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Data-driven p-norms for estimating transmission loss coefficients in power systems

Author

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  • Oscar Danilo Montoya
  • Walter Gil-González
  • Luis Fernando Grisales-Noreña

Abstract

This research introduces a novel convex methodology for estimating transmission loss coefficients (B-coefficients) in power systems using a data-driven approach based on power system measurements. To enhance estimation accuracy and practical relevance, the model is evaluated across a wide spectrum of operating conditions, incorporating random variations in active power injections and demand profiles modeled via uniform and Gaussian distributions. A semi-definite programming (SDP) model leveraging p-norm formulations is proposed to derive the B-coefficients efficiently. Numerical evaluations on IEEE 14-, 39-, 57-, and 118-bus test feeders demonstrate the effectiveness and robustness of the approach, yielding average estimation errors between −6% and 5% across diverse scenarios. These results confirm the reliability of the proposed methodology, contributing to improved accuracy in transmission loss modeling and supporting more efficient power system operations.

Suggested Citation

  • Oscar Danilo Montoya & Walter Gil-González & Luis Fernando Grisales-Noreña, 2026. "Data-driven p-norms for estimating transmission loss coefficients in power systems," PLOS ONE, Public Library of Science, vol. 21(3), pages 1-11, March.
    • Handle: RePEc:plo:pone00:0345033
    DOI: 10.1371/journal.pone.0345033
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