A Data-driven Method for Oscillation Stability Assessment of Grid-connected Wind Farm Based on Dynamic Parameters Equivalent

被引：0
|
作者：
Chen J. [1 ]
Du W. [2 ]
Wang H. [2 ]
机构：
[1] State Key Laboratory of Alternate Power Systems with New Energy Resources, North China Electric Power University), Changping District, Beijing
[2] School of Electric Engineering, Sichuan University, Sichuan Province, Chengdu
来源：
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2022年 / 42卷 / 19期
基金：
中国国家自然科学基金;
关键词：
data driven; equivalent system; open-loop mode resonance; oscillation stability;
D O I：
10.13334/j.0258-8013.pcsee.211514
中图分类号：
学科分类号：
摘要：
Open-loop modal resonance theory provides a clear mechanism analysis for the oscillation stability caused by wind turbine generators (WTGs) connected to power system. However, how to use measured data to evaluate the oscillation stability in a real power system with large-scale wind farm connected still remains an open issue. For the oscillation stability in a multi-wind-farm connected power system, this paper proposed an equivalent-system-based and data-driven method for oscillation stability assessment (OSA). This method could achieve dynamic equivalence of large-scale wind farm systems in the case of some uncertain parameters of the full system, use the joint distributed adaptation algorithm to establish a data-driven oscillation stability evaluation model offline in the equivalent system, and generalize it to online evaluation in the real system. Through a typical simulation system example and a real large-scale wind farm example, and combined with various comparative tests, the efficacy and adaptability of the proposed approach for OSA were demonstrated, proving the excellent evaluation performance of the established data-driven model. The method proposed in this paper can transfer the burden of online calculation of the evaluation model to offline training through deep learning, which is of great significance for realizing the measurement-based OSA of large wind farms. ©2022 Chin.Soc.for Elec.Eng.
引用
收藏
页码：6958 / 6972
页数：14
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