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

Xiao, H.; Fabus, S.; Su, Y.; You, S.; Zhao, Y.; Li, H.; Zhang, C.; Liu, Y.; Yuan, Haoyu; Zhang, Yingchen; Tan, Jin

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DOE OSTI · 1606111

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

Xiao, H.·Fabus, S.·Su, Y.·You, S.·Zhao, Y.·Li, H.·Zhang, C.·Liu, Y.·Yuan, Haoyu·Zhang, Yingchen·Tan, Jin

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Abstract

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.

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

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Xiao, H., Fabus, S., Su, Y., You, S., Zhao, Y., Li, H., Zhang, C., Liu, Y., Yuan, Haoyu, Zhang, Yingchen, Tan, Jin. 2020-03-18. Data-Driven Security Assessment of Power Grids Based on Machine Learning Approach: Preprint. https://www.osti.gov/biblio/1606111

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