Probability-box informed hysteresis modelling through metaheuristic search algorithms

• Autores: Jone Ugarte Valdivielso, Jose Ignacio Aizpurua Unanue, Manex Barrenetxea Iñarra
• Localización: Actas del XVI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados: (MAEB 2025) 28-30 de mayo, Donostia/San Sebastián / coord. por Leticia Hernando Rodríguez, Josu Ceberio Uribe, Jon Vadillo Jueguen, 2025, ISBN 978-84-1319-656-5, págs. 200-209
• Idioma: inglés
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• Resumen

  • Hysteresis modelling is crucial for many industrial applications ranging from mate- rial science to power and electrical energy systems. A frequently used approach in the magnetic materials and employed in the power and energy sector is the Jiles-Atherton (JA) model, which approximates the hysteresis curve through a partial differential equation (PDE). However, the parameter-estimation for the PDE is challenging. The present study evaluates the JA parameter estimation through the integration of probability-box (p-box) parameter initialization with metaheuristic search algorithms. The proposed p-box informed parameter ini- tialization is tested for two different iron core materials integrated with three different metaheuristic-search algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). Then, the p-box approach is compared against the classical uniform and normal distribution based parameter initialization strategies. The results show that p-box parameter initialization can be used to estimate JA parameters accurately when there is little knowledge about the magnetic material and the transformer.