Multiclass power quality events classification using variational mode decomposition with fast reduced kernel extreme learning machine‐based feature selection

Chakravorti, Tatiana; Dash, P.K.

doi:10.1049/iet-smt.2017.0123

Cited by 33 publications

(19 citation statements)

References 34 publications

(33 reference statements)

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“…Variational mode decomposition (VMD) [122][123][124][125][126][127][128][129][130][131] In compared with EMD, VMD can determine the related frequency bands more adaptively and estimates the corresponding modes simultaneously.…”

Section: Wavelet Transform and Its Variants' Based Methodsmentioning

confidence: 99%

“…122 In recent years, the VMD method was highly used for the study of power quality signals. [123][124][125][126][127][128][129][130][131] In Aneesh et al, 123 the authors presented a comparison between the VMD and EWT-based feature extraction technique for PQD&C using SVM. The obtained results prove the superiority of VMD-based feature extraction over EWT.…”

Section: Variational Mode Decomposition-based Methodsmentioning

confidence: 99%

“…Ucar et al 65 proposed a PQ disturbance detection and classification technique using DWT-based ELM. The ST was combined with an ELM technique for automatic nonstationary power quality disturbance recognition in Babu et al 87 Sahani and Dash 128 presented VMD and an afresh developed WOSELM for PQD&C in real-time environment. Similarly, the authors 129 used VMD and fast-reduced kernal ELM for PQD&C in real-time environment.…”

Section: Elm-based Approachmentioning

confidence: 99%

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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: Wavelet Transform and Its Variants' Based Methodsmentioning

confidence: 99%

Section: Variational Mode Decomposition-based Methodsmentioning

confidence: 99%

Section: Elm-based Approachmentioning

confidence: 99%

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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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“…Apart from the algorithms discussed in the preceding section, some new SP based techniques have performed a vital role in PQDs detection in the last two decades. These includes, advanced DSP techniques [121], [122], slanttransform (SLT) [123], improved chirplet transform (ICT) [124], amplitude and frequency demodulation (AFD) technique [125], higher-order statistics (HOS) [126] and HOS with case-based reasoning [127], time-time transform (TTT) [128], principal curves (PC) [129], DWT and IDWT [130], sequence components of voltages are measured in presence of solar PV using FFT [131], sparse signal decomposition on hybrid dictionaries reduced [132], kernel extreme learning machine technique [133], double resolution ST (DRST) [134], DWT, multi-resolution analysis, and the concept of signal energy [135], phase-locked loop (PLL) and symmetrical components [136], Reduced sample Hilbert-Huang transform (RSHHT) [137]. However, time-frequency based ST is found superior to STFT and WT [138].…”

Section: ) Miscellaneous Pqds Detection Techniquesmentioning

confidence: 99%

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

et al. 2020

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The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This paper presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.

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“…Moreover, the drastic fluctuations of the IMF will lead to large errors in the final forecast based on the EMD. Therefore, IMFs decomposed by VMD are more suitable for the establishment of hybrid forecasting model than IMFs decomposed by EMD [ 28 – 30 ].…”

Section: Data Simulation and Analysismentioning

confidence: 99%

A Hybrid Model for Forecasting Sunspots Time Series Based on Variational Mode Decomposition and Backpropagation Neural Network Improved by Firefly Algorithm

Yang

2018

Computational Intelligence and Neuroscience

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The change of the number of sunspots has a great impact on the Earth's climate, agriculture, communications, natural disasters, and other aspects, so it is very important to predict the number of sunspots. Aiming at the chaotic characteristics of monthly mean of sunspots, a novel hybrid model for forecasting sunspots time-series based on variational mode decomposition (VMD) and backpropagation (BP) neural network improved by firefly algorithm (FA) is proposed. Firstly, a set of intrinsic mode functions (IMFs) are obtained by VMD decomposition of the monthly mean time series of the sunspots. Secondly, the firefly algorithm is introduced to initialize the weights and thresholds of the BP neural network, and a prediction model is established for each IMF. Finally, the predicted values of these components are calculated to obtain the final predict results. Comparing BP model, FA-BP model, EMD-BP model, and VMD-BP model, the simulation results show that the proposed algorithm has higher prediction accuracy and can be used to forecast the time series of sunspots.

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Multiclass power quality events classification using variational mode decomposition with fast reduced kernel extreme learning machine‐based feature selection

Cited by 33 publications

References 34 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

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

A Hybrid Model for Forecasting Sunspots Time Series Based on Variational Mode Decomposition and Backpropagation Neural Network Improved by Firefly Algorithm

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