Power system distributed key section detection online based on K nearest neighbor algorithm
DOI：10.7667/PSPC161482
Key Words:key section  K nearest neighbor algorithm (KNN)  distributed  machine learning  data mining
Author NameAffiliationE-mail
WANG Bin Guangdong Power Grid Power Dispatching Control Center, Guangzhou 510600, China  
GUO Wenxin Guangdong Power Grid Power Dispatching Control Center, Guangzhou 510600, China guowenxin1985@126.com 
LIU Wentao Guangdong Power Grid Power Dispatching Control Center, Guangzhou 510600, China  
LU Jiangang Guangdong Power Grid Power Dispatching Control Center, Guangzhou 510600, China  
XIANG Dejun Guangdong Power Grid Power Dispatching Control Center, Guangzhou 510600, China  
ZHOU Zhemin Qing Da Gao Ke System Control Company, Beijing 100084, China  
YU Zhiwen Guangdong Power Grid Power Dispatching Control Center, Guangzhou 510600, China  
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Abstract:The renewable energy such like wind energy and solar energy is rather uncertain and intermittent, thus it has raised a more strict requirement for the efficiency of operating. Key section automatic detection and total transfer capability calculation online is the key way to guarantee security of large power grid by operators, thus it has been put much more attention. From the perspective of data-driven, in this paper, feature set is built to describe the state, and then machine learning method is utilized to map the feature set to whether the section exits or not, where KNN method is to play as a classifier. Numerical tests show that the proposed machine learning method can reduce the time needed to discover key sections and have high performance in accuracy compared with traditional methods. This work is supported by the Science and Technology Program of China Southern Power Grid:the Key Technology and Demonstration Application for Security Feature Selection and Knowledge Discovery in Complex Large-scale Power System Based on Big Data (No. GDKJ00000058).
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