Data-driven p-norms for estimating transmission loss coefficients in power systems - Oscar Danilo Montoya; Walter Gil-González; Luis Fernando Grisales-Noreña (PLoS ONE, 2026)
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.