摘要
随着高比例新能源并网、大容量直流密集接入和常规电源持续关停,电力系统原本相对均衡的惯量资源分布格局被打破,转动惯量在空间分布上发生了新的变化。为使系统运行人员及时感知惯量空间分布情况、精准定位惯量薄弱节点,提出一种基于多新息辨识的电力系统节点惯量估计方法。首先在分析系统惯量资源对节点惯量支撑作用的基础上,基于输出误差滑动平均模型构建惯量空间分布估计模型。进而采用多新息辨识方法求解模型中的待辨识参数,得出系统内所有节点的等效惯量,评判整个系统的惯量空间分布情况。最后在IEEE 39节点系统上进行仿真分析,验证了所提方法的有效性,以及对不同规模、不同位置故障的适应性。
With a high proportion of new energy sources connected to the grid,intensive access to largecapacity DC and continuous shutdown of conventional power sources,the originally relatively balanced inertia resource distribution pattern of the power system has been broken,and new changes in the spatial distribution of rotational inertia have occurred.In order to enable system operators to sense the spatial distribution of inertia in time and locate the weak nodes of inertia accurately,an estimation method of power system nodal inertia based on multi-innovation identification is proposed.Firstly,based on the analysis of the system inertia resources to nodal inertia support role,the inertia spatial distribution estimation model is built based on the output error moving average model.Then,the parameters to be identified in the model are solved using a multi-innovation identification method to derive the equivalent inertia of all nodes in the system and to evaluate the spatial distribution of inertia of the whole system.Finally,simulations are carried out on the IEEE 39-node system to verify the effectiveness of the proposed method and its adaptability to faults of different scales and locations.
作者
李元臣
文云峰
叶希
蒋小亮
林晓煌
LI Yuanchen;WEN Yunfeng;YE Xi;JIANG Xiaoliang;LIN Xiaohuang(College of Electrical and Information Engineering,Hunan University,Changsha 410082,China;State Grid Sichuan Electric Power Company,Chengdu 610041,China;State Grid Henan Electric Power Company Economic Research Institute,Zhengzhou 450000,China)
出处
《电力自动化设备》
EI
CSCD
北大核心
2022年第8期89-95,共7页
Electric Power Automation Equipment
基金
国家自然科学基金资助项目(52077066)
湖南省自然科学基金优秀青年项目(2020JJ3011)
湖湘青年科技创新人才项目(2020RC3015)。
关键词
电力系统
惯量空间分布
输出误差滑动平均模型
节点惯量
多新息辨识
频率稳定
electric power systems
spatial distribution of inertia
output error moving average model
nodal inertia
multi-innovation identification
frequency stability
