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

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

doi:10.3390/en14237995

Raoult Teukam Dabou, Jacques Tagoudjeu + Show 2 more

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https://doi.org/10.3390/en14237995

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Journal: Energies	Publication Date: Nov 30, 2021
Citations: 5	License type: CC BY 4.0

Affiliation: Université de Yaoundé I, Université Laval, Hydro-Québec

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).

Highlights

Introduction published maps and institutional affilDynamic state estimation (DSE) is a fast-developing tool for stability monitoring and control [1]
According to the IEEE TF on DSE [2], enhanced system observability using DSE based internal angle and speed estimates will lead to several breakthroughs in Wide-Area System Integrity Protection Systems (WA-SIPS) functions, such as faster out-of-step detection, more realistic location of runaway generator and minimal amount of generation/load to be shed in order to preserve system integrity without knowing the topology accurately
Several approaches are proposed in [9] for sparse signal decomposition based on discrete cosine transform (DCT)+K-singular value decomposition (SVD), DCT+ method of optimal directions (MOD), discrete wavelet transform (DWT)+K-SVD, and DWT+MOD

Summary

Introduction

Introduction published maps and institutional affilDynamic state estimation (DSE) is a fast-developing tool for stability monitoring and control [1]. According to the IEEE TF on DSE [2], enhanced system observability using DSE based internal angle and speed estimates will lead to several breakthroughs in Wide-Area System Integrity Protection Systems (WA-SIPS) functions, such as faster out-of-step detection, more realistic location of runaway generator and minimal amount of generation/load to be shed in order to preserve system integrity without knowing the topology accurately. Instrumental in these achievements is the ability to predict fault-induced instability from DSE based information in real-time, reliably, securely, and fast enough to enable timely and effective countermeasures [3]. In [10], new automatic transient detection and localization approaches are proposed for power quality analysis based on an over-complete dictionary (OCD) and − 1 norm minimization

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