Classification of Power Quality Disturbances via Deep Learning

Ma, Jian; Zhang, Jun; Xiao, Lei; Chen, Kexu; Wu, Jianhua

doi:10.1080/02564602.2016.1196620

Cited by 67 publications

(29 citation statements)

References 20 publications

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“…[219][220][221][222][223] Ma et al 219 proposed stacked autoencoder (SAE) as a DL architecture to extract advanced features from PQ disturbance signals for automatic PQD&C. Moreover, variances of signals and a PSO algorithm were employed to support PQD&C process. It has been effectively used in various diverse research fields, such as speech recognition, human face recognition, computer vision, signal, image, and information processing.…”

Section: Deep Learning-based Approachmentioning

confidence: 99%

“…The application of DL algorithms on automatic power system fault detection and classification approaches was carried out by various authors in the previous studies. [219][220][221][222][223] Ma et al 219 proposed stacked autoencoder (SAE) as a DL architecture to extract advanced features from PQ disturbance signals for automatic PQD&C. Moreover, variances of signals and a PSO algorithm were employed to support PQD&C process. Liu et al 220 presented a new PQD&C method based on singular spectrum analysis (SSA), curvelet transform (CT), and deep convolutional neural networks (DCNNs).…”

Section: Deep Learning-based Approachmentioning

confidence: 99%

See 1 more Smart Citation

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Mishra

2019

Int Trans Electr Energ Syst

130

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Summary Power quality (PQ) studies have gained huge attention from the academics and the industry over the past three decades. The main objective of this article is to provide a comprehensive review on the state‐of‐the‐art techniques based on digital signal processing (DSP) and machine learning for automatic recognition of PQ events. It is aimed to present extensive information on the status of detection and classification of PQ events to the academics following a line of investigation on the similar domain. On the other hand, microgrid is one of the emerging architecture under the umbrella of smart grid infrastructure. In microgrid environment, the integration of renewable energy sources and distributed generators boosts the application of power electronic technology, which is the main cause of various PQ issues. Therefore, various PQ detection and classification (PQD&C) schemes for microgrid application using DSP and machine learning techniques have been reviewed in this article. Moreover, a comparative assessment on various PQD&C techniques has been carried out in details considering several criteria, such as type of data used (synthetic or real world), type of PQ disturbance studied (single or multiple), and performance in terms of accuracy (both noiseless and noisy environment). Hence, several types of research work in PQD&C area, such as the detection principles, benefits, and weaknesses of former works related to each PQD&C technique, are tinted in this manuscript. Therefore, from this review, the opportunities in PQD&C research domain in the power system can be explored further.

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Section: Deep Learning-based Approachmentioning

confidence: 99%

Section: Deep Learning-based Approachmentioning

confidence: 99%

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Mishra

2019

Int Trans Electr Energ Syst

130

View full text Add to dashboard Cite

show abstract

“…In [99] [113], Hybrid soft computing technique [114], higher order statistics and case-based reasoning [115]. In [116] the advantage of deep learning is utilized on image file classification, the image files are of three-phase power quality events data, while in [117] a staked autoencoder is used as a deep learning framework for the classification of power quality distribution. In [118] the proposed method decouples the signal of the power system into independent components and then classifies power quality disturbances by specialized classifiers.…”

Section: Miscellaneous Feature Extraction Techniquesmentioning

confidence: 99%

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification

Ahsan¹,

Pan²,

Li³

2018

JPEE

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Around the globe, the necessity of green supply with a dedicated standard quality thrust of consumers is increasing day by day. The advancement in technology urges the electrical power system to deliver a high-quality rated undistorted sinusoidal current, the voltage at a constant desired standard frequency to its consumers. The present paper reveals a complete and inclusive study of power quality events, such as automatic classification and signal processing via creative techniques and the noises effect on the detection and classification of power quality disturbances. It's planned to make a possible list for quick reference to obtain an extensive variety on the condition & status of available research for detection and classification for young engineers, designers and researchers who enter in the power quality field. The current extensive study is supported by a critical review of more than 200 publications on detection and classification techniques of power quality disturbances.

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“…In recent years, few classification methods have been explored for multiple PQDs . Most of the research work focused on only two kinds of multiple PQD such as sag with harmonics and swell plus harmonics . In Borras et al, harmonics plus sag, harmonics plus swell, oscillatory transient plus sag, oscillatory transient plus swell, and oscillatory transient with harmonics were classified using support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

Classification of multiple power quality events via compressed deep learning

Liu

Hussain

Shen

et al. 2019

Int Trans Electr Energ Syst

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Summary This paper presents a recently established compressed sensing (CS) and sparse autoencoder (SAE) based on deep learning (DL) method for classification of single and multiple power quality disturbances (PQDs). The CS technique is paying considerable attention in recent years due to below sampling rate comparatively Nyquist sampling. Initially, the CS technique is applied to extract the features of PQD waveforms. The extracted features are applied as inputs to the sparse autoencoder based on DL for classification of nine single and 22 combined classes of PQDs. The DL helps to remove a redundant feature and improves classification performance. Finally, backpropagation is applied to fine‐tune the entire network. The effectiveness of the proposed algorithm has been tested with more than 6580 numbers of real and synthetic single and multiple PQD data, and the results are recorded. High correct classification rate is obtained with noise and without noise level. Noise level was considered from 20 to 50 dB. The performance of the proposed technique has been assessed by comparing the results against recently reported methods. Results show that the proposed CS‐ and SAE‐based DL algorithms can be efficiently used for single and multiple PQDs classifications.

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Classification of Power Quality Disturbances via Deep Learning

Cited by 67 publications

References 20 publications

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification

Classification of multiple power quality events via compressed deep learning

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Classification of Power Quality Disturbances via Deep Learning

Cited by 67 publications

References 20 publications

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection &amp; Classification

Classification of multiple power quality events via compressed deep learning

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A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification