Comparative study of clustering techniques for real‐time dynamic model reduction

Statistical Analysis and Data Mining

Lu, Shuai¹  Cotilla-Sanchez, Eduardo²  Huang, Zhenyu³  Wang, Shaobu³  Purvine, Emilie⁴  Halappanavar, Mahantesh⁵  Lin, Guang⁶ 

[1] EnerMod Austin, Texas;Oregon State University School of Electrical Engineering and Computer Science Corvallis, Oregon;Pacific Northwest National Laboratory Energy and Environment Directorate Richland, Washington;Pacific Northwest National Laboratory National Security Directorate Richland, Washington;Pacific Northwest National Laboratory Physical and Computational Sciences Directorate Richland, Washington;Purdue University Department of Mathematics West Lafayette, Indiana

关键词: graph clustering;    model reduction;    power system dynamics;    SVD;   

DOI  :  10.1002/sam.11352

学科分类：社会科学、人文和艺术（综合）

来源: John Wiley & Sons, Inc.

【 摘 要 】

Dynamic model reduction in power systems is necessary for improving computational efficiency. Traditional model reduction using linearized models or offline analysis is not adequate to capture dynamic behaviors of the power system, especially with the new mix of intermittent generation and intelligent consumption, making the power system more dynamic and nonlinear. Real-time dynamic model reduction has emerged to fill this important need. This paper explores using clustering techniques to analyze real-time phasor measurements to identify groups of generators with similar behavior, as well as a representative generator from each group for dynamic model reduction. Two clustering techniques—graph clustering and k-means—are considered. These techniques are compared with a previously developed dynamic model reduction approach using singular value decomposition. Two sample power grid datasets are used to test these different model reduction techniques. Based on the algorithms' relative performance, recommendations are provided for practical use.

【 授权许可】

Unknown