| 引用本文: | 颜湘武,李君岩.基于主成分分析法的直驱式风电场分群方法[J].电力系统保护与控制,2020,48(5):127-133.[点击复制] |
| YAN Xiangwu,LI Junyan.Grouping method of direct drive wind farm based on principal component analysis[J].Power System Protection and Control,2020,48(5):127-133[点击复制] |
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| 摘要: |
| 为了建立较为精确的风电场等值模型，提出了一种基于主成分分析法的直驱式风电场分群方法。首先对直驱式风电机组进行建模和分析，得到了表征风电机组运行状态的全部状态变量。然后利用主成分分析法提取了3个主导变量，它们代表了全部状态变量90%以上的信息，可较准确地反映机组的运行点。最后为了进行对比分析，在算例中分别以3个主导变量和风速为分群指标进行分群计算，并在Matlab/Simulink平台上搭建了风电场的详细模型、以主导变量分群的等值模型及以风速分群的等值模型。通过比较三种模型在风速波动及电网故障情况下动态特性的仿真结果，验证了所提分群方法的正确性及较高的精确性。以该方法分群建立的风电场等值模型的精确性较高。 |
| 关键词: 主成分分析法 直驱式风电场 分群指标 状态变量 主导变量 |
| DOI：10.19783/j.cnki.pspc.190575 |
| 投稿时间：2019-05-21修订日期：2019-06-12 |
| 基金项目:河北省自然科学基金项目资助(E20185021134); |
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| Grouping method of direct drive wind farm based on principal component analysis |
| YAN Xiangwu,LI Junyan |
| (Key Laboratory of Distributed Energy Storage and Microgrid of Hebei Province North China Electric Power University, Baoding 071003, China) |
| Abstract: |
| In order to establish a more accurate wind farm equivalent model, a direct-drive wind farm clustering method based on principal component analysis is proposed. Firstly, all the state variables that characterize the operating state of the wind turbine are obtained by modeling and analyzing the direct-drive wind turbine. Then, three dominant variables are extracted by principal component analysis, which represent more than 90% of all state variables. It can accurately reflect the operating point of the wind turbine. Finally, for comparative analysis, the hierarchical clustering algorithm is used for clustering calculation with three dominant variables and wind speed as the grouping index. The detailed model of wind farm, the equivalent model of dominant variable grouping and the equivalent model of wind speed grouping are built on Matlab/Simulink platform. By comparing the simulation results of the dynamic characteristics of the three models under wind speed fluctuation and grid fault condition, the correctness and high accuracy of the proposed clustering method are verified. The wind farm equivalent model established by this method has higher accuracy. This work is supported by Natural Science Foundation of Hebei Province (No. E20185021134) and Science and Technology Project of State Grid Headquarters:Research on Inertia, Damping and Primary Adjustment Method of Doubly-fed Induction Wind Turbine (No. SGTYHT/18-JS-206). |
| Key words: principal component analysis direct-drive wind farm grouping indicator state variables dominant variable |
