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The parameters set of the Jiles-Atherton hysteresis model is identified by using a real coded genetic algorithm. The parameters identification is performed by minimizing the mean squared error between experimental and simulated magnetic field curves. The procedure is validated by comparing experimental and simulated results.
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... and measured hysteresis loops for a different ma- terial B are shown in Fig. 4; the corresponding field curves are presented in Fig. 5. The search ranges and the optimized param- eters set are shown in Table II. The comparison between these results shows a good agreement. Fig. 6 and 7 show, respectively, the evolution of the MSE for materials A and B. We observe that the error decreases quickly and the algorithm ...
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Citations
... By doing so, the algorithm creates better solutions. The main parameters needed for GA are population size, β (integer for calculating the probability for each parent's selection), mutation step size and mutation rate [31]. ...
... For material A, the saturation is known and M s is not included in the estimation procedure. The rest of the material A parameters are set based on approximations and literature values [19], [31]. The limits for material B are higher because previous analysis indicated that the parameter values for material B are higher than for material A [33]. ...
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.
... In recent years, there have also been many scholars using different algorithms to achieve the identification of the key parameters of the J-A hysteresis model. Leite [16] used a Genetic Algorithm to identify the unknown parameters of the J-A hysteresis model; the identification results have small error and high accuracy, but the classical Genetic Algorithm cannot solve the local optimal solution problem. Trapanese [17] introduced chaos theory and a simulated annealing algorithm to the classical genetic algorithm, which solved the problem of the classical genetic algorithm [18] and improved the accuracy of identification, but the calculation speed is slow and the convergence time is long; Chen [19] proposed an improved J-A hysteresis model, so that the number of parameters to be identified increased from five to seven, and the key parameters were identified using a differential evolutionary algorithm, which was able to identify the parameter values more quickly, but it had a large error in accuracy and the algorithm had a complicated calculation process. ...
... Recalculate the inertia weight ω according to equation (16) Calculate the fitness of each particle according to equation(15) Figure 6. Flow chart of the improved PSO. ...
As a typical intelligent device, magnetorheological (MR) dampers have been widely applied in vibration control and mitigation. However, the inherent hysteresis characteristics of magnetic materials can cause significant time delays and fluctuations, affecting the controllability and damping performance of MR dampers. Most existing mathematical models have not considered the adverse effects of magnetic hysteresis characteristics, and this study aims to consider such effects in MR damper models. Based on the magnetic circuit analysis of MR dampers, the Jiles–Atherton (J-A) model is adopted to characterize the magnetic hysteresis properties. Then, a weight adaptive particle swarm optimization algorithm (PSO) is introduced to the J-A model for efficient parameter identifications of this model, in which the differential evolution and the Cauchy variation are combined to improve the diversity of the population and the ability to jump out of the local optimal solution. The results obtained from the improved J-A model are compared with the experimental data under different working conditions, and it shows that the proposed J-A model can accurately predict the damping performance of MR dampers with magnetic hysteresis characteristics.
... Jiles, back in 1989 [6] and 1992 [4], wrote down instructions and advice to obtain the values of variables from the measured hysteresis loops. In the last two decades, different optimization techniques (i.e., generic algorithm, particle swarm optimization, etc.) were used to obtain model variable values more accurately [7][8][9][10][11]. ...
... or with M against H [2] where the dependence of M over H is represented in differential form (10), with the aim that both relationships contain the same information. ...
... However, at the same time, the energy balance equation is changed. From (10) and (14), it follows that M is equal to Mirr and, further, from (12), that Mrev is zero, meaning that variable = 0 because M cannot be equal to Man (if = , it means there are no hysteresis losses). According to these facts, the irreversible magnetization is: ...
The aim of the proposed paper is to harmonize the Jiles–Atherton (J–A) static hysteresis loop models presented by different authors at different publication dates. The reviewed papers, which explain the hysteresis loop model of magnetic materials originally developed by Jiles are taken into consideration due to their different variable nomenclature and even physical meaning. Inconsistency between Jiles’ referenced works is shown and the consequences are presented as differences between BH curve behaviors and, on the application level, as differences in calculated static hysteresis losses. The harmonization of J–A hysteresis models is presented and confirmed by numerical calculations and measured data.
... The defined problem has a large number of variables, which are required to be optimized for reaching an optimal solution. The realcoded GA, where the chromosome is represented as a real number (Deb, 2000), is suited for problems with a large number of variables (Leite et al., 2004;Subbaraj et al., 2011). Chromosomes within the population use selection, crossover, and mutation to evolve the population over iterations. ...
The aim of a mining complex optimization is to maximize the economic value of the mining project as a whole. To maximize the economic value, it is required to simultaneously optimize the mining extraction sequence and destination of the material into various processing streams. This work presents a global optimization model to simultaneously optimize all aspects of the mining complex under uncertainty. To solve the mining complex problem, the method uses a combination of the maximum flow and a genetic algorithm to define the optimal production sequence, and the flow of extracted material into various destination streams are defined based on the dynamic cutoff grade optimization and block economic values. The dynamic cutoff grade is optimized using Lane's method. An application for a copper-gold mining complex indicates that the optimizer generates results that reduce the risk of not meeting the targets. When compared to commercial deterministic mine planning software, proposed algorithm generates 9.08% higher net present value and the stochastic design generated 13.70% higher expected net present value compared. Two different destination policies are evaluated to study the impact of destination policies on the net present value. Due to change in destination policies, difference of 4.36% is observed in net present value for the stochastic model.
... The applied procedure for determination of the JA model parameters is very important in the acuurcy of the results. Some have used Genetic algorithm for optimization and obtaining the JA parameters as such that the difference of the modeled hysteresis loops and the measured one is minimized [26]. In this paper, the JA model based on magnetic vector potential using FE modeling is implemented. ...
Ferroresonance leads to a high saturation of the transformer core. This causes severe rise of the amplitude of current and voltage of the transformer and leads to current and voltage non-sinusoidal waveforms. Therefore, the increase of the transformer losses and its temperature rises during Ferroresonance can overstress the transformer insulation system and elevate the chance of the transformer failure. In this paper electromagnetic and thermal behaviors of the transformer during Ferroresonance are investigated using a three dimensional finite element model. The modeling of the core is the most important challenge in the solution of the transformer electromagnetic equations during Ferroresonance. Thus, the Jiles-Atherton (JA) model is employed to model the core hysteresis loop. Based on the proposed approach, the behavior of a single-phase transformer under fundamental and sub-harmonic Ferroresonance modes are simulated and the results are compared with the experimental results. Furthermore, a thermal equivalent circuit is developed, based on the simulated transformer losses and thereby, the transformer temperature rises are estimated during Ferroresonance.
... A real-coded representation of a GA is applicable in which the chromosomes are expressed as real numbers. Real-coded GAs are efficient (Deb, 2000) and are suited for problems with a large number of variables (Leite et al., 2004;Subbaraj et al., 2011). Chromosomes within the population are evolved over successive generations using genetic operations such as selection, crossover and mutation. ...
An open pit mining operation is a complex system that constitutes several components or processes. An optimal production sequence that defines timing of extraction and a dynamic cut-off grade policy that defines the supply of materials from sources to destinations within the system are crucial to the success of an operation. In current practice, separate sequencing and cut-off grade models achieve these important milestones as part of strategic planning. This paper presents a mathematical model that derives the optimal extraction sequence and cut-off grade policy simultaneously considering grade uncertainty and stockpiling. A framework of genetic, maximum flow and cut-off grade algorithms solves this complex non-linear problem. An application of the method at realistic copper and gold mining operations reveals the value (up to 29% increase in discounted value) of stockpiling as well as risk quantification under uncertainty.
... This is done by choosing suitable boundaries for the unknowns. The minimal and maximal values for the above-mentioned parameters in the JA+R model are given in Appendix A. These bounds are based on successively applying the SFLA to learn the behavior of the optimisation process and the already known material parameters found in the literature [25], [26], [18], [36]. ...
The purpose of this paper is to estimate the parameters of the Jiles-Atherton hysteresis model, based on minor-loop measurement data in weak applied fields. The well-known hysteresis model by Jiles and Atherton serves as a basis of this work with an extension for closure of minor loops. In order to represent minor loops correctly, a dissipative factor is introduced. A methodology to obtain the initial magnetisation of a specimen is defined, based on an expansion in terms of higher-order Gaussian functions. The methodology is implemented within a finite-element method using an interconnection between MATLAB and COMSOL. This interconnection allows the investigation of potentially large ferromagnetic objects to be calibrated to the proposed ferromagnetic model in weak fields. The proposed methodology was verified using an original approach. The approach relies on the use of a sensor array that makes it possible to detect local variations of magnetic properties in steel plates. Material parameters for our test specimen are succesfully obtained by means of experimental data, using the Shuffled Frog Leaping optimisation algorithm. An analysis of the obtained results show that the calibrated model is able to represent the measurement data accurately.
... To overcome the drawbacks of the direct iterative methods, artificial intelligence methods have been proposed in recent years to estimate the JA hysteresis model parameters [22][23][24][25][26]. Among these methods, genetic algorithm (GA) has been confirmed to be effective, even though the initial values are unknown or the problem is of multimodal and complex nature. ...
... For comparison with the other optimisation methods, the proposed JA hysteresis model is also simulated by GA [22], SA [21], and DE [25]. The maximum generation of the four methods is 200. ...
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.
... From this observation, we choose to fix the selected simplified crystallographic texture for each material. A Genetic Algorithm [9] is used to fit the hysteresis loop under compressive stress considering the RMS error between model and experimental results as objective function. Parameter k is considered as a piecewise linear function of γ using 5 reference values of γ (0; 0.25; 0.5; 0.75; 1). ...
Based on a multiscale modelling of the anhysteretic magnetization taking into account mechanical stress and crystallographic texture effects, an extension of the Jiles-Atherton (J-A) hysteresis model is proposed. The magnetization and the volume fractions given by the multiscale approach are advantageously used in the J-A model to modify the anhysteretic magnetization and the pinning parameter. The parameters of the proposed model are identified in order to fit with characterization results under compressive stress using two sets of experimental data. Using the same optimized parameters, the model is tested for the representation of loops under other stress conditions to evaluate its prediction capabilities.
... The combined use of GA and SA for parametric identification problems has been also found effective by Fulginei and Salvini [106] in case of magnetic hysteresis characterized by symmetric or asymmetric loops. Note that the first attempts in using GA for the parametric identification of the Jiles-Atherton model exploit a binary codification [8,333], but the applications based on real codification soon became more popular [66,184]. ...
This work aims to provide a broad overview of computational techniques belonging to the area of artificial intelligence tailored for identification of nonlinear dynamical systems. Both parametric and nonparametric identification problems are considered. The examined computational intelligence techniques for parametric identification deal with genetic algorithm, particle swarm optimization, and differential evolution. Special attention is paid to the parameters estimation for a rich class of nonlinear dynamical models, including the Bouc–Wen model, chaotic systems, the Jiles–Atherton model, the LuGre model, the Prandtl–Ishlinskii model, the Preisach model, and the Wiener–Hammerstein model. On the other hand, genetic programming and artificial neural networks are discussed for nonparametric identification applications. Once the identification problem is formulated, a detailed illustration of the considered computational intelligence techniques is provided, together with a comprehensive examination of relevant applications in the fields of structural mechanics and engineering. Possible directions for future research are also addressed.
