This paper utilizes modern statistical and machine learning methodology to track oscillation modes in complex power engineering systems. The damping ratio of the electromechanical oscillation mode is formulated as a function of power of the generators and loads as well as bus voltage magnitudes in the entire power system. The celebrated Lasso algorithm is implemented to solve this high-dimension modeling problem. By the nature of the $L_1$ design, the Lasso algorithm can automatically render a sparse solution, and by eliminating redundant features, it provides desirable prediction power.

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[1] Weike Mo, "Power System Oscillation Mode Prediction Data", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/8dvt-6c38. Accessed: Dec. 08, 2024.
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url = {http://dx.doi.org/10.21227/8dvt-6c38},
author = {Weike Mo },
publisher = {IEEE Dataport},
title = {Power System Oscillation Mode Prediction Data},
year = {2019} }
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T1 - Power System Oscillation Mode Prediction Data
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Weike Mo. (2019). Power System Oscillation Mode Prediction Data. IEEE Dataport. http://dx.doi.org/10.21227/8dvt-6c38
Weike Mo, 2019. Power System Oscillation Mode Prediction Data. Available at: http://dx.doi.org/10.21227/8dvt-6c38.
Weike Mo. (2019). "Power System Oscillation Mode Prediction Data." Web.
1. Weike Mo. Power System Oscillation Mode Prediction Data [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/8dvt-6c38
Weike Mo. "Power System Oscillation Mode Prediction Data." doi: 10.21227/8dvt-6c38