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UID:20260901T181723EDT-6234bSeuIs@132.216.98.100
DTSTAMP:20260901T221723Z
DESCRIPTION:Abstract\n\nThe widespread adoption of renewable energy is caus
 ing unpredictability in power networks\, making it hard to predict critica
 l operating points. This new reality challenges traditional stability asse
 ssment methods still used by most grid operators.\n\nIt becomes essential 
 to adopt a systematic approach that encompasses all hourly operating point
 s over the course of a study year. This thesis introduces novel assessment
  frameworks that leverage machine learning techniques to allow rapid\, det
 erministic time-series assessments of angular transient stability in the c
 ontext of high renewable penetration. The frameworks not only offer an eva
 luation of the transient stability of the grid at a high level but also pr
 ovide the possibility of performing meticulous analyses of emerging trends
  in the dynamic responses of individual synchronous generators within syst
 ems experiencing reduced inertia.\n\nThe heavy computational burden associ
 ated with such time-series stability assessments are substantially reduced
  through the strategic use of supervised and unsupervised learning algorit
 hms. A modified version of the Affinity Propagation clustering algorithm i
 s proposed to cluster the subset of all operating points of a given study 
 year and derive a representative subset of these points. In addition\, Gra
 dient Boosting Regressors are also used to predict transient stability ind
 ices for all hours of the studied year. And finally\, an agglomerative hie
 rarchical clustering algorithm is proposed to cluster synchronous generato
 rs based on their dynamic response.\n\nThe proposed frameworks are demonst
 rated to be ideal for grid planners in identifying pathways to achieve rel
 iable integration of renewable energy resources. Through a series of case 
 studies\, these frameworks were evaluated to determine the transient stabi
 lity performance of an IEEE-39 test system augmented with renewable energy
  resources.\n
DTSTART:20240517T163000Z
DTEND:20240517T183000Z
LOCATION:Room 603\, McConnell Engineering Building\, CA\, QC\, Montreal\, H
 3A 0E9\, 3480 rue University
SUMMARY:PhD defence of Tayeb Meridji –  Power System Stability Assessment F
 rameworks Using Machine-Learning Techniques
URL:https://www.mcgill.ca/ece/channels/event/phd-defence-tayeb-meridji-powe
 r-system-stability-assessment-frameworks-using-machine-learning-357321
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