Uncertainty Distribution Assessment of Jiles-Atherton Parameter Estimation for Inrush Current Studies

TL;DR

This work addresses the challenge of accurately estimating JA hysteresis parameters for transformer inrush current studies under uncertainty. It introduces an uncertainty-aware framework that propagates PDF-based parameter initialization through metaheuristic optimizers (GA, PSO, DE) and evaluates performance on two core materials using RMSE between measured and JA-predicted flux density. Key findings show that Gaussian PDF initialization, especially at 5% sigma, improves both accuracy and computation time, with DE performing well under low-information conditions and PSO excelling in certain Gaussian settings. The approach enhances transformer transient modeling and offers a practical decision-support tool for selecting initialization strategies and algorithms in industrial JA parameter estimation. $$B$$-H curve fidelity and efficient offline JA calibration underpin improvements in inrush current minimization and transformer reliability.

Abstract

Transformers are one of the key assets in AC distribution grids and renewable power integration. During transformer energization inrush currents appear, which lead to transformer degradation and can cause grid instability events. These inrush currents are a consequence of the transformer's magnetic core saturation during its connection to the grid. Transformer cores are normally modelled by the Jiles-Atherton (JA) model which contains five parameters. These parameters can be estimated by metaheuristic-based search algorithms. The parameter initialization of these algorithms plays an important role in the algorithm convergence. The most popular strategy used for JA parameter initialization is a random uniform distribution. However, techniques such as parameter initialization by Probability Density Functions (PDFs) have shown to improve accuracy over random methods. In this context, this research work presents a framework to assess the impact of different parameter initialization strategies on the performance of the JA parameter estimation for inrush current studies. Depending on available data and expert knowledge, uncertainty levels are modelled with different PDFs. Moreover, three different metaheuristic-search algorithms are employed on two different core materials and their accuracy and computational time are compared. Results show an improvement in the accuracy and computational time of the metaheuristic-based algorithms when PDF parameter initialization is used.

Figures (14)

• Figure 1: B-H curves for the two materials analysed in this study.
• Figure 2: Overall block diagram of the proposed framework.
• Figure 3: Error PDF for different algorithms with uniform parameter initialization for material A.
• Figure 4: Computational time PDF for different algorithms with uniform parameter initialization for material A.
• Figure 5: Comparison of the probability distribution of error and computational time for material A.