| 引用本文: | 胥威汀,刘俊勇,唐权,等.含风电系统断面TTC运行规则的极限学习机提取方法[J].电力系统保护与控制,2018,46(23):135-142.[点击复制] |
| XU Weiting,LIU Junyong,TANG Quan,et al.Extreme learning machine-based estimation of total transfer capability of transmission corridors in wind-integrated power systems[J].Power System Protection and Control,2018,46(23):135-142[点击复制] |
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| 摘要: |
| 风电集中接入使得传统方式有效计算极限传输容量存在困难。为此，提出一种基于差分进化极限学习机的含风电系统输电断面极限传输功率(Total Transfer Capability, TTC)运行规则提取方法。首先基于K-medoids聚类方法提取以“风功率-负荷”二维特征表征的典型运行场景，然后通过随机采样和重复潮流方法生成用于TTC运行规则挖掘的知识库。接着采用RELIEF-F算法筛除冗余特征并辨识与输电断面TTC存在强关联的特征属性，以削减运行特征的高维度。最终通过将训练数据输入差分进化极限学习机，从知识库中提取TTC运行规则。算例验证表明，所提方法能够以较高的计算精度及较强的泛化能力实现TTC的快速估计。 |
| 关键词: 风电 极限传输功率 数据挖掘 场景聚类 RELIEF-F特征筛选 差分进化极限学习机 |
| DOI：10.7667/PSPC180610 |
| 投稿时间：2018-05-22修订日期：2018-09-30 |
| 基金项目:国家自然科学基金项目资助(51437003);国网四川省电力公司科技项目资助(SGSCJY00JHJS201700009) |
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| Extreme learning machine-based estimation of total transfer capability of transmission corridors in wind-integrated power systems |
| XU Weiting,LIU Junyong,TANG Quan,QIU Gao,WANG Yunling,YANG Xinting,LI Ao |
| (Sichuan Electric Power Corporation Power Economic Research Institute, Chengdu 610041, China;College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China) |
| Abstract: |
| Central integration of wind farmmakes it hard to effectively compute Total Transfer Capability (TTC) through traditional way. For this reason,a data mining technique named Differential Evolution Extreme Learning Machine (DE-ELM) is proposed to extract operating rules for the TTC of tie-lines in wind-integrated power systems. Representative operating scenarios are firstly determined by K-medoids clustering under the two-dimensional “wind power-load consumption” feature space. Then knowledge base for TTC operation rule mining is generated by stochastic sampling and repeated power flow. Secondly, to reduce the ultra-high dimensionality of operating features, RELIEF-F algorithm is employed to screen the redundant features and identify the features that are strongly correlated to the TTC. Finally, the TTC operation rules are extracted from the knowledge base by feeding training data into the DE-ELM. Numerical results show that the proposed method can fast estimate TTC with satisfying accuracy and strong generalization. This work is supported by National Natural Science Foundation of China (No. 51437003) and Science and Technology Project of State Grid Sichuan Electric Power Company (No. SGSCJY00JHJS201700009). |
| Key words: wind power total transfer capability (TTC) data mining scenario clustering RELIEF-F based feature selection differential evolution extreme learning machine (DE-ELM) |
