Prediction of Transformer Residual Flux Based on J-A Hysteresis Theory

Circuit breakers are effectively utilized for the controlled switching technique to mitigate inrush current when energizing an unloaded transformer. The core of the controlled switching technique is to obtain the appropriate closing angle based on the residual flux after opening. For the prediction of residual flux, the voltage integration method faces the difficult problem of determining the integration upper limit, while the Jiles- Atherton (J-A) model has the advantages of clear physical meaning of parameters, accurate calculation, and the ability to iteratively solve residual magnetism. It has low dependence on the initial conditions and greatly avoids the influence of DC offset and noise on measurement results. Firstly, an improved particle-swarm optimization algorithm is proposed in this paper to address the problem of slow convergence speed and susceptibility to local optima in current particle-swarm optimization algorithms for extracting J-A model parameters. The problem of slow convergence speed and susceptibility to local optima in traditional particle-swarm optimization algorithms is solved by optimizing the velocity and position-update formulas of particles in this algorithm. This new algorithm not only accelerates convergence speed, but also balances the overall and local search capabilities. Then, based on the J-A model, residual flux prediction of the transformer is carried out, and a transformer no-load energization experimental platform is built. A simulation model combining the J-A model and classical transformer is constructed using PSCAD/EMTDC to predict the residual flux of the transformer at different closing angles. Finally, by combining simulation with actual experimental waveform data, the accuracy of residual flux prediction was verified by comparing the peak values of the inrush current.