Abstract:The static parameter identification results of the JilesAtherton (JA) hysteresis model directly affect the predictive effect of the model on the transformer core hysteresis characteristics. Aiming at the problems of poor optimization ability and long calculation time existing in current single intelligent algorithm, a hybrid algorithm combining artificial fish school algorithm and linearly decreasing particle swarm optimization algorithm with optimized inertia weight is proposed. A transformer core magnetic performance test system was set up to conduct the experiment research on the hysteresis and loss characteristics of the transformer core under sinusoidal excitation. The identification speed and accuracy of JA model parameters for the proposed hybrid algorithm and other single intelligent algorithms were compared and analyzed. The results show that the root mean square error of the hybrid algorithm identification result is only 0006 9, which is lower than those of other single intelligent algorithms. The results prove that the proposed hybrid algorithm has the advantages of being not easy to fall into local optimal solutions, faster convergence speed and higher parameter identification accuracy compared with other single intelligent algorithms. In addition, considering the influence of the dynamic loss component on the hysteresis characteristics of the transformer core under alternating magnetic field, the existing dynamic loss coefficient solution method was modified, and a JA dynamic hysteresis model was established. Comparing the forecast result of the dynamic hysteresis loop model with experiment data, the correctness and effectiveness of the proposed method are verified.