摘要
精确估计惯量是客观评估电网频率安全稳定性的关键环节,现有方法主要基于有功–频率大扰动的离散事件,无法对电网惯量连续估计。鉴于此,该文研究利用有功–频率类噪声扰动信息连续估计电网惯量的方法。首先以区域为对象,将断面联络功率和区域内负荷扰动作为输入、频率扰动作为输出,应用输出误差(output error,OE)模型对区域惯量进行估计,进而对各区域合并求解全网等效惯量。提出数据分段、移动数据窗的处理方式,对多段类噪声输入的OE模型分范围选阶辨识,并剔除离群值,获取充足的惯量估计样本及其均值,减小随机扰动引起的辨识误差。最后通过算例系统验证所提方法和模型的有效性及精确性。
Accurate inertia estimation is the key to objectively evaluate the safety and stability of power grid frequency. Existing methods are mainly based on discrete events with large active power-frequency disturbances, which cannot continuously estimate the power grid inertia. This paper studied a method for continuously estimating the power grid inertia using active power-frequency ambient noise information. Taking the area as the object, the cross-section contact power and the load disturbance in the area were taken as the input, and the frequency was taken as the output. The output error(OE) model was used to estimate the area inertia, and area inertia values were merged to find the inertia of the entire grid. A processing method of data segmentation and moving data window was adopted, and the OE model order selected by different ranges. The outliers were eliminated to obtain sufficient inertia estimation samples and average value to reduce identification errors caused by random disturbances. Finally, the validity and accuracy of the proposed method and model were verified by test system.
作者
李世春
夏智雄
程绪长
舒征宇
钟浩
涂杰
黄森焰
LI Shichun;XIA Zhixiong;CHENG Xuchang;SHU Zhengyu;ZHONG Hao;TU Jie;HUANG Senyan(Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station(College of Electrical Engineering and New Energy,China Three Gorges University),Yichang 443002,Hubei Province,China;Shandong Career Development College,Jining 272067,Shandong Province,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2020年第14期4430-4439,共10页
Proceedings of the CSEE
基金
国家自然科学基金项目(51907104)
湖北省自然科学基金项目(2019CFB207)。
关键词
电网惯量连续估计
系统辨识
类噪声
输出误差模型
数据分段
continuous estimation of grid inertia
system identification
ambient noise
output error(OE)model
data segmentation
