Application of Machine Learning for Online Dynamic Security Assessment in Presence of System Variability and Additive Instrumentation Errors

Anubhav Nath, Reetam Sen Biswas, Anamitra Pal

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

1 Scopus citations

Abstract

Large-scale blackouts that have occurred in the past few decades have necessitated the need to do extensive research in the field of grid security assessment. With the aid of synchrophasor technology, which uses phasor measurement unit (PMU) data, dynamic security assessment (DSA) can be performed online. However, existing applications of DSA are challenged by variability in system conditions and unaccounted for measurement errors. To overcome these challenges, this research develops a DSA scheme to provide security prediction in real-time for load profiles of different seasons in presence of realistic errors in the PMU measurements. The major contributions of this paper are: (1) develop a DSA scheme based on PMU data, (2) consider seasonal load profiles, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA with and without erroneous measurements. The performance of this approach is tested on the IEEE-118 bus system. Comparative analysis of the accuracies of the ML algorithms under different operating scenarios highlights the importance of considering realistic errors and variability in system conditions while creating a DSA scheme.

Original languageEnglish (US)
Title of host publication51st North American Power Symposium, NAPS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728104072
DOIs
StatePublished - Oct 2019
Externally publishedYes
Event51st North American Power Symposium, NAPS 2019 - Wichita, United States
Duration: Oct 13 2019Oct 15 2019

Publication series

Name51st North American Power Symposium, NAPS 2019

Conference

Conference51st North American Power Symposium, NAPS 2019
Country/TerritoryUnited States
CityWichita
Period10/13/1910/15/19

Keywords

  • Dynamic Security Assessment (DSA)
  • Machine Learning (ML)
  • Phasor Measurement Unit (PMU)
  • Renewable Generation

ASJC Scopus subject areas

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

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