Searcharxiv⌕ Search

arXiv · 2611.05109

Stray Losses in Power Transformers: A Comparative Study of Modeling Frameworks for Mild Steel

Abstract

Accurately predicting eddy-current and hysteresis losses in mild steel components such as the structural parts and tank of power transformers remains a challenge in transformer design. Conventional numerical approaches for eddy-current loss analysis require discretization at a fraction of the skin depth. While accurate, such fine volumetric mesh becomes computationally prohibitive for large power transformers. Moreover, hysteresis loss is not always included in this analysis. To investigate the trade-off between computational cost and accuracy, this work presents a comparative analysis of eight distinct modeling frameworks for predicting both eddy-current and hysteresis losses in mild steel. These include efficient methods avoiding meshing, such as standard impedance boundary conditions, and more accurate approaches retaining full volumetric meshing, namely complex permeability, nonlinear single-valued B-H curve, and Jiles-Atherton hysteresis modeling. To accurately represent the material's electromagnetic behavior, the properties of the mild steel used are measured directly from samples. Finally, loss measurements are conducted to benchmark all eight frameworks, demonstrating mean absolute percentage errors between predictions and measurements generally below 20%.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Micha Swaanen, Timo Overboom, Siamak Pourkeivannour, Tom Huiskamp, Guus Pemen. 2026-11-03. Stray Losses in Power Transformers: A Comparative Study of Modeling Frameworks for Mild Steel. https://arxiv.org/abs/2611.05109

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

KEEP EXPLORING

Related papers

Reinforcement Learning based Traffic Signal Control for Queue Minimization: From Lane-based to Laneless Traffic

Efficient traffic signal control (TSC) is essential for reducing congestion and delays. Traditional TSC methods struggle to capture the dynamics of laneless, heterogeneous traffic. We propose a reinforcement learning (RL) based adaptive TSC framework whose reward depends only on approach-level queue length, motivated by the difficulty of obtaining per-vehicle measurements in laneless traffic, and evaluate it using SUMO on a conventional lane-based, homogeneous (LBHO) intersection and on a non-orthogonal laneless, heterogeneous (LLHE) intersection. On the LBHO site, the lowest mean configuration has about 23\% lower mean queue length than a Dynamic Webster baseline, and the unselected Full-State configuration about 15\% lower. On the LLHE site, all RL configurations have 66-73\% lower mean queue length than a Dynamic Webster baseline under nominal demand, and 63--74\% lower under two higher-demand scenarios evaluated without retraining. Our algorithm also outperforms actuated control and a RESCO-DQN baseline on mean queue length. We additionally release a laneless, heterogeneous-vehicle SUMO framework integrated with RL based signal control, to our knowledge the first such open implementation on a current SUMO version.

eess.SY↗

A Deep State-Space Model Compression Method using Upper Bound on Output Error

We study deep state-space models (Deep SSMs) that contain linear quadratic-output (LQO) systems as internal blocks and present a compression method with a provable output error guarantee. We first derive an upper bound on the output error between two Deep SSMs and show that the bound can be expressed in terms of the $h^2$-error norms between the layerwise LQO systems. In particular, we show that reducing the $h^2$ approximation errors of the LQO systems placed in shallow layers is effective in reducing the derived upper bound on the output error. Next, we formulate an optimization problem for the derived upper bound and develop a gradient-based MOR method. In the numerical experiments, using the IMDb task from the LRA benchmark, we demonstrate the effectiveness of the proposed upper-bound-based compression method. In particular, we show that the number of trainable parameters can be reduced by approximately 60\% without retraining while maintaining the performance of the original model.

eess.SY↗

Safe Navigation in Cluttered Environments Via Spline-Based Harmonic Potential Fields

We provide a motion-planning framework for target reaching and obstacle avoidance in cluttered environments. Given a polyhedral decomposition of the feasible space, the agent is guided through a prescribed sequence of cells by a suitable control policy. For each cell, we construct a potential induced by a harmonic function on the unit disk whose Dirichlet boundary condition is represented by a cardinal B-spline curve. A detailed analysis of the curve periodicity, support, and control-point selection provides an explicit analytical construction of these potentials and the associated vector fields. We further characterize inter-cell control continuity and forward invariance of non-exit facets, and introduce a smoothed control law ensuring continuous switching between independently constructed neighboring cells. Numerical results illustrate that the resulting framework safely guides the agent along the prescribed cell sequence from the initial point to the target.

eess.SY↗