Data-driven security assessment of the electric power system

Seyedali Meghdadi, Guido Tack, Ariel Liebman

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


The transition to a new low emission energy future results in a changing mix of generation and load types due to significant growth in renewable energy penetration and reduction in system inertia due to the exit of ageing fossil fuel power plants. This increases technical challenges for electrical grid planning and operation. This study introduces a new decomposition approach to account for the system security for short term planning using conventional machine learning tools. The immediate value of this work is that it provides extendable and computationally efficient guidelines for using supervised learning tools to assess first swing transient stability status. To provide an unbiased evaluation of the final model fit on the training dataset, the proposed approach was examined on a previously unseen test set. It distinguished stable and unstable cases in the test set accurately, with only 0.57% error, and showed a high precision in predicting the time of instability, with 6.8% error and mean absolute error as small as 0.0145.

Original languageEnglish
Title of host publication9th International Conference on Power and Energy Systems, ICPES 2019
EditorsXiangjing Su
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781728126586, 9781728126579
ISBN (Print)9781728126593
Publication statusPublished - 2019
EventInternational Conference on Power and Energy Systems 2019 - Perth, Australia
Duration: 10 Dec 201912 Dec 2019
Conference number: 9th (Proceedings ) (Website)


ConferenceInternational Conference on Power and Energy Systems 2019
Abbreviated titleICPES 2019
OtherThis is not the same conference series as ERA conference record as "International Conference on Power and Energy Systems (POES)"
Internet address


  • Machine learning
  • power system dynamics
  • transient stability

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