Predictive Online Transient Stability Assessment for Enhancing Efficiency

Rui Ma, Sara Eftekharnejad, Chen Zhong

Research output: Contribution to journalArticlepeer-review


Online transient stability assessment (TSA) is essential for the reliable operation of power systems. The increasing deployment of phasor measurement units (PMUs) across power systems provides a wealth of fast, accurate, and detailed transient data, offering significant opportunities to enhance online TSA. Unlike conventional data-driven methods that require large volumes of transient PMU data for accurate TSA, this paper develops a new TSA method that requires significantly less data. This data reduction is enabled by generative and adversarial networks (GAN), which predict voltage time-series data following a transient event, thereby minimizing the need for extensive data. A classifier embedded in the generative network deploys the predicted data to determine the stability of the system. The developed method preserves the temporal correlations in the multivariate time series data. Hence, compared to the state-of-the-art methods, it is more accurate using only one sample of the measured PMU data and has a shorter response time.

Original languageEnglish (US)
Pages (from-to)207-217
Number of pages11
JournalIEEE Open Access Journal of Power and Energy
StatePublished - 2024


  • Classification
  • generative adversarial networks
  • phasor measurement unit
  • transient stability

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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