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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27730/25:10256781

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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)

Název v anglickém jazyce

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

Popis výsledku anglicky

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)

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

<a href="/cs/project/TN02000025" target="_blank" >TN02000025: Národní centrum pro energetiku II</a><br>

Návaznosti

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

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Results in Engineering

ISSN

2590-1230

e-ISSN

2590-1230

Svazek periodika

25

Číslo periodika v rámci svazku

25

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

25

Strana od-do

nestránkováno

Kód UT WoS článku

001402475300001

EID výsledku v databázi Scopus

2-s2.0-85214023693