective online monitoring to assess thermal performance and life expectancy, top-oil temperature (TOT) and hot spot temperature (HST) should be accurately estimated. The thermal-electrical analogy is used for the model based on the resistance-capacitance (RC) circuit to approximate the evolution of thermal performance. The Gradient-based estimation guarantees the convergence of the parameter estimation error to zero only when the persistence of excitation (PE) condition holds for regressor signals. As the choice of input-output data used for parameter identification is crucial, the design of experiment (DoE) is generally performed in the laboratory to satisfy PE conditions.

Keywords

Hotspot, Top-oil temperature, Persistence of excitation, System identification, Tranformer

Research areas

Electrical Engineering

References

  1. D. Susa, M. Lehtonen, and H. Nordman, “Dynamic thermal modelling of power transformers,” IEEE Trans on Power Delivery, vol. 20, no. 1, pp. 197–204, Feb. 2005
  2. B. C. Lesieutre, W. H. Hagman, and J. Kirtley, “An improved transformer top oil temperature model for use in an on-line monitoring and diagnostic system,” IEEE Trans on Power Delivery, vol. 12, no. 1, pp. 249–256.
  3. Syed Shadab, J. Hozefa, K. Sonam, S. Wagh, and N. M. Singh, “Gaussian process surrogate model for an effective life assessment of transformer considering model and measurement uncertainties,” Int Journal of Electrical Power & Energy Systems, vol. 134, p. 107401, 2022.
  4. Syed Shadab “Persistence of excitation in an Online Monitoring of Transformers", Power and Energy Society (PES) General Meeting (GM), 2022

Funding

No data provided

Supplemental files

No data provided

Additional information

Competing interests
No competing interests were disclosed.
Data availability statement
Data sharing not applicable to this poster as no datasets were generated or analyzed during the current study.
Creative Commons license
Copyright © 2023 Syed. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Syed, S. Persistence of excitation in an online monitoring of transformer: A system identification perspective [not peer reviewed]. Peeref 2023 (poster).
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