Data-driven p-norms for estimating transmission loss coefficients in power systems
Oscar Danilo Montoya, Walter Gil-González, Luis Fernando Grisales-Noreña
TL;DR
This paper presents a new data-driven method to estimate transmission loss coefficients in power systems, improving accuracy and reliability using convex optimization.
Contribution
A novel convex methodology using p-norms and semi-definite programming for estimating B-coefficients in power systems.
Findings
The proposed model achieves average estimation errors between −6% and 5% across various test systems.
The method is robust and effective under diverse operating conditions with random variations in power injections and demand.
Numerical evaluations on IEEE bus systems confirm the reliability of the approach.
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…
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Taxonomy
TopicsPower System Optimization and Stability · Optimal Power Flow Distribution · Electric Power System Optimization