基于参数辨识的波浪发电场等效建模研究

提出了基于参数辨识的波浪发电场等效建模策略,将波浪发电场等效为单机模型。以阿基米德波浪摆(AWS)发电场内某一测点的实测波浪力作为输入,整个发电场稳态有功功率作为输出,采用粒子群算法(PSO)辨识单机等效模型中驱动系统的等效参数。在Matlab/Simulink中搭建了计及尾流和时滞的AWS波浪发电场详细模型,并利用多组实测波浪数据对等效建模策略进行了仿真验证。仿真结果表明,在不同实测数据下辨识得到的等效模型驱动参数相差不大,参数辨识结果平稳合理;对于某一组实测数据下辨识得到的等效驱动参数,在不同实测数据下获得的等效模型和详细模型功率曲线均基本一致。

Parameter identification based on equivalent modeling of AWS wave farm

Parameter identification based time domain equivalent modeling method of wave farm is proposed. The wave data measured in any measuring point of the Archimedes wave swing (AWS)-based wave farm and the total output power of the wave farm are used to identify the parameters of the equivalent mechanical model by Particle swarm optimization(PSO) . The detailed model of the wave farm considering wake and time-lag effects are built via MATLAB/Simulink. Simulations are performed using multiple sets of measured wave data to validate the effectiveness of the proposed method. The simulation results show that the equivalent parameters identified under different measured data are stable and reasonable. For the equivalent model identified by the first set of measured data, the output power of the equivalent model and the detailed model fit well under the other three sets of measured wave data.