Islanding Detection and Power Quality Diagnosis of Wind Power Integrated Microgrid with Reduced Feature Trained Novel Optimized Random Decision Forest
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10255313" target="_blank" >RIV/61989100:27240/24:10255313 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27730/24:10255313
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/10.1155/2024/5198814" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1155/2024/5198814</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1155/2024/5198814" target="_blank" >10.1155/2024/5198814</a>
Alternative languages
Result language
angličtina
Original language name
Islanding Detection and Power Quality Diagnosis of Wind Power Integrated Microgrid with Reduced Feature Trained Novel Optimized Random Decision Forest
Original language description
Distributed generations (DGs) have been increasingly addressing the ongoing power deficit in the electricity market. However, a significant concern in DG-integrated microgrids is the detection of accidental islanding. To tackle this issue, this article proposes a cost-friendly, novel data-driven passive islanding detection scheme named EEMD-HOBRC, combining noise-assisted ensemble empirical mode decomposition (EEMD) and a hybrid optimization-based random forest classifier (HOBRFC). The detection scheme employs a diverse set of features extracted from both raw and EEMD decomposed signals. Essential features are selected using the binary grey wolf optimizer (BGWO) to reduce computational burden. To further improve classification accuracy, the parameters of the random forest classifier are optimized through a hybrid particle swarm and reformed grey wolf optimization (PSRGWO) technique with Cohen's kappa index as the cost function. The proposed technique is rigorously validated in two different multi-DG environments, encompassing islanding and various nonislanding events. The results demonstrate the effectiveness of the approach in terms of enhanced accuracy, detection time, and performance under both noisy and noise-free conditions. The accuracy of detection under ideal and high noise scenarios is found to be 99.88% and 99.2%, respectively, with maximum detection time of 34.27 ms. Comparative analysis with other algorithms also supports the superiority of the proposed technique. Finally, the method is successfully applied to shrink the nondetection zone (NDZ) with minimal power mismatch, further enhancing its utility in practical applications.
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
International Journal of Energy Research
ISSN
0363-907X
e-ISSN
1099-114X
Volume of the periodical
2024
Issue of the periodical within the volume
Volume 2024
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
nestránkováno
UT code for WoS article
001198001400001
EID of the result in the Scopus database
2-s2.0-85189949353