面向双馈风机的分层型免疫协同进化粒子群算法多参数辨识

双馈异步电机在实际工程运行情况下是一种高度非线性动态系统,并且由于其参数随环境的强变化性,常规方法难以实时得到其高精度参数。结合粒子群算法的高效多模态收敛性能以及免疫机理全局优化能力强等特点,研究了分层型免疫协同进化粒子群算法(co-evolutionary particle swarm optimization algorithm based on hierarchical-particle immune, HICPSO)智能计算模型参数。将该方法应用于双馈电机参数辨识与建模,提出了分层型免疫协同进化粒子群算法的双馈电机在线参数辨识。仿真结果表明该算法只需要采集控制过程中的数据,不需要数据手册的电机设计值等其他参数,且能在双馈电机运行实时跟随电机参数变化。

Multi-parameter Identification of CO-Evolutionary Particle Swarm Optimization Algorithm Based on Hierarchical-particleImmune for DFIG

The doubly-fed induction motor is a highly nonlinear dynamic system under actual engineering operation conditions, due to the strong variability of its parameters with the environment, it is difficult for conventional methods to obtain high-precision parameters in real time. Combining the high-efficiency multi-modal convergence performance of the particle swarm optimization algorithm(PSO) and the immune mechanism have the ability of strong global optimization search, the parameters of the Intelligent Calculation Model of Co-evolutionary Particle Swarm Optimization Algorithm Based on Hierarchical-particle Immune (HICPSO) are studied. The method is applied to parameter identification and modeling of doubly-fed machines, and the online parameter identification of doubly-fed machines based on HICPSO is proposed. The simulation results show that the algorithm only needs to collect the data in the control process, does not need other parameters such as the motor design value of the data sheet, and can follow the motor parameter change in real time in the doubly-fed motor operation.