风力发电
电力系统
计算机科学
电压
电力系统仿真
风电预测
控制理论(社会学)
数学优化
边距(机器学习)
功率(物理)
工程类
数学
电气工程
物理
量子力学
机器学习
人工智能
控制(管理)
作者
Lin Xue,Tao Niu,Zhengshuo Li,Fan Li
标识
DOI:10.1109/tste.2022.3164923
摘要
A high proportion of wind power systems with correct as set weak network connections may suffer from cascading trip faults due to the lack of conventional power plants and voltage/var support, which brings about great challenges to secure wind farm operations. To accurately and efficiently evaluate the dynamic var margin for an online field application within large-scale integrated wind areas, the transient dynamic voltage processes of hundreds or thousands of wind turbines must all be considered due to the dramatic interior voltage fluctuation effect of wind units, which brings a heavy computational burden for solving the TSCOPF problem. Therefore, to address these large-scale challenges, this paper proposes an objective-oriented approximation dimensionality reduction equivalent (OOA-DRE) approach, which accurately transforms the high-dimensional differential system to a low-dimensional system with few differential equations and low-dimensional algebraic equations for the wind unit with the maximum electrical distance value. Meanwhile, this paper uses the Levenberg-Marquardt method to obtain the global pre-evaluation error, so that the evaluation error reaches the minimum value. Finally, an actual wind power system verifies that the calculation time is reduced by approximately 2/3 compared with the original model on the premise of ensuring accuracy for large-scale systems.
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