| 引用本文: | 林俐,费宏运,刘汝琛,潘险险.基于分层聚类算法的地区风电出力典型场景选取方法[J].电力系统保护与控制,2018,46(7):1-6.[点击复制] |
| LIN Li,FEI Hongyun,LIU Ruchen,PAN Xianxian.A regional wind power typical scenarios’ selection method based on hierarchical clustering algorithm[J].Power System Protection and Control,2018,46(7):1-6[点击复制] |
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| 摘要: |
| 为反映风电场出力变化特征，提出了一种基于分层聚类算法的地区风电出力典型场景选取方法。首先采用分层聚类算法对风电出力样本进行聚类分析，得到反映样本亲疏关系的聚类树状图。随后考虑风电出力典型场景的选取质量，采用类间样本离差平方和来描述类间样本的差异性，以此作为聚类数的判定依据，从而实现样本的有效划分。最后，以某地区实际风电出力数据为例，验证了所提方法的合理性，并面向调峰、无功配置等需求选取了风电出力典型日场景。 |
| 关键词: 分层聚类算法 典型场景 聚类树状图 风电出力样本 聚类数 |
| DOI：10.7667/PSPC170454 |
| 投稿时间：2017-03-29修订日期：2017-07-07 |
| 基金项目:国家自然科学基金重大项目资助(51190103) |
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| A regional wind power typical scenarios’ selection method based on hierarchical clustering algorithm |
| LIN Li,FEI Hongyun,LIU Ruchen,PAN Xianxian |
| (State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;Shanxi Electric Power Engineering Co., Ltd, China Energy Engineering Group, Taiyuan 030000, China;Guangdong Power Grid Development Research Institute, Guangzhou 510080, China) |
| Abstract: |
| In order to reflect the features of wind farm power variation, this paper puts forward a method for regional wind power typical scenarios’ selection based on hierarchical clustering algorithm. Firstly, it uses the hierarchical clustering algorithm to cluster the wind power output samples and gains a clustering tree to reflect the similarity relation between samples. Then, in order to improve the quality of wind power typical scenarios’ selection, the sum of squares of deviations is used to describe the difference between interclass samples, which is regarded as a basis to determine the number of clusters, and it realizes the samples’ effective division. Finally, by using the real wind power output data in a certain region, it verifies the method’s rationality, and selects regional wind power typical scenarios meeting the requirements of peak regulation and reactive power configuration. This work is supported by National Natural Science Foundation of China (No. 51190103). |
| Key words: hierarchical clustering algorithm typical scenarios clustering tree wind power output samples number of clusters |
