Wavelet-Based ensembled intelligent technique for a better quality of fault detection and classification in AC microgrids
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256189" target="_blank" >RIV/61989100:27240/24:10256189 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27730/24:10256189
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2590174524002915" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2590174524002915</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ecmx.2024.100813" target="_blank" >10.1016/j.ecmx.2024.100813</a>
Alternative languages
Result language
angličtina
Original language name
Wavelet-Based ensembled intelligent technique for a better quality of fault detection and classification in AC microgrids
Original language description
This research introduces a novel intelligent protection technique for microgrids, leveraging Discrete Wavelet Transform (DWT) and an Ensemble Bagged Decision Tree (EBDT) classifier for precise fault detection and classification across various scenarios. Non-stationary voltage and current signals are analysed using DWT to extract wavelet detailed coefficients, which are then used to compute the energy of these coefficients as input features for the EBDT. The hyperparameters of the EBDT are optimized using a random search algorithm to enhance robustness in fault classification. The proposed protection scheme's efficacy is validated on a modified IEC test microgrid model, incorporating both inverter-interfaced Distributed Generators (DGs) and synchronous DGs, under different fault conditions simulated in the MATLAB/SIMULINK environment. Results demonstrate that the method is both fast and accurate, achieving fault detection and classification accuracies of 100% in both grid-connected and islanded modes of operation. The performance of the proposed model is benchmarked against state-of-the-art methods, including Decision Tree and Random Forest classifiers. Additionally, the robustness of the proposed technique is confirmed under conditions of DG uncertainty and in the presence of noise. The effectiveness of the technique is assured across various fault conditions, and it is further validated in an OPAL-RT real-time environment. This study significantly enhances the reliability and resilience of microgrid protection systems, offering a robust solution for real-time fault management, which is crucial for the stability of modern power networks integrating renewable energy sources.
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
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Energy Conversion and Management-X
ISSN
2590-1745
e-ISSN
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Volume of the periodical
24
Issue of the periodical within the volume
24
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
1-16
UT code for WoS article
001373449900001
EID of the result in the Scopus database
2-s2.0-85211063195