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

TL;DR

This paper evaluates how different parameter initialization strategies, especially using PDFs, affect the accuracy and efficiency of Jiles-Atherton model parameter estimation for inrush current analysis in transformers.

Contribution

It introduces a framework to assess the impact of various initialization strategies on Jiles-Atherton parameter estimation accuracy and computational efficiency.

Findings

PDF initialization improves estimation accuracy.

PDF initialization reduces computational time.

Metaheuristic algorithms perform better with PDF initialization.

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.