A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F23%3A10252550" target="_blank" >RIV/61989100:27730/23:10252550 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27240/23:10252550

Result on the web

<a href="https://www.mdpi.com/1996-1073/16/11/4406" target="_blank" >https://www.mdpi.com/1996-1073/16/11/4406</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/en16114406" target="_blank" >10.3390/en16114406</a>

Result language

angličtina

Original language name

A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

Original language description

Power quality (PQ) monitoring and detection has emerged as an essential requirement due to the proliferation of sensitive power electronic interfacing devices, electric vehicle charging stations, energy storage devices, and distributed generation energy sources in the recent smart grid and microgrid scenarios. Even though, to date, the traditional approaches play a vital role in providing a solution to the above issue, the limitations, such as the requirement of significant human effort and not being scalable for large-scale power systems, force us to think of alternative approaches. Looking at a better perspective, deep-learning (DL) has gained the main attraction for various researchers due to its inherent capability to classify the data by extracting dominating and prominent features. This manuscript attempts to provide a comprehensive review of PQ detection and classification based on DL approaches to explore its potential, efficiency, and consistency to produce results accurately. In addition, this state-of-the-art review offers an overview of the novel concepts and the step-by-step method for detecting and classifying PQ events. This review has been presented categorically with DL approaches, such as convolutional neural networks (CNNs), autoencoders, and recurrent neural networks (RNNs), to analyze PQ data. This paper also highlights the challenges and limitations of using DL for PQ analysis, and identifies potential areas for future research. This review concludes that DL algorithms have shown promising PQ detection and classification results, and could replace traditional methods. (C) 2023 by the authors.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Energies

ISSN

1996-1073

e-ISSN

1996-1073

Volume of the periodical

16

Issue of the periodical within the volume

11

Country of publishing house

CH - SWITZERLAND

Number of pages

31

Pages from-to

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UT code for WoS article

001005216300001

EID of the result in the Scopus database

2-s2.0-85161582272