Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2607.23536

Sensitivity Analysis of Dynamic Line Rating for ACSR Conductors using IEEE-738

Shashank Singh·Ashish Kumar Mishra·Vinod M. P.·Christian Romeis
View original source ↗Read source document ↗Export metadata ↓

Abstract

Dynamic Line Rating (DLR) is a novel technique that enhances the utilization of transmission line capacity. It is nevertheless unclear how much measurement uncertainty in important environmental parameters affects the DLR calculation. Using the IEEE-738 standard, this paper presents a systematic parametric sensitivity analysis of DLR for a 795 kcmil ACSR Drake conductor. The DLR computation encompasses 832 operating points which includes daytime/nighttime solar scenarios, clear/non-clear atmospheric clearness conditions, wind speeds ranging from 0 to 15.25 m/s, and ambient temperatures between 15 and 50 degrees Celsius. According to sensitivity analysis, wind sensitivity tends to decrease at higher ambient temperatures, whereas temperature sensitivity tends to increase with wind speed. Pearson correlation analysis indicates a strong negative linear association between DLR and ambient temperature, and a strong positive linear association with wind speed. A regression model incorporates both individual and interactive effects of wind and temperature, and explains over 93% of the observed DLR variability across all cases. Finally, the observations serve as a guide for operational planning and uncertainty assessment in DLR-based transmission systems.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shashank Singh, Ashish Kumar Mishra, Vinod M. P., Christian Romeis. 2026-07-26. Sensitivity Analysis of Dynamic Line Rating for ACSR Conductors using IEEE-738. https://arxiv.org/abs/2607.23536

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Observability and parameter estimation of a generic model for aggregated distributed energy resources

We propose a novel framework for estimating the parameters of an aggregated distributed energy resources (DER A) model. First, we introduce a rigorous method to determine whether all model parameters are estimable. When they are not, our approach identifies the subset of parameters that can be estimated. The proposed framework offers new insights into the number and specific parameters that can be reliably estimated based on commonly available measurements. It also highlights the limitations of calibrating such models. Second, we introduce a Kalman filtering method to calibrate the DER A model. Since we account for nonlinear effects such as saturation and deadbands, we develop a specific mechanism to handle smoothing functions within the Kalman filter. Specifically, we consider the extended and the unscented Kalman filter. We demonstrate the effectiveness of the proposed framework on a modified IEEE 34-node distribution feeder with inverter- based resources. Our findings align with the North American Electric Reliability Corporation's parameterization guideline and underscore the importance of model calibration in accurately capturing the collective dynamics of distributed energy resources installed on distribution systems.

eess.SY↗
arXiv2026

Salted Fisher Information for Hybrid Systems

Discrete events change how parameter-influence propagates in hybrid systems. Prevailing Fisher information for- mulations assume that sensitivities evolve smoothly according to continuous-time variational equations and therefore neglect the sensitivity updates induced by discrete events. This paper derives a Fisher information matrix formulation compatible with hybrid systems. To do so, we use the saltation matrix, which encodes the first-order transformation of sensitivities induced by discrete events. We call the resulting formulation the salted Fisher information matrix (SFIM). The proposed framework unifies continuous information accumulation during flows with discrete updates at event times. We also show that hybrid persistence of excitation is sufficient for the SFIM to be positive definite

eess.SY↗
arXiv2026

Min-Max Grassmannian Optimization for Online Subspace Tracking

We propose GeRoST (Geometrically Robust Subspace Tracking), an online subspace tracking algorithm that models uncertainty in a subspace using a Grassmannian ball. We derive an exact scalar dual for the worst-case subspace problem, establish conditions for a unique worst-case subspace and a Riemannian gradient, and characterize the minimum radius needed to cover a dimensional extension of the target subspace. Each update uses either a spectral direction computed in a reduced subspace or the gradient of the window reconstruction loss. Our numerical experiments show that GeRoST achieves lower mean post-fault prediction error than GREAT in system identification. In video separation, it achieves higher precision and a better precision--recall balance, as measured by the F$_1$ score, than both GREAT and GRASTA at the reported thresholds, with lower recall and longer runtime.

eess.SY↗