Parameters Identification Of Ship Permanent Magnet Motor Based On Self-Adaptive Hybrid Mutation Differential Evolution

As the core actuator of electric propulsion system,propulsion motor has been developed rapidly with the wide application of electric propulsion ship.Featuring high power density,small size and superior control performance,Permanent Magnet Synchronous Motor(PMSM)becomes very popular in the shipping market.In order to achieve high performance control for the motor,accurate motor parameters are needed.However,the motor parameters are time-varying in the actual operation process,which affects the control effect of the motor.Therefore,this paper focuses on ship PMSM to study the theory and application of motor parameter identification.Firstly,the mathematical model of PMSM in two-phase rotating coordinate system is taken as the parameter identification model,and the parameter identification system of PMSM based on vector control is built.Aiming to achieve the numerical stability of the state equation of the motor,the trapezoid method is introduced.We use the example ship data to build a ship motion control system,and perform a numerical simulation of the motor under vector control.Secondly,this paper introduces differential evolution algorithm to study parameter identification.Aiming at the problem that differential evolution algorithm is easy to fall into local minima,theoretical analysis and simulation were performed to verify the influencing factors of the performance of the differential evolution algorithm,and points out the direction for subsequent algorithm optimization.Based on the problem that basic differential evolution algorithm is easy to lead to low parameter identification accuracy,an self-adaptive hybrid mutation differential evolution algorithm is proposed to enhance the global optimization and local search ability of the algorithm.Experimental results verified that the algorithm is highly competitive in solving single-objective optimization problems.Finally,for the problem of the actual value of direct axis current will affect the identification accuracy of motor parameters.The output signals of the motor mathematical model is obtained by Newton's method.The problem of motor parameter identification is transformed into an unconstrained optimization problem based on the penalty function,and the self-adaptive hybrid mutation differential evolution algorithm is introduced to realize the motor parameter identification.In order to verified the correctness of the theory and the proposed method,this paper carried out large quantites simulations verification on the Matlab platform for different motor operating conditions.Experimental results show that the proposed algorithm can identify motor parameters well under both constant torque and sudden torque conditions.