The stability of inter-area electromechanical oscillations
are critical to power system reliability. Due to the complexities
of power systems, relationships between system conditions
and oscillation characteristics, such as damping and frequency,
tend to be expressed only in generalities. In this study, a list of
influential factors on Western Electricity Coordinating Council
(WECC) interconnect modal characteristics are identified and
evaluated with advanced machine learning techniques including
principal component analysis, analysis of variance, random forest
feature selection, support vector machine, and artificial neural
network approaches.
Revised: May 15, 2019 |
Published: August 5, 2018
Citation
Hou Z., J.D. Follum, P.V. Etingov, F.K. Tuffner, D. Kosterev, and G.H. Matthews. 2018.Machine Learning of Factors Influencing Damping and Frequency of Dominant Inter-area Modes in the WECC Interconnect. In IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS 2018), June 24-28, 2018, Boise, ID, 1-6. Piscataway, New Jersey:IEEE.PNNL-SA-130439.doi:10.1109/PMAPS.2018.8440361