基于改进混沌布谷鸟算法的风电场多机等值参数辨识方法

Identifying multi-machine equivalent parameters of wind farms based on an improved chaotic cuckoo search algorithm

针对风电场的参数准确辨识问题,提出了一种基于改进混沌布谷鸟算法的风电场多机等值参数辨识方法。首先,阐述了风电场的系统结构与多机等值建模思路,设计了混沌布谷鸟算法的改进方案。然后,通过综合使用解析法与辨识法,建立了基于改进混沌布谷鸟算法的风电场多机等值参数依次辨识流程。最后,利用Matlab平台搭建了风电场多机等值仿真模型,比较了所提依次辨识方法与同时辨识方法在风电场参数辨识中的效果,分析了改进混沌布谷鸟算法与传统粒子群算法、布谷鸟算法的性能差异。结果表明:所提方法将风机参数辨识平均误差由11.07%降低至2.41%,提高了风电场动态特性拟合度,验证了其用于风电场多机等值参数辨识的有效性。

To solve the problem of accurate parameter identification of wind farms,an approach for identifying multi-machine equivalent parameters of wind farms based on an improved chaotic cuckoo search algorithm(CCSA)is proposed.First,the system structure of wind farms and its multi-machine equivalent modeling are described,and an improved scheme of the CCSA is designed.Then,by combining the analytical and identification methods,a sequential identification process of multi-machine equivalent parameters of wind farms based on the improved CCSA is established.Finally,a simulation model of the wind farms with multiple doubly-fed induction generators(DFIGs)is built using the Matlab platform,and the effects of the proposed sequential and simultaneous identification methods in identifying the wind farm parameters are compared.The performance differences among the improved CCSA,traditional particle swarm optimization(PSO),and cuckoo search algorithms(CSA)are analyzed.The simulation results show that the proposed identification method can reduce the DFIG’s parameter identification average error from 11.07%to 2.41%,and more precisely imitate the dynamic characteristics of the wind farms.Therefore,the effectiveness of the proposed approach is well validated.