Research on Remanence Detection of Transformer with Phase Selection of Vacuum Switch

In the current grid operation, using vacuum interrupters to carry out phase selection of transformer is an effective measure to reduce inrush current. Phase selection is limited by many factors in practical application, such as the pre-breakdown characteristic, the mechanical dispersion of vacuum interrupters, the detection of remanence, etc. For the detection of remanence, the Jiles-Atherton(J-A) model has the advantages of clear physical meaning of parameters and accurate calculation. However, the current optimization algorithms for extracting the parameters of J-A model such as particle swarm algorithm and genetic algorithm have the problems of slow convergence and easy to fall into local optimal solutions. This paper proposes a particle swarm optimization algorithm that improves the particle velocity and position update formula, and uses the inertia weight based on the nonlinear change of logistic chaos. This algorithm has the advantages of not easy to fall into the local optimal solution, fast convergence speed and high accuracy of parameter identification. Secondly, using PSCAD/EMTDC to build a remanence detection model combining J-A model and transformer which predicts remanence in transformer at different closing angles. Finally, the combination of simulation and experiment verifies the reasonableness of the model by comparing the peak value of inrush current, which is a guide to reduce the inrush current generated when the transformer is closing.