Artificial intelligence and machine learning techniques for power quality event classification: a focused review and future insights

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10256781" target="_blank" >RIV/61989100:27240/25:10256781 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27730/25:10256781

Result on the web

<a href="https://doi.org/10.1016/j.rineng.2024.103873" target="_blank" >https://doi.org/10.1016/j.rineng.2024.103873</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rineng.2024.103873" target="_blank" >10.1016/j.rineng.2024.103873</a>

Result language

angličtina

Original language name

Artificial intelligence and machine learning techniques for power quality event classification: a focused review and future insights

Original language description

Power Quality (PQ) disturbances are critical in modern power systems, significantly impacting electrical networks&apos; stability, reliability, and efficiency. With the increasing penetration of renewable energy sources, non-linear loads, and power electronic devices, the detection, classification, and mitigation of PQ disturbances have become more complex. Traditional PQ analysis methods, which rely heavily on human expertise and rule-based systems, are often insufficient in handling the growing complexity and volume of data in real-time applications. This review comprehensively analyzes the latest advancements in Artificial Intelligence (AI) and Machine Learning (ML) techniques applied to PQ analysis, achieving classification accuracies as high as 99.94 % with hybrid approaches like dual-tree wavelet packet transforms combined with extreme learning machine (ELM). Integrating advanced signal processing techniques, such as wavelet transforms and empirical mode decomposition, has demonstrated accuracy improvements of up to 5 % in challenging scenarios. This paper explores the challenges associated with AI-based PQ analysis, including the need for large datasets, overfitting issues, and the lack of interpretability in complex models. Future research directions are outlined, emphasizing the development of hybrid models, explainable AI systems, and real-time adaptability to dynamic grid conditions. This review provides a holistic understanding of state-of-the-art AI/ML methods in PQ analysis. It highlights their potential to transform modern power systems by ensuring higher reliability, better fault detection, and more efficient power delivery. © 2024 The Author(s)

Czech name

—

Czech description

—

Type

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

CEP classification

—

OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

<a href="/en/project/TN02000025" target="_blank" >TN02000025: National Centre for Energy II</a><br>

Continuities

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

Publication year

2025

Confidentiality

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

Name of the periodical

Results in Engineering

ISSN

2590-1230

e-ISSN

2590-1230

Volume of the periodical

25

Issue of the periodical within the volume

25

Country of publishing house

US - UNITED STATES

Number of pages

25

Pages from-to

nestránkováno

UT code for WoS article

001402475300001

EID of the result in the Scopus database

2-s2.0-85214023693