Chua’s model of nonlinear coil in a ferroresonant circuit obtained using Dommel’s method and grey box modelling approach

Abstract

Chua's model of the nonlinear coil is often used in ferroresonance research. In this paper, two methods are used to determine such a model: usual Dommel's method and a grey box modelling approach, which is a novel way to model the nonlinear coil in a ferroresonant circuit. The parameters of the grey box model are determined by nonlinear regression using a metaheuristic optimization algorithm. The obtained models are evaluated by comparing the steady-state types obtained by measurements with those obtained using simulation. Comparing the obtained Chua's models, results indicate that the grey box modelling approach is more accurate than Dommel's method for lower coil nominal voltage. However, the main conclusion is that Chua's model in general cannot be used to predict all steady-state types typical for ferroresonance, i.e. it can be used in a highly restricted manner for appearance of certain steady-state types only.

Ferromagnetic core coil hysteresis modeling using fractional derivatives

“…The subject of accurate modeling of the more difficult cases of attempts to resemble magnetic hystereses is still an open topic [1,2]. Some of the most popular models are: the Jiles-Atherton model [3], the Preisach model [4] and the Chua model [5]. Over the years, there have been many propositions to modify these models in order to reflect the results of measurements made on iron-core coils more accurately [6,7].…”

Modeling of a Nonlinear Coupled Compliant Mechanism via Developed Kinematics-integrated Neural Network Algorithm

“…Compared with black-box NN, grey-box NN embedding prior knowledge has some advantages including faster convergence speed, better generalization performance and more stable learning process Liu et al (2016); Pai et al (2015); Milicevic et al (2016); Zhou (2013). In order to validate the performance of the designed grey-box NN, a series of tests are carried out including convergence rate test and prediction performance test.…”

“…Despite the accuracy of the NN model, it needs a sufficient and reasonable data set Ning et al (2011). However, a sufficient and reasonable data set is difficult to acquire in the field of engineering application Liu et al (2016); Pai et al (2015); Milicevic et al (2016).…”