A comprehensive review of feature extraction techniques for power quality event detection with novel approaches for enhanced classification in smart grids
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10258001" target="_blank" >RIV/61989100:27240/25:10258001 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27730/25:10258001
Result on the web
<a href="https://link.springer.com/article/10.1007/s00202-025-03149-w" target="_blank" >https://link.springer.com/article/10.1007/s00202-025-03149-w</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00202-025-03149-w" target="_blank" >10.1007/s00202-025-03149-w</a>
Alternative languages
Result language
angličtina
Original language name
A comprehensive review of feature extraction techniques for power quality event detection with novel approaches for enhanced classification in smart grids
Original language description
The accurate detection and classification of power quality events (PQEs) are critical for ensuring the stability, reliability, and efficiency of modern power systems, especially within the evolving landscape of smart grids and microgrids. This paper presents a comprehensive review of state-of-the-art feature extraction techniques for PQE detection, focusing on their application to non-stationary power signals. We systematically analyze and categorize the most widely adopted signal processing techniques and soft computing methodologies, evaluating their effectiveness in addressing the complex challenges of power quality monitoring. The study highlights the strengths and limitations of various feature extraction approaches, ranging from time domain to frequency-domain and time–frequency domain methods, and critically examines their suitability for real-time PQE detection in smart grid environments. A key contribution of this work is the identification of a novel framework for feature selection, which optimizes classification performance while minimizing computational complexity. The paper discusses the inherent trade-offs between computational efficiency and classification accuracy, particularly in applications where real-time processing is crucial. In addition, we address common pitfalls in feature selection, such as the risk of misapplications that may result in erroneous decision making and propose strategies to mitigate these issues. The analysis further explores the gap between theoretical advancements in PQE detection methods and their practical implementation in real-world power systems, offering insights into the integration of advanced computational techniques with current power quality monitoring systems. Through this comprehensive review, we provide a detailed roadmap for researchers and practitioners seeking to enhance the accuracy and efficiency of PQE classification systems. Our findings not only propose novel insights into feature extraction techniques but also highlight key areas for future research, particularly in the context of smart grid applications. This paper lays the groundwork for the development of more robust, real-time PQE detection systems, offering significant implications for the ongoing evolution of power quality management in modern electrical grids. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - 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
Result continuities
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)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Electrical Engineering
ISSN
0948-7921
e-ISSN
1432-0487
Volume of the periodical
30.5.2025
Issue of the periodical within the volume
30 May 2025
Country of publishing house
US - UNITED STATES
Number of pages
33
Pages from-to
nestránkováno
UT code for WoS article
001499200400001
EID of the result in the Scopus database
2-s2.0-105006920319