Classification of power quality events using deep learning on event images

Balouji, Ebrahim; Salor, Özgül

doi:10.1109/pria.2017.7983049

Cited by 46 publications

(27 citation statements)

References 9 publications

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“…Instead, the self-supervised training was carried out by the error between the reconstructed samples and the input samples. The trained system can extract features and identify sag sources on the input voltage sag waveform samples [37,38]. The structure of the self-supervised voltage sag source identification method based on CNN is shown in Figure 4.…”

Section: Self-supervised Voltage Sag Source Identification Methods Basmentioning

confidence: 99%

Self-Supervised Voltage Sag Source Identification Method Based on CNN

Mei

Zhang

et al. 2019

Energies

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A self-supervised voltage sag source identification method based on a convolution neural network is proposed in this study. In addition, a self-supervised CNN (Convolutional Neural Networks) voltage sag source identification model is constructed on the basis of the convolution neural network and AutoEncoder. The convolution layer and pool layer in CNN are used to extract the voltage sag characteristics, and the self-supervised network training process is realized based on the principle of AE. In the constructed mode, features which reflect the data characteristics are used rather than artificial features, thus improving the accuracy of practical application. It is unnecessary to input a lot of correct labels before the self-supervised training process. The model can meet the requirements of sag source identification on timeliness, practicability, diversity, and versatility in the context of modern big data. In this study, three-phase asymmetric sag sources in sag sources are classified into more detailed categories according to different fault phases. Therefore, the proposed method can not only identify the voltage sag source, but also accurately determine the specific fault phase. Finally, the optimal parameters of the model are recognized through a case study, and a self-supervised CNN model is established based on the data type of voltage sag. This model extracts features and identifies sag sources through the measured sag data. The superiority of the proposed method is verified by a comparison.

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Section: Self-supervised Voltage Sag Source Identification Methods Basmentioning

confidence: 99%

Self-Supervised Voltage Sag Source Identification Method Based on CNN

Mei

Zhang

et al. 2019

Energies

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“…With the increasing application of nonlinear and power electronics based loads and generators, the harmonic distortions and instable situations frequently appears in power grid. Deep learning is successfully employed for the classification of PQ events of the electricity networks in (Balouji & Salor, 2017). Instead of sampling the voltage data of the PQ event data like the existing analysis methods, the image files of the three-phase PQ events are processed for classification by deep learning techniques.…”

Section: Power Quality Monitoringmentioning

confidence: 99%

Big data analytics in smart grids: a review

2018

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Data analytics are now playing a more important role in the modern industrial systems. Driven by the development of information and communication technology, an information layer is now added to the conventional electricity transmission and distribution network for data collection, storage and analysis with the help of wide installation of smart meters and sensors. This paper introduces the big data analytics and corresponding applications in smart grids. The characterizations of big data, smart grids as well as huge amount of data collection are firstly discussed as a prelude to illustrating the motivation and potential advantages of implementing advanced data analytics in smart grids. Basic concepts and the procedures of the typical data analytics for general problems are also discussed. The advanced applications of different data analytics in smart grids are addressed as the main part of this paper. By dealing with huge amount of data from electricity network, meteorological information system, geographical information system etc., many benefits can be brought to the existing power system and improve the customer service as well as the social welfare in the era of big data. However, to advance the applications of the big data analytics in real smart grids, many issues such as techniques, awareness, synergies, etc., have to be overcome.

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“…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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Classification of power quality events using deep learning on event images

Cited by 46 publications

References 9 publications

Self-Supervised Voltage Sag Source Identification Method Based on CNN

Self-Supervised Voltage Sag Source Identification Method Based on CNN

Big data analytics in smart grids: a review

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

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Classification of power quality events using deep learning on event images

Cited by 46 publications

References 9 publications

Self-Supervised Voltage Sag Source Identification Method Based on CNN

Self-Supervised Voltage Sag Source Identification Method Based on CNN

Big data analytics in smart grids: a review

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

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