引用本文: | 徐鑫裕,边晓燕,张 骞,等.基于数据驱动的双馈风电场经VSC-HVDC并网
次同步振荡影响因素分析[J].电力系统保护与控制,2021,49(21):80-87.[点击复制] |
XU Xinyu,BIAN Xiaoyan,ZHANG Qian,et al.Analysis of influencing factors of subsynchronous oscillation caused by a DFIG-based wind farm via the VSC-HVDC grid-connected system based on a data driven method[J].Power System Protection and Control,2021,49(21):80-87[点击复制] |
|
摘要: |
双馈风电场经柔性直流(VSC-HVDC)并网的振荡数据中蕴含大量信息,这些信息能够反映不确定因素对系统次同步振荡的影响。提出了基于数据驱动的方法分析风速和风电场出口电流波动的不确定因素组合对次同步振荡的影响。首先,对系统振荡数据按风速进行分段,运用基于Nuttall窗插值的FFT识别功率数据中与小信号分析结果相对应的次同步振荡分量并提取幅值。然后,利用高斯混合模型(GMM)聚类算法对因素组合进行聚类,通过三种内部有效性指标评价聚类效果。最后,从次同步振荡分量幅值变化的角度分析了风速/电流波动聚簇对次同步振荡的影响。结果表明:在振荡影响因素组合聚类方面,所提GMM方法相比于K-Means具有更好的聚类效果。当风速/电流波动因素组合属于部分聚簇时,会恶化系统次同步振荡。 |
关键词: 双馈风电场 柔性直流输电 次同步振荡 高斯混合模型 Nuttall窗 |
DOI:DOI: 10.19783/j.cnki.pspc.210101 |
投稿时间:2021-01-24修订日期:2021-01-24 |
基金项目:国家自然科学基金项目资助(51977128);上海市教育发展基金会和上海市教育委员会“曙光计划”(18SG50) |
|
Analysis of influencing factors of subsynchronous oscillation caused by a DFIG-based wind farm via the VSC-HVDC grid-connected system based on a data driven method |
XU Xinyu,BIAN Xiaoyan,ZHANG Qian,CHANG Xiqiang,HUANG Ruanming,SUN Kaining |
(1. College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China;
2. Xinjiang Electric Power Company, Urumqi 830063, China; 3. State Grid Shanghai Electric Power
Economic and Technology Research Institute, Shanghai 200233, China) |
Abstract: |
The oscillation data of a DFIG-based wind farm via the VSC-HVDC grid-connected system contains a lot of information. These can illustrate the comprehensive effect of uncertain factors on Sub-Synchronous Oscillation (SSO). This paper proposes a data-driven method to analyze the influence of the combination of uncertain factors of wind speed and current fluctuation at the Point of Common Coupling (PCC) of the wind farm on SSO. First, the system oscillation data is processed in segments according to wind speed. FFT based on Nuttall window interpolation is applied to detect the SSO component in the power data corresponding to the results of small-signal analysis, and the amplitude is extracted. Then, a Gaussian Mixture Model (GMM) clustering algorithm is adopted to cluster the factor combination, and the clustering quality is evaluated by three internal validity indicators. Finally, the influence of wind speed/current fluctuation clustering on SSO is analyzed from the perspective of the amplitude change of the SSO component. The results show that in the aspect of factor combination clustering, the proposed GMM clustering method has a better clustering effect than K-Means. When the combination of wind speed/current fluctuation factors belongs to partial clustering, the SSO of the system will worsen.
This work is supported by the National Natural Science Foundation of China (No. 51977128). |
Key words: DFIG-based wind farm VSC-HVDC sub-synchronous oscillation Gaussian mixture model Nuttall window |