Data-Driven Security Assessment of Power Grids Based on Machine Learning Approach: Preprint

Conference · Wed Mar 18 04:00:00 EDT 2020

OSTI ID:1606111

Xiao, H. ^[1]; Fabus, S. ^[1]; Su, Y. ^[1]; You, S. ^[1]; Zhao, Y. ^[1]; Li, H. ^[1]; Zhang, C. ^[1]; Liu, Y. ^[2]; Yuan, Haoyu ^[3]; Zhang, Yingchen ^[3]; Tan, Jin ^[3]

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1. University of Tennessee, Knoxville
2. University of Tennessee, Knoxville; Oak Ridge National Laboratory
3. National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Data-driven security assessment provides key indicators on power system stability using simulations on scheduling models, as opposed to dynamic simulations that are more time-consuming. This paper investigates data-driven security assessment of power grids based on machine learning. Multivariate random forest regression is used as the machine learning algorithm due to its high robustness to the input data. Three stability issues are analyzed using the proposed machine learning tool, including transient stability, frequency stability and small signal stability. The estimation values from machine learning tool are compared with those from dynamic simulations. Results show that the proposed machine learning tool can effectively predict the stability margins for the three stability metrics.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1606111
• Report Number(s): NREL/CP-5D00-74256
• Country of Publication: United States
• Language: English

Related Subjects

14 SOLAR ENERGY
97 MATHEMATICS AND COMPUTING
data-driven security assessment
frequency stability
machine learning
multivariate random forest
small-signal stability
transient stability