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Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble

Author

Listed:
  • 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)

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.

Suggested Citation

  • Petar Sarajcev & Antonijo Kunac & Goran Petrovic & Marin Despalatovic, 2021. "Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble," Energies, MDPI, vol. 14(11), pages 1-26, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3148-:d:563895
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    References listed on IDEAS

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    1. Sergio Bruno & Giovanni De Carne & Massimo La Scala, 2020. "Distributed FACTS for Power System Transient Stability Control," Energies, MDPI, vol. 13(11), pages 1-16, June.
    2. Arcadio Perilla & Stelios Papadakis & Jose Luis Rueda Torres & Mart van der Meijden & Peter Palensky & Francisco Gonzalez-Longatt, 2020. "Transient Stability Performance of Power Systems with High Share of Wind Generators Equipped with Power-Angle Modulation Controllers or Fast Local Voltage Controllers," Energies, MDPI, vol. 13(16), pages 1-17, August.
    3. Yuwei Zhang & Wenying Liu & Fangyu Wang & Yaoxiang Zhang & Yalou Li, 2020. "Reactive Power Control Method for Enhancing the Transient Stability Total Transfer Capability of Transmission Lines for a System with Large-Scale Renewable Energy Sources," Energies, MDPI, vol. 13(12), pages 1-14, June.
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    Cited by:

    1. Teshome Lindi Kumissa & Fekadu Shewarega, 2023. "Fast Power System Transient Stability Simulation," Energies, MDPI, vol. 16(20), pages 1-17, October.
    2. Weijia Wen & Xiao Ling & Jianxin Sui & Junjie Lin, 2023. "Data-Driven Dynamic Stability Assessment in Large-Scale Power Grid Based on Deep Transfer Learning," Energies, MDPI, vol. 16(3), pages 1-15, January.
    3. Mihail Senyuk & Murodbek Safaraliev & Firuz Kamalov & Hana Sulieman, 2023. "Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology," Mathematics, MDPI, vol. 11(3), pages 1-15, January.
    4. Petar Sarajcev & Dino Lovric, 2024. "Machine Learning Classifier for Supporting Generator’s Impedance-Based Relay Protection Functions," Energies, MDPI, vol. 17(8), pages 1-16, April.
    5. Petar Sarajcev & Dino Lovric, 2023. "Manifold Learning in Electric Power System Transient Stability Analysis," Energies, MDPI, vol. 16(23), pages 1-20, November.
    6. Shitu Zhang & Zhixun Zhu & Yang Li, 2021. "A Critical Review of Data-Driven Transient Stability Assessment of Power Systems: Principles, Prospects and Challenges," Energies, MDPI, vol. 14(21), pages 1-13, November.
    7. Mahdi Khodayar & Jacob Regan, 2023. "Deep Neural Networks in Power Systems: A Review," Energies, MDPI, vol. 16(12), pages 1-38, June.
    8. Aristeidis Mystakidis & Paraskevas Koukaras & Nikolaos Tsalikidis & Dimosthenis Ioannidis & Christos Tjortjis, 2024. "Energy Forecasting: A Comprehensive Review of Techniques and Technologies," Energies, MDPI, vol. 17(7), pages 1-33, March.

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