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基于EKS的风电并网系统频率特征参数辨识算法

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新型电力系统建设背景下系统对接入风电的频率调节能力提出了要求。但风电在可调容量耗尽时将退出调频,导致系统等效惯量等参数和频率响应特性变化,影响系统切机切负荷等频率控制措施的正确动作。为此,提出一种基于扩展卡尔曼平滑算法(Extented Kalman Smoother,EKS)的风电并网系统频率特征参数辨识方法,可实现不同工况下系统频率特征参数的高效准确辨识。首先分析了风电场频率控制方法,建立了基于共模分量的风电并网系统频率响应模型,然后分析了EKS算法,进而提出了基于EKS的系统频率特征参数辨识方法,最后通过仿真算例进行了验证。仿真结果表明,所提方法可有效辨识不同工况下系统的惯量、阻尼等频率特征参数。
Frequency Characteristic Parameter Identification Algorithm for Wind Power Grid Connected System Based on EKS
Under the background of new power system construction,the system has put forward requirements for the frequency regulation ability of wind power integration.But when the adjustable capacity of wind power is depleted,it will exit frequency regulation,causing changes in parameters such as equivalent inertia and frequency re-sponse characteristics of the system,which affects the correct action of frequency control measures such as turbine and load cutting.Therefore,this paper proposes an identification method of frequency characteristic parameters of wind power grid-connected system based on Extented Kalman Smoother,EKS),which can realize efficient and accurate i-dentification of frequency characteristic parameters of the system under different working conditions.Firstly,the fre-quency control method of wind farm is analyzed,and the frequency response model of wind power grid-connected sys-tem based on common-mode component is established.Then,EKS algorithm is analyzed,and then the identification method of system frequency characteristic parameters based on EKS is proposed.Finally,it is verified by a simulation example.The simulation results show that the proposed method can effectively identify the inertia,damping and other frequency characteristic parameters of the system under different working conditions.

Grid-connected wind power systemFrequency responseExtended Kalman smootherParameter iden-tification

袁康波、郑迪、汪伟、钱丽娟

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中国计量大学机电工程学院,浙江 杭州 310018

中国计量大学浙江省智能制造质量大数据溯源与应用重点实验室,浙江 杭州 310018

风电并网系统 频率响应 扩展卡尔曼平滑器 参数辨识

国家自然科学基金项目

52107132

2024

计算机仿真
中国航天科工集团公司第十七研究所

计算机仿真

CSTPCD
影响因子:0.518
ISSN:1006-9348
年,卷(期):2024.41(6)