An Improved Method for Acquisition of the Parameters of Jiles-Atherton Hysteresis Scalar Model Using Integral Calculus

Mendes, Filomena Barbosa Rodrigues; Leite, Jean V.; Batistela, Nelson Jhoe; Sadowski, N.; Suárez, Fredy Maglorio Sobrado

doi:10.1590/2179-10742017v16i1880

Cited by 6 publications

(4 citation statements)

References 14 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mendes et al put forward a method for the approximate calculation of the parameters with the help of integral [4] . Leite et al proposed an optimized integral algorithm for the acquisition of parameter models [5] . In addition, Krzysztof Chwastek et al brought forward a direct search algorithm to determine the parameters [6] , and Ramezan-Ali Naghizadeh et al obtained the parameters using the Shuffled Frog Leaping Algorithm (SFLA) [7] .Therefore, it is challenging to calculate the J-A model parameters.…”

Section: Introductionmentioning

confidence: 99%

A new method for calculating the parameter k of Jiles-Atherton theory

Chen

Yang

2023

International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022)

View full text Add to dashboard Cite

The Jiles-Atherton model is a key theory for researching the hysteresis loop of ferromagnetic materials. In application, its theoretical parameters are difficult to determine. This paper proposes a new method for calculating the parameter k based on its definition in the Jiles-Atherton theory and the hypothesis of pinning energy. The influence of the maximum magnetization intensity (Mm) on the calculation results of the theoretical parameter k value is discussed by using the data reported in the literature, The value of (Mm) is determined, Then, the reliability of the calculated k value is verified by the measured initial magnetization curve.

show abstract

Section: Introductionmentioning

confidence: 99%

A new method for calculating the parameter k of Jiles-Atherton theory

Chen

Yang

2023

International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022)

View full text Add to dashboard Cite

show abstract

“…Meanwhile, Naghizadeh et al generated the parameters using the shuffled frog leaping algorithm [8]. To acquire the parameters, Mendes et al recommended the trust region dogleg algorithm [9], and Rubežić et al introduced the chaotic optimization method [10]. The algorithms mentioned above exhibits the remarkable advantages of simple concepts, easy implementation, and fast convergence speed, however, each method has its own unique applicability.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of Jiles-Atherton Model Based on Levy Whale Optimization Algorithm

Chen

Wang

2022

IEEE Access

View full text Add to dashboard Cite

The Jiles-Atherton model is key to researching the hysteresis loop. The focus of scholars across various countries has always been the parameter identification of the Jiles-Atherton model. This paper on the Levy whale optimization algorithm (LWOA), based on the whale optimization algorithm (WOA), proposes to overcome the disadvantage that WOA tends to involve the local optimum. The recommended algorithm uses the Levy flight strategy instead of the encircling prey policy since the former improves the global search. Therefore, the new algorithm is better at stability and calculation accuracy. To substantiate the efficacy of the proposed algorithm, it is tested against six benchmark functions and compared with the WOA, particle swarm optimization (PSO), grey wolf algorithm (GWO), and shuffled frog leaping algorithm (SFLA). In addition, the proposed algorithm is applied to realize two classical engineering problems, such as the tension/compression spring and welded beam design issues. The experimental findings reveal that the proposed algorithm is highly competitive with metaheuristic optimizers and improves the algorithm's performance. To address the poor stability of the J-A model parameter identification, an improved calculation method for parameter k and the reduced parameter ranges of the model parameters a and α were combined with LWOA. The proposed algorithm is called C-LWOA, which is compared with LWOA, PSO, GWO, SFLA, and the cuckoo search (CS) based on the data reported in the literature. Moreover, the simulation results demonstrate that the stability and calculation accuracy of the parameter identification by the C-LWOA was significantly strengthened. Equally important, the calculation error was within 0.2%. Finally, the proposed algorithm was subsequently used to fit the actual measurements of the hysteresis loop of permalloy.

show abstract

“…Thus, the JA model is extensively used in electromagnetic transients for modelling electromagnetic devices, such as power transformers and electromagnetic voltage transformers [10]. However, challenges in estimating the JA hysteresis model parameters remain when using this model to represent the non‐linear characteristics of electromagnetic devices [11–13].…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation of extended Jiles–Atherton hysteresis model based on ISFLA

Zou

2020

IET Electric Power Applications

View full text Add to dashboard Cite

This study introduces an extended Jiles–Atherton (JA) hysteresis model, which considers dynamic loss and anisotropy. The two considerations facilitate an accurate non‐linearity representation of electromagnetic devices, thereby resulting in a precise agreement between the simulated and measured hysteresis loops. To achieve this goal, JA hysteresis model parameters must be estimated by an optimisation algorithm. Therefore, an improved shuffled frog‐leaping algorithm (ISFLA) is proposed in this study. Differential evolution (DE) mutation operator, adaptive step size factor, and inertia weight factor are considered. Then, the implementation of the ISFLA is discussed using MATLAB. The proposed ISFLA is verified by the measured hysteresis loops of grain‐oriented silicon toroidal core. The comparison between genetic algorithm, simulated annealing, DE, and ISFLA is discussed. Results show that the proposed ISFLA demonstrates better global optimum ability, lower computational burden, and faster convergence rate than the other three methods.

show abstract

An Improved Method for Acquisition of the Parameters of Jiles-Atherton Hysteresis Scalar Model Using Integral Calculus

Cited by 6 publications

References 14 publications

A new method for calculating the parameter k of Jiles-Atherton theory

A new method for calculating the parameter k of Jiles-Atherton theory

Parameter Identification of Jiles-Atherton Model Based on Levy Whale Optimization Algorithm

Parameter estimation of extended Jiles–Atherton hysteresis model based on ISFLA

Contact Info

Product

Resources

About