基于特征分析的大规模风电场等值建模  

Feature Analysis Based Equivalent Modeling for Large-Scale Wind Farms

作　　者：黄伟[1] 张小珍[1] 

机构地区：[1]华北电力大学电气与电子工程学院,北京市昌平区102206

出　　处：《电网技术》2013年第8期2271-2277,共7页Power System Technology

摘　　要：传统的基于加权平均的风电场单机动态等值模型的精度将难以满足包含大规模风电的电力系统安全稳定性分析的需要。如果能依据一定的指标对风电机组进行分群,建立风场多机等值模型,将在很大程度上提高风场模型的精确度。通过特征分析的方法,确定了合理的风电机组分群指标,从物理意义上阐释了指标的合理性。基于该分群指标搭建了风场多机等值模型,该模型的有功、无功出力曲线在风速发生波动或系统侧发生三相短路故障时与详细风场模型几乎一致,具有较高的精度。仿真结果验证了该分群指标的合理性。但是由于该分群指标是基于对线性化状态方程的相关分析得到的,因此大扰动情况该分群指标的合理性还需进一步深入研究。The accuracy of traditional weighted average based dynamic equivalent model for single wind power generator is hard to meet the demand of security and stability analysis of power grid containing large-scale wind farms,and the accuracy of wind farm model can be improved to a great extent if wind power generators were clustered according to a certain index and then a multi machine equivalent model of wind farm was built.Through feature analysis the reasonable clustering index for wind power generators is determined and the reasonableness of the index is expounded by physical meaning.Based on this clustering index an accurate multi machine equivalent model for wind farm is built,and the active and reactive output curves of the built model are almost the same as those of the detailed wind farm model under fluctuation of wind speed or under three-phase short-circuit fault occurred at grid side.The reasonableness of the clustering index is verified by simulation results,however such a clustering index is attained based on correlation analysis of linearized state equation,so the reasonableness of the clustering index under large disturbance should be further researched.

关 键 词：状态方程 主导特征根 相关因子 主导状态变量 分群指标 聚类分析 

分 类 号：TM71[电气工程—电力系统及自动化]