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

Raoult Teukam Dabou, Innocent Kamwa, Jacques Tagoudjeu and Francis Chuma Mugombozi
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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

Energies, 2021, vol. 14, issue 23, 1-20

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).

Keywords: sparse signal decomposition; supervised dictionary learning; dictionary impulsion; singular value decomposition; discrete cosine transform; discrete Haar transform; discrete wavelet transform; transient stability assessment (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 (2)

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