摘要
在虚拟惯量控制技术的驱动下,风电高渗透电网包含不同形式惯量资源,系统等效惯量呈现复杂的非线性时变波动特征。为更好地诠释系统等效惯量的不确定性,提出了一种考虑风机虚拟惯量的系统等效惯量概率预测方法。首先利用数据驱动方法构建系统等效惯量的点预测模型,预判等效惯量的变化趋势;然后采用非参数核密度估计建立各时段预测误差概率密度函数,得到一定置信水平下待预测时刻系统等效惯量可能发生波动的区间范围。基于改进的IEEE RTS-79系统进行算例分析,结果表明所提方法与传统参数估计方法相比可靠性更高,能够为新型电力系统在低惯量场景下运行方式安排提供有益的辅助决策信息。
Driven by virtual inertia control technology,high wind power penetration grid contains different forms of inertia resources,and the system equivalent inertia presents the characteristic of complex nonlinear,time-varying and fluctuation. In order to better explain the uncertainty of system equivalent inertia,a probability prediction method of the system equivalent inertia considering the virtual inertia of wind turbine is proposed. Firstly,the point prediction model of system equivalent inertia is established through data-driven method,and the change trend of the equivalent inertia is predicted. Then,the non-parameter kernel density estimation is used to establish the probability density function of the prediction error for each time period,and the interval in which the system equivalent inertia may fluctuate under a certain confidence level at the prediction moment is obtained. An example is analyzed based on the modified IEEE RTS-79 system,and the results show that the proposed method is more reliable than the traditional parameter estimation method,it can provide useful decision-making information for new power system operation mode arrangement under low inertia scenarios.
作者
巴文岚
文云峰
叶希
文明
黄明增
张武其
BA Wenlan;WEN Yunfeng;YE Xi;WEN Ming;HUANG Mingzeng;ZHANG Wuqi(College of Electrical and Information Engineering,Hunan University,Changsha 410082,China;State Grid Sichuan Electric Power Company,Chengdu 610041,China;Economic&Technology Research Institute of State Grid Hunan Electric Power Co.,Ltd.,Changsha 410118,China)
出处
《电力自动化设备》
EI
CSCD
北大核心
2023年第3期124-130,165,共8页
Electric Power Automation Equipment
基金
国家自然科学基金资助项目(52077066)。
关键词
双馈感应风机
系统等效惯量
惯量评估
惯量预测
不确定性
doubly-fed induction generator
system equivalent inertia
inertia estimation
inertia forecasting
uncertainty
