The Chaotic beamforming adaptive algorithm is new adaptive method for antenna array’s radiation pattern synthesis. This adaptive method based on the optimization of the Least Mean Square algorithm using Chaos theory enables fast adaptation of antenna array radiation pattern, reduction of the noisy reference signal’s impact, and the improvement of the tracking capabilities. We performed simulations for linear and circular antenna arrays. We also compared the performances of the used and existing algorithms in terms of the radiation pattern comparison.
This paper deals with parameter estimation of single-phase transformer equivalent circuit by using Chaotic Optimization Approach (COA). Unknown transformer equivalent circuit parameters need to be accurately estimated for the best possible matching between the measured and the estimated transformer output characteristics (for example, output power—load resistance characteristic). Unlike literature approaches which apply different estimation techniques and are based either on the nameplate data or the load data obtained from experiments, in this paper the use of COA is evaluated on both types of input data. For two single-phase transformers, different with respect to machine power and voltage levels, the COA-based parameter estimation is compared to various literature techniques as well as to classical method based on open-circuit and short-circuit tests. The results show that COA outperforms other approaches in terms of average error between the measured and the estimated values of the primary current, secondary current and secondary voltage at full load, or between the measured and the estimated output characteristics. The effectiveness of COA is additionally confirmed through its application on laboratory 2kVA, 220 V/110 V, 50 Hz single-phase transformer.
Purpose
The purpose of this paper is to propose a chaotic optimization method for identifying the parameters of the Jiles–Atherton (J-A) hysteresis model.
Design/methodology/approach
The J-A model has five parameters which are assigned with physical meaning and whose determination is demanding. To determine these parameters, the fitness function, which represents the difference between the measured and the modeled hysteresis loop, is formed. Optimal parameter values are the values that minimize the fitness function.
Findings
The parameters of J-A model for three magnetic materials are determined. The model with the optimal parameters is validated using measured data and comparison with particle swarm optimization algorithm, genetic algorithm, pattern search and simulated annealing algorithm. The results show that the proposed method provides better agreement between measured and modeled hysteresis loop than other methods used for comparison. The proposed method is also suitable for simultaneous optimization of multiple hysteresis loops.
Originality/value
Chaotic optimization method is implemented for the first time for J-A model parameter identification. Numerical comparisons with results obtained with other optimization algorithms demonstrate that this method is a suitable alternative in parameters identification of J-A hysteresis model. Furthermore, this method is easy to implement and set up.
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