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Sparse Signal Reconstruction on Fixed and Adaptive Supervised Dictionary Learning for Transient Stability Assessment

Author

Listed:
  • Raoult Teukam Dabou

    (Electrical and Computer Science Engineering Department, Laval University, Quebec, QC G1V 0A6, Canada)

  • Innocent Kamwa

    (Electrical and Computer Science Engineering Department, Laval University, Quebec, QC G1V 0A6, Canada)

  • Jacques Tagoudjeu

    (Department of Mathematics and Physical Science, National Advanced School of Engineering of Yaoundé, University of Yaoundé I, Yaoundé P.O. Box 8390, Cameroon)

  • Francis Chuma Mugombozi

    (Department of Power Systems Simulation and Evolution, Research Institute of Hydro Québec/IREQ, Varennes, QC J3X 1S1, Canada)

Abstract

Fixed and adaptive supervised dictionary learning (SDL) is proposed in this paper for wide-area stability assessment. Single and hybrid fixed structures are developed based on impulse dictionary (ID), discrete Haar transform (DHT), discrete cosine transform (DCT), discrete sine transform (DST), and discrete wavelet transform (DWT) for sparse features extraction and online transient stability prediction. The fixed structures performance is compared with that obtained from transient K-singular value decomposition (TK-SVD) implemented while adding a stability status term to the optimization problem. Stable and unstable dictionary learning are designed based on datasets recorded by simulating thousands of contingencies with varying faults, load, and generator switching on the IEEE 68-bus test system. This separate supervised learning of stable and unstable scenarios allows determining root mean square error (RMSE), useful for online stability status assessment of new scenarios. With respect to the RMSE performance metric in signal reconstruction-based stability prediction, the present analysis demonstrates that [DWT], [DHT|DWT] and [DST|DHT|DCT] are better stability descriptors compared to K-SVD, [DHT], [DCT], [DCT|DWT], [DHT|DCT], [ID|DCT|DST], and [DWT|DHT|DCT] on test datasets. However, the K-SVD approach is faster to execute in both off-line training and real-time playback while yielding satisfactory accuracy in transient stability prediction (i.e., 7.5-cycles decision window after fault-clearing).

Suggested Citation

  • Raoult Teukam Dabou & Innocent Kamwa & Jacques Tagoudjeu & Francis Chuma Mugombozi, 2021. "Sparse Signal Reconstruction on Fixed and Adaptive Supervised Dictionary Learning for Transient Stability Assessment," Energies, MDPI, vol. 14(23), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7995-:d:691720
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    References listed on IDEAS

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    1. Juan Carlos Bravo-Rodríguez & Francisco J. Torres & María D. Borrás, 2020. "Hybrid Machine Learning Models for Classifying Power Quality Disturbances: A Comparative Study," Energies, MDPI, vol. 13(11), pages 1-20, June.
    2. Rial A. Rajagukguk & Raden A. A. Ramadhan & Hyun-Jin Lee, 2020. "A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power," Energies, MDPI, vol. 13(24), pages 1-23, December.
    3. Huaishuo Xiao & Jianchun Wei & Qingquan Li, 2017. "Identification of Combined Power Quality Disturbances Using Singular Value Decomposition (SVD) and Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Techniques (TLS-ESPRIT)," Energies, MDPI, vol. 10(11), pages 1-16, November.
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    Cited by:

    1. 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.
    2. Zbigniew Leonowicz & Michal Jasinski, 2022. "Machine Learning and Data Mining Applications in Power Systems," Energies, MDPI, vol. 15(5), pages 1-2, February.

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