基于IGWO-K-means的风电场动态等值建模

风电场等值建模是分析风电系统的前提和基础,为了提高风电场动态等值建模精度,降低等值难度,本文基于风(风速和风向)、风机本体、风电输出效果和风机工作环境等4个方面,从内蒙古某风电场24台机组实际采样的运行数据中选取了14个变量作为分群指标,全面描述了风电场特性。其次提出了收敛因子非线性策略和动态参考率策略两个控制策略,改进了灰狼优化算法(GWO),并结合K-means聚类算法寻找最佳聚类中心,输出聚类结果,建立风电场动态等值模型。最后在MATLAB/Simulink平台上建立风电场聚类模型,验证该模型的可行性。结果表明,该方法提高了风电场等值建模的精度,能够更好地描述风电场的动态特性。

Dynamic Equivalent Modeling of Wind Farm Based on IGWO-K-means Method

The equivalent modeling of wind farm is the premise and basis for the analysis of wind power system. In order to improve the precision of dynamic equivalence of wind farm and reduce the difficulty of equivalence, 14 variables from the running data of the 24 wind turbines in an Inner Mongolia wind farm are selected as clustering index based on such four aspects as wind(wind speed and wind direction), induction machine, wind power output effect and working environment, which comprehensively describes the characteristics of the wind farm. Secondly, two control strategies, nonlinear strategy for convergent factor and dynamic reference rate strategy, are introduced to improve Grey Wolf Optimizer algorithm (GWO). In addition, the best cluster centers are searched by combining with K-means clustering algorithm, the clustering results are outputted, and a dynamic equivalent model of wind farm is built. Finally, a clustering model is established on MATLAB/Simulink platform to verify the feasibility of the model. The results show that the method can improve the accuracy of wind farm equivalent modeling and describe the dynamic characteristics of wind farms more accurately.