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Comparative Study of Event Prediction in Power Grids using Supervised Machine Learning Methods

Comparative Study of Event Prediction in Power Grids using Supervised Machine Learning Methods

Kategori
Vitenskapelig Kapittel/Artikkel/Konferanseartikkel
Sammendrag
There is a growing interest in applying machine learning methods on large amounts of data to solve complex problems, such as prediction of events and disturbances in the power system. This paper is a comparative study of the predictive performance of state-of-the-art supervised machine learning methods. The event prediction models are trained and validated using high-resolution power quality data from measuring instruments in the Norwegian power grid. The recorded event categories in the study were voltage dips, ground faults, rapid voltage changes and interruptions. Out of the tested machine learning methods, the Random Forest models indicated a better prediction performance, with an accuracy of 0.602. The results also indicated that rapid voltage changes (accuracy = 0.710) and voltage dips (accuracy = 0.601) are easiest to predict among the tested power quality events.
Oppdragsgiver
  • Research Council of Norway (RCN) / 268193
Språk
Engelsk
Forfatter(e)
Institusjon(er)
  • SINTEF Energi AS / Energisystemer
  • Norges teknisk-naturvitenskapelige universitet
År
Forlag
IEEE
Bok
2020 International Conference on Smart Energy Systems and Technologies - SEST
ISBN
978-1-7281-4701-7