Automatic detection-localization of fault point on waveform and classification of power quality disturbance waveshape fault using wavelet and neural network

Dilokratanatrakool, C.; Ayudhya, Piyasawat Navaratana Na; Chayavanich, T.; Prapanavarat, C.

doi:10.1109/rissp.2003.1285564

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…The Time delay neural network (TDNN) and feed-forward neural network (FFNN) are the two different Paradigms of neural networks for the classification of power system disturbance waveforms which are presented in [125]. In [126] Wave-shape fault is displayed for automatic detection, localization and to classify various types of disturbances. In [127] a neural network based approach is proposed to identify the non-intrusive harmonic source.…”

Section: Classification Based On the Neural Networkmentioning

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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Section: Classification Based On the Neural Networkmentioning

confidence: 99%

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

Ahsan¹,

Pan²,

Li³

2018

JPEE

View full text Add to dashboard Cite

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“…It is implemented primarily for classifying power quality events as proposed in [13]. Authors have proposed an automatic localisation method for various power [14]. An approach of identification of harmonics source is presented in [15].…”

Section: Introductionmentioning

confidence: 99%

Evaluation and classification of power quality disturbances based on discrete Wavelet Transform and artificial neural networks

Alshahrani

Abbod

Alamri

et al. 2015

2015 50th International Universities Power Engineering Conference (UPEC)

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In this paper, detection method and classification technique of power quality disturbances is presented. Due to the increase of nonlinear load recently, it becomes an essential requirement to insure high level of power supply and efficient commotional consuming. Wavelet Transform represents a powerful mathematical platform which is needed especially at non-stationary situations. Disturbances are fed into wavelets to filter, detect and extract its features at different frequencies.Training of features extracted by DWT is done using artificial neural networks ANN to classify power quality disturbances.

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“…In power quality analysis, it is used to localise events associated power lines as in [12]. An automatic fault detector is proposed in [13]. It has helped in harmonic identification method presented in [14].…”

Section: Introductionmentioning

confidence: 99%

Detection and classification of power quality events based on wavelet transform and artificial neural networks for smart grids

Alshahrani

Abbod

Alamri

2015

2015 Saudi Arabia Smart Grid (SASG)

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Automatic detection-localization of fault point on waveform and classification of power quality disturbance waveshape fault using wavelet and neural network

Cited by 5 publications

References 7 publications

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

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

Evaluation and classification of power quality disturbances based on discrete Wavelet Transform and artificial neural networks

Detection and classification of power quality events based on wavelet transform and artificial neural networks for smart grids

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Automatic detection-localization of fault point on waveform and classification of power quality disturbance waveshape fault using wavelet and neural network

Cited by 5 publications

References 7 publications

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

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

Evaluation and classification of power quality disturbances based on discrete Wavelet Transform and artificial neural networks

Detection and classification of power quality events based on wavelet transform and artificial neural networks for smart grids

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Product

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

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