Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble

Petar Sarajcev, Antonijo Kunac, Goran Petrovic and Marin Despalatovic
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Petar Sarajcev: Department of Power Engineering, University of Split, FESB, 21000 Split, Croatia
Antonijo Kunac: Department of Power Engineering, University of Split, FESB, 21000 Split, Croatia
Goran Petrovic: Department of Power Engineering, University of Split, FESB, 21000 Split, Croatia
Marin Despalatovic: Department of Power Engineering, University of Split, FESB, 21000 Split, Croatia

Energies, 2021, vol. 14, issue 11, 1-26

Abstract: Increased integration of renewable energy sources brings new challenges to the secure and stable power system operation. Operational challenges emanating from the reduced system inertia, in particular, will have important repercussions on the power system transient stability assessment (TSA). At the same time, a rise of the “big data” in the power system, from the development of wide area monitoring systems, introduces new paradigms for dealing with these challenges. Transient stability concerns are drawing attention of various stakeholders as they can be the leading causes of major outages. The aim of this paper is to address the power system TSA problem from the perspective of data mining and machine learning (ML). A novel 3.8 GB open dataset of time-domain phasor measurements signals is built from dynamic simulations of the IEEE New England 39-bus test case power system. A data processing pipeline is developed for features engineering and statistical post-processing. A complete ML model is proposed for the TSA analysis, built from a denoising stacked autoencoder and a voting ensemble classifier. Ensemble consist of pooling predictions from a support vector machine and a random forest. Results from the classifier application on the test case power system are reported and discussed. The ML application to the TSA problem is promising, since it is able to ingest huge amounts of data while retaining the ability to generalize and support real-time decisions.

Keywords: power system stability; transient stability assessment; transient stability index; machine learning; deep learning; autoencoder; transfer learning; ensemble; dataset (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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