Machine-Learning-based Advanced Dynamic Security Assessment: Prediction of Loss of Synchronism in Generators

Ramin Vakili, Mojdeh Khorsand

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

Abstract

This paper proposes a machine-learning-based advanced online dynamic security assessment (DSA) method, which provides a detailed evaluation of the system stability after a disturbance by predicting impending loss of synchronism (LOS) of generators. Voltage angles at generator buses are used as the features of the different random forest (RF) classifiers which are trained to consecutively predict LOS of the generators as a contingency proceeds and updated measurements become available. A wide range of contingencies for various topologies and operating conditions of the IEEE 118-bus system has been studied in offline analysis using the GE positive sequence load flow analysis (PSLF) software to create a comprehensive dataset for training and testing the RF models. The performances of the trained models are evaluated in the presence of measurement errors using various metrics. The results reveal that the trained models are accurate, fast, and robust to measurement errors.

Original languageEnglish (US)
Title of host publication2020 52nd North American Power Symposium, NAPS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728181929
DOIs
StatePublished - Apr 11 2021
Externally publishedYes
Event52nd North American Power Symposium, NAPS 2020 - Tempe, United States
Duration: Apr 11 2021Apr 13 2021

Publication series

Name2020 52nd North American Power Symposium, NAPS 2020

Conference

Conference52nd North American Power Symposium, NAPS 2020
Country/TerritoryUnited States
CityTempe
Period4/11/214/13/21

Keywords

  • Machine learning
  • online dynamic security assessment
  • predicting loss of synchronism
  • random forest classifier
  • stability assessment

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Hardware and Architecture
  • Computer Networks and Communications

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