A new fast-learning algorithm for predicting power system stability

Ahmed A. Daoud, George G. Karady, Ibrahim A. Amin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

This paper presents a new fast learning, on-line method for the prediction of power system transient instability and an example of its application to a single machine and infinite bus. The proposed algorithm is adapted from a proven robotic ball-catching algorithm, which includes fast learning. For instability prediction, the ball location is replaced by measured relative generator rotor angle. Using the measured relative rotor angle, the control algorithm predicts the rotor angle at a future time. The relative rotor angle is sampled at a rate of 600 times per second. This new fast learning algorithm predicts the rotor angle 500 milliseconds into the future. The increase of the generator relative rotor angle beyond a predetermined threshold is a prediction that loss of synchronism will occur. When loss of synchronism is predicted a protection scheme can initiate a stability aid such as generator tripping, braking resistor and/or fast valving.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Power Engineering Society Transmission and Distribution Conference
Pages594-598
Number of pages5
Volume2
EditionWINTER MEETING
Publication statusPublished - 2001
Event2001 IEEE Power Engineering Society Winter Meeting - Columbus, OH, United States
Duration: Jan 28 2001Feb 1 2001

Other

Other2001 IEEE Power Engineering Society Winter Meeting
CountryUnited States
CityColumbus, OH
Period1/28/012/1/01

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ASJC Scopus subject areas

  • Engineering(all)
  • Energy(all)

Cite this

Daoud, A. A., Karady, G. G., & Amin, I. A. (2001). A new fast-learning algorithm for predicting power system stability. In Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference (WINTER MEETING ed., Vol. 2, pp. 594-598)