Ship Propulsion Motor Parameter Dynamic Identification Based On Adaptive PSO Algorithm

With the rapid development of power electronic technology and AC motor control technology, vector control which is used widely in propulsion motor control system of electric propulsion ship, has become a modern mainstream motor control theory. An excellent performance of propulsion motor control system is the premise of ships’ safe navigation, and it mainly depends on the accuracy of motor parameters. However, the motor parameters may change all the time in actual operation process, it is necessary to identify parameters of ship propulsion motor online.This article does a deep research of ship asynchronous propulsion motor dynamic model, and derives both flux oriented dynamic and discrete model. Then, the reasons for both early maturity problem and easily trapping in local premium problem which exist in standard particle swarm optimization (SPSO) algorithm are analyzed deeply. Aiming at solving the two problems, this thesis proposes an adaptive particle swarm optimization (APSO) algorithm by introducing the evolution speed factor and concentration factor, and the APSO algorithm possesses both rapidity and precise convergence characteristics. A contrast test between APSO algorithm and now available ones is made on the benchmark functions. Besides, according to the particularity of ship asynchronous propulsion motor, an objective function of PSO algorithm is designed. Meanwhile, the proposed APSO algorithm is applied to ship asynchronous propulsion motor parameter identification, and the implementation process is analyzed in detail. Finally, the simulation platform of ship asynchronous propulsion motor parameter identification is realized in Simulink, and simulates motor parameter identification under three classic load change.The simulation results indicate that both rapidity and convergence precision of proposed APSO are better than these of now available ones. This method solves the early maturity problem and easily trapping in local premium problem of SPSO, and it makes electrical parameters online identification with high precision realized. Thereinto, the electrical parameters include rotor resistance and inductance, stator resistance and inductance, mutual inductance.