Detection and classification of power quality disturbances in time domain using probabilistic neural network

Chen, Z. M.; Li, M. S.; Ji, Tianyao

doi:10.1109/ijcnn.2016.7727344

Cited by 13 publications

(6 citation statements)

References 11 publications

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“…In the case of [15], probabilistic Neural Networks are compared with multilayer with feedback using Wavelet with multiresolution analysis to extract the characteristics, and it was concluded that probabilistic Neural Networks are more efficient than multilayer Neural Networks. In [38], the increased effectiveness of probabilistic Neural Networks for event classification was also concluded. In this case, instead of performing it in the frequency domain, it is performed in the time domain, so that the analysis and extraction of characteristics is conducted through mathematical morphology models and with the Teager energy operator.In [39], convolutional Neural Networks are used to detect and classify disturbances.…”

Section: Neural Networkmentioning

confidence: 96%

Techniques to Locate the Origin of Power Quality Disturbances in a Power System: A Review

et al. 2022

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The complexity in the power system topology, together with the new paradigm in generation and demand, make achieving an adequate level of supply quality a complicated goal for distribution companies. The electrical system power quality is subject to different regulations. On one hand, EN-50160 establishes the characteristics of the voltage supplied by public electricity networks, therefore affecting distribution companies. On the other hand, the EN-61000 series of standards regulates the electromagnetic compatibility of devices connected to the network, therefore affecting the loads. Power companies and device manufacturers are both responsible and affected in the issue of quality of supply. Despite the regulations, there are certain aspects of the supply quality that are not solved. One of the most important is the location of the disturbance’s origin. This paper presents a review of the main techniques to locate the disturbance’s origin in the electric network through two approaches: identification of the disturbance’s cause and the location of the origin.

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Section: Neural Networkmentioning

confidence: 96%

Techniques to Locate the Origin of Power Quality Disturbances in a Power System: A Review

et al. 2022

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“…In [139] a multilayer perception neural network is presented for classification of power quality. As in most research, the authors used frequency domain as an analysis tool, but in this paper [140] the author presented an approach in the time domain for detection and classification of power quality. In this paper hybrid detection and classification method is shown in which quantity characteristics are introduced then recognition algorithm is used for signal and multiple disturbances recognition [141].…”

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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“…Other techniques such as sparse signal decomposition (SSD) [16], radial basis function neural network [17], and probabilistic neural network techniques in [18,19] have been used to identify and detect PQ problems. In [20], the phasor measurement units based on the fuzzy technique have been used to categorise the PQ disturbances.…”

Section: Introductionmentioning

confidence: 99%

Categorisation of power quality problems using long short‐term memory networks

Abdelsalam

Hassanin

Hasanien

2021

IET Generation Trans & Dist

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Recognition of power quality (PQ) troubles is a critical task in the electrical power system. All previous works solve this problem using two‐step methodology: Feature extraction and classification steps with each step using its own techniques, and this consumes a computation time. The purpose of this study is to utilise a novel artificial intelligence (AI) technique for recognition of PQ events. The proposed AI technique is the long short‐term memory (LSTM) network, which detects and classifies the PQ events in one step. This technique extracts amplitude, disturbance duration and total harmonic distortion from the captured waveform, and the LSTM uses its own rules to classify the PQ events. Many simple PQ events such as interruption, sag, flicker, swell and surge or complex PQ events such as sag plus harmonics and swell plus harmonics are generated using MATLAB programming environment to evaluate the performance of LSTM. Also, real‐time measurements are collected from an industrial substation and are used to ensure the effectiveness of the proposed LSTM technique. A comparison with other techniques is conducted and the results verify the good performance of LSTM in classifying the PQ problems.

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Detection and classification of power quality disturbances in time domain using probabilistic neural network

Cited by 13 publications

References 11 publications

Techniques to Locate the Origin of Power Quality Disturbances in a Power System: A Review

Techniques to Locate the Origin of Power Quality Disturbances in a Power System: A Review

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

Categorisation of power quality problems using long short‐term memory networks

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Detection and classification of power quality disturbances in time domain using probabilistic neural network

Cited by 13 publications

References 11 publications

Techniques to Locate the Origin of Power Quality Disturbances in a Power System: A Review

Techniques to Locate the Origin of Power Quality Disturbances in a Power System: A Review

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

Categorisation of power quality problems using long short‐term memory networks

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Product

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