RETRACTED ARTICLE: Detection and classification of power quality disturbances or events by adaptive NFS classifier

Kiruthiga, B.; Banu, R. Narmatha; Devaraj, D.

doi:10.1007/s00500-019-04538-7

Cited by 17 publications

(7 citation statements)

References 9 publications

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“…It can accurately estimate the PQD even in noisy environments. And, the literature [133] used the traditional HHT transform and NFS classifier. The combination of neural networks and fuzzy systems effectively improves the adaptability of neural networks.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Yan¹,

Chen²,

Geng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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With increasing global concerns about clean energy in smart grids, the detection of power quality disturbances (PQDs) caused by energy instability is becoming more and more prominent. It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous, which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids. In order to ensure safe and reliable equipment implementation, appropriate PQD detection technologies must be adopted to avoid such adverse effects. This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field, where specific scenarios and events for which each technique is applicable are also clearly presented. Finally, comments on the future evolution of PQD detection techniques are given. Unlike the published review articles, this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

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Section: Artificial Neural Networkmentioning

confidence: 99%

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Yan¹,

Chen²,

Geng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…The country's economic growth also depends upon the total quantity of power produced and supplied [1]. But there have occurred many losses in the presence of irregularity such as interruption in voltage, current, and frequency while transmitting the power [2]. These irregularities will degrade the efficiency of the equipment and cause the breakdown of electrical and electronic equipment.…”

Section: Introductionmentioning

confidence: 99%

Red deer optimized recurrent neural network for the classification of power quality disturbance

Dawood¹,

Babulal²

2023

Electr Eng

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Power Quality Disturbance (PQD) in a power grid distribution destroys the quality of power to the user. Thus, early detection of disturbances in the power grid distribution is essential to diagnose the network before failure. Several disturbances in the power grid may cause voltage sag, voltage swell, or occurrence of both. In the proposed method deep recurrent neural network (DRNN) is used for classifying the PQD as well as Red Deer Optimization (RDO) algorithm is used for optimizing the weight from DRNN. Based on the behaviour of deer roaring rate will optimize the weight of DRNN from RDO. Signal processing is done by S-transform (ST) because of the better performance in signals detection in terms of a high order of noise. The proposed method is implemented in Simulink tool and the results are compared with the existing methods. The result shows that the power disturbances are classified with high accuracy of 99.95% and precision of 99.98% that are higher than the existing methods.

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“…The Bagging ensemble classifier with the flexible analytic wavelet transform (FAWT) method in [ 32 ] is applied to discriminate multiple PQEs in RE connected power networks with promising results compared to individual weak classifiers. The S-Transform extraction method with Adaboost ensemble approach [ 33 ] and Hilbert Huang Transform feature extraction with adaptive NFS [ 34 ] have been used for PQ analysis with achievement of higher accuracy and better performance than single classifiers. Furthermore, DWT analysis with voting approach in [ 35 ] and stacking ensemble approach in [ 36 ] have shown better effectiveness in predicting various PQEs in the PV integrated power network.…”

Section: Introductionmentioning

confidence: 99%

A random subspace ensemble classification model for discrimination of power quality events in solar PV microgrid power network

Vinayagam¹,

Othman

Veerasamy

et al. 2022

PLoS ONE

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This study proposes SVM based Random Subspace (RS) ensemble classifier to discriminate different Power Quality Events (PQEs) in a photovoltaic (PV) connected Microgrid (MG) model. The MG model is developed and simulated with the presence of different PQEs (voltage and harmonic related signals and distinctive transients) in both on-grid and off-grid modes of MG network, respectively. In the pre-stage of classification, the features are extracted from numerous PQE signals by Discrete Wavelet Transform (DWT) analysis, and the extracted features are used to learn the classifiers at the final stage. In this study, first three Kernel types of SVM classifiers (Linear, Quadratic, and Cubic) are used to predict the different PQEs. Among the results that Cubic kernel SVM classifier offers higher accuracy and better performance than other kernel types (Linear and Quadradic). Further, to enhance the accuracy of SVM classifiers, a SVM based RS ensemble model is proposed and its effectiveness is verified with the results of kernel based SVM classifiers under the standard test condition (STC) and varying solar irradiance of PV in real time. From the final results, it can be concluded that the proposed method is more robust and offers superior performance with higher accuracy of classification than kernel based SVM classifiers.

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RETRACTED ARTICLE: Detection and classification of power quality disturbances or events by adaptive NFS classifier

Cited by 17 publications

References 9 publications

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Red deer optimized recurrent neural network for the classification of power quality disturbance

A random subspace ensemble classification model for discrimination of power quality events in solar PV microgrid power network

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