基于PSO算法的定速风电机组三质块传动系统模型参数辨识

为获得传动系统模型的准确参数,提出阵风激励下三质块传动系统模型的参数辨识方法。根据定速风电机组机械动态与电气动态解耦的特性,提出在辨识传动系统模型参数时可忽略电气动态,据此获得定速风电机组的简化模型。采用轨迹灵敏度方法,分析了传动系统各参数的可辨识性及辨识的难易程度。基于粒子群优化算法(PSO)对传动系统模型进行了参数辨识。辨识结果与轨迹灵敏度分析结论一致,验证了提出的参数辨识方法的可行性。

Parameter identification of three-mass drive-train system for fixed-speed wind turbine generator based on PSO algorithm

In order to obtain accurate parameters for a drive-train model, a method for parameter identification of a three-mass drive-train system with gusty wind excitation is proposed. According to the decoupling of the mechanical dynamics and electrical dynamics of fixed-speed wind turbine generators, the electrical dynamics can be neglected when identifying parameters of a drive-train model. Based on this, a simplified model for fixed-speed wind turbine generators was obtained. The identifiability of the parameters of the drive-train system and the difficulties in parameter identification were analyzed with the trajectory sensitivity analysis method. Finally, the parameters of the drive-train model were identified based on the particle swarm optimization(PSO)algorithm. The identified results are consistent with the trajectory sensitivity analysis results, verifying the feasibility of the proposed parameter identification method.