Extreme learning machine-based estimation of total transfer capability of transmission corridors in wind-integrated power systems
DOI：10.7667/PSPC180610
Key Words:wind power  total transfer capability (TTC)  data mining  scenario clustering  RELIEF-F based feature selection  differential evolution extreme learning machine (DE-ELM)
Author NameAffiliation
XU Weiting Sichuan Electric Power Corporation Power Economic Research Institute, Chengdu 610041, China 
LIU Junyong College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China 
TANG Quan Sichuan Electric Power Corporation Power Economic Research Institute, Chengdu 610041, China 
QIU Gao College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China 
WANG Yunling Sichuan Electric Power Corporation Power Economic Research Institute, Chengdu 610041, China 
YANG Xinting Sichuan Electric Power Corporation Power Economic Research Institute, Chengdu 610041, China 
LI Ao Sichuan Electric Power Corporation Power Economic Research Institute, Chengdu 610041, China 
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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).
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