Automatic J–A Model Parameter Tuning Algorithm for High Accuracy Inrush Current Simulation

Xishan Wen, Jingzhuo Zhang, Hailiang Lu
2017 Energies  
Inrush current simulation plays an important role in many tasks of the power system, such as power transformer protection. However, the accuracy of the inrush current simulation can hardly be ensured. In this paper, a Jiles-Atherton (J-A) theory based model is proposed to simulate the inrush current of power transformers. The characteristics of the inrush current curve are analyzed and results show that the entire inrush current curve can be well featured by the crest value of the first two
more » ... f the first two cycles. With comprehensive consideration of both of the features of the inrush current curve and the J-A parameters, an automatic J-A parameter estimation algorithm is proposed. The proposed algorithm can obtain more reasonable J-A parameters, which improve the accuracy of simulation. Experimental results have verified the efficiency of the proposed algorithm. Energies 2017, 10, 480 2 of 15 in saturation and the estimation of residual fluxes. A hysteretic core model was implemented to auto initialize residual flux. In [21] , the use of a modified J-A model with nonconstant parameters was suggested to improve simulation accuracy. An additional parameter, the peak magnetic field intensity, is added to the J-A parameters to model the power transformer. The inrush current simulated by the new model can match the measured one more precisely. In [22] , the sympathetic inrush phenomenon that might occur among several parallel transformers was studied in detail. In addition, a dynamic-simulation-based reconstruction method is proposed for reproducing the phenomenon. Although various transformer models have been proposed to deal with different problems, there is still room to further improve the performance and adaptivity. Previous methods may have limitations related to the proper representation of the hysteretic behavior of the core, and some others may be too complicated to be implemented and used for practical applicaiton. A precise transformer model depends on the accurate description of its magnetic characteristics. The characteristics exhibit non-linear, hysteretic, and dynamic behavior due to eddy and anomalous losses in electrically conducting magnetic materials. In this paper, to model the power transformer, the Jiles-Atherton (J-A) model [23, 24] was used for its accuracy and ease in numerical implementation. The original J-A model is based on the energy-balance equation, which describes hysteresis loops of soft magnetic materials by simulating the magnetization process by using domain wall motion with pinning effects. By using only five parameters, the J-A model can give a detailed and reasonable expression for the hysteresis characteristics of the soft magnetic materials. The J-A model is very sensitive to variations in the parameters, thus determining them with reasonable accuracy is a major challenge. Many advanced numerical methods are proposed to determine the correct J-A parameters. For example, in [25] , the parameters of the J-A model were identified by using the stochastic optimization method simulated annealing. In [26] , the inverse J-A model was used to represent the hysteresis phenomenon in the magnetic core. The parameters of this model are optimally determined using inrush current measurements by the shuffled frog-leaping algorithm. Different J-A parameters represent different hysteresis characteristics, and thus lead to different inrush currents. The accuracy of inrush current simulation can be improved by using the more accurate J-A parameters. The direct correlation between J-A parameters and inrush current hasn't been built up systematically in previous work. In addition, an efficient automatic algorithm for determining the J-A parameters for inrush current simulation needs to be proposed. In this paper, based on the proposed transformer model, the impact of the five J-A parameters are explored in detail. Based on the observation, the key feature of the inrush current signals is extracted. Using the feature as the objective function, an efficient automatic J-A parameter optimization algorithm is proposed. The proposed algorithm is applied to two different types of transformers to verify its efficiency. Experimental results show that the simulated inrush current can match the measure inrush current well, which demonstrates the accuracy and robustness of the proposed algorithm. The major contribution of this paper lies in four aspects as shown below: (a) A J-A theory based transformer model is proposed to simulate the inrush current of the power transformer. (b) The impact of each J-A parameter on the inrush current is analysed. It is found that a more accurate inrush current simulation can be achieved by tuning the J-A parameters. (c) The most representative feature of the inrush current signals is extracted as the objective function for J-A parameter tuning. (d) An automatic J-A parameter tuning algorithm is proposed and verified. The rest of the paper is organized as follows. Section 2 presents an accurate power transformer model based on J-A theory. The impact of J-A parameters on the inrush current is analyzed in Section 3.
doi:10.3390/en10040480 fatcat:eb2kk36ayvfg7glbqxncbukjuq