A Current Predictive Control-Oriented Induction Motor Total Loss Hybrid Search Efficiency Optimization Strategy

Aiming at the problem of low operating efficiency of induction motors under light load, low speed and other conditions far from the rated operating point, this paper proposes a hybrid search optimization algorithm based on the total loss model of the motor and the improved fuzzy-golden section algorithm. The reference value of the excitation current dynamic optimization achieves the goal of reducing system losses and improving system efficiency. First, according to the total loss modeling of the induction motor controlled by current prediction, obtain the theoretical value of the excitation current under the minimum power loss; on this basis, the improved fuzzy search and the optimized fuzzy factor are used to improve the global search speed and accuracy of the search algorithm. To solve the problem of the oscillation of the system at the best point, the online golden section method is introduced to improve the local search speed of the system at the best point and reduce the excitation current pulsation and system loss. Simulation and experimental results show that compared with conventional algorithms, the hybrid search algorithm based on improved fuzzy and golden section can effectively reduce the output power of induction motors under the same torque conditions, and the motor operating efficiency is improved. The algorithm has fast convergence speed, dynamic and good steady-state performance and other advantages.