KAUST Grant NumberOSR-2019-CoE-NEOM-4178.12
Permanent link to this recordhttp://hdl.handle.net/10754/667556
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AbstractThis paper proposes two different methods to train the DTs for voltage stability assessment, which in turn can aid in deriving preventive actions that can be given as recommendations to system operators or automatic load shedding schemes. In the voltage stability indices method, the DTs are trained on contingency cases that are classified based on voltage stability indices. In the region classification method, the DTs are trained on a new classification criterion that enlarges and generalizes the existing security boundary method of "stable"and "unstable"regions to a more granular operating space based on the distance from the nearest Saddle-Node Bifurcation. Case studies were performed using the Nordic 32 system for different contingency cases, several operating conditions and different network configurations. The ability to classify the degree of voltage stability of a multitude of operation conditions could be useful to aid operators in selecting and applying preventive measures to steer away the system from unstable conditions or conditions that are close to breaching operational requirements w.r.t. voltage stability.
CitationNarasimham Arava, V. S., & Vanfretti, L. (2020). Decision Trees for Voltage Stability Assessment. 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). doi:10.1109/smartgridcomm47815.2020.9302952
SponsorsThis work was supported in part by FP7/2007-2013 under Grant 283012, NYSERDA under agreement 137951, and by the Center of Excellence for NEOM Research at the King Abdullah University of Science and Technology under grant OSR-2019-CoE-NEOM-4178.12.
Conference/Event name2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020