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)

Xiao, Huaishuo; Wei, Jianchun; Li, Qingquan

doi:10.3390/en10111809

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…The cluster-based sparse coding (CSC) algorithm is proposed in [20] for multiple events detection, recognition, and temporal localization in large-scale power systems while in [21], sparse representation classification and random dimensionality reduction projection are used to extract, reduce feature dimensionality and classify power system faults. The singular value decomposition (SVD) and total least square estimation via rotational invariance techniques are used in [22], to analyze and extract amplitude, frequency, attenuation coefficient, initial phase of combined PQs. Among all dictionaries-based learning schemes, which built on K-SVD and OMP algorithms, used e.g., in [23,24] for short-term prediction of solar power fluctuations, is often considered as the most powerful computationally and accuracy wise.…”

Section: Background On Sparse Dictionaries Learningmentioning

confidence: 99%

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

et al. 2021

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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).

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Section: Background On Sparse Dictionaries Learningmentioning

confidence: 99%

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

et al. 2021

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“…Flicker waveform, which has amplitude envelope with low frequency component, was modelled mathematically as in Formula (10)…”

Section: Flicker Separationmentioning

confidence: 99%

“…These disturbances greatly affect the safe and economical operations of power systems, decreasing the lifetime and performance of electrical equipment connected to the system. PQD analysis, including disturbances detection and classification, is an important task to provide adequate information about remedial actions to address the root problem [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network

et al. 2018

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This paper proposes a novel, two-stage and hybrid approach based on variational mode decomposition (VMD) and the deep stochastic configuration network (DSCN) for power quality (PQ) disturbances detection and classification in power systems. Firstly, a VMD technique is applied to discriminate between stationary and non-stationary PQ events. Secondly, the key parameters of VMD are determined as per different types of disturbance. Three statistical features (mean, variance, and kurtosis) are extracted from the instantaneous amplitude (IA) of the decomposed modes. The DSCN model is then developed to classify PQ disturbances based on these features. The proposed approach is validated by analytical results and actual measurements. Moreover, it is also compared with existing methods including wavelet network, fuzzy and S-transform (ST), adaptive linear neuron (ADALINE) and feedforward neural network (FFNN). Test results have proved that the proposed method is capable of providing necessary and accurate information for PQ disturbances in order to plan PQ remedy actions accordingly.

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An Integrated Approach Improved Fast S-transform and SVD Noise Reduction for Classification of Power Quality Disruptions in Noisy Environments

Goh

Liao

Zhang

et al. 2022

Electric Power Components and Systems

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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)

Cited by 4 publications

References 27 publications

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

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

Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network

An Integrated Approach Improved Fast S-transform and SVD Noise Reduction for Classification of Power Quality Disruptions in Noisy Environments

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