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FACTS & FIGURES

JCR Impact Factor: 0.700
JCR 5-Year IF: 0.700
SCOPUS CiteScore: 2.0
Issues per year: 3
Current issue: Jun 2025
Next issue: Nov 2025
Avg review time: 88 days
Avg accept to publ: 60 days
APC: 300 EUR


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2025-Jun-19
Clarivate Analytics published the InCites Journal Citations Report for 2024. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.600 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2025-May-01
Starting from 2025, our Journal will appear 3 times a year. Issues will be published in February, June, and October.

2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.600 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

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  2/2025 - 3

Investigating the Operational Limits of a Horizontal Axis Wind Turbine Gearbox Through Unsupervised Comparison Between Units of the Same Equipment from Three Nearby Power Plants

AVILA, S. L. See more information about AVILA, S. L. on SCOPUS See more information about AVILA, S. L. on IEEExplore See more information about AVILA, S. L. on Web of Science, VELLOSO, B. P. See more information about  VELLOSO, B. P. on SCOPUS See more information about  VELLOSO, B. P. on SCOPUS See more information about VELLOSO, B. P. on Web of Science, CARDOZO, G. See more information about  CARDOZO, G. on SCOPUS See more information about  CARDOZO, G. on SCOPUS See more information about CARDOZO, G. on Web of Science, MATSUO, T. K. See more information about MATSUO, T. K. on SCOPUS See more information about MATSUO, T. K. on SCOPUS See more information about MATSUO, T. K. on Web of Science
 
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Download PDF pdficon (1,409 KB) | Citation | Downloads: 3 | Views: 10

Author keywords
correlation, turbines, vibrations, wind energy generation, wind farms

References keywords
wind(32), turbine(14), review(12), energy(12), turbines(11), access(10), vibration(8), renewable(7), data(7), maintenance(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2025-06-30
Volume 25, Issue 2, Year 2025, On page(s): 19 - 26
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2025.02003

Abstract
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Considering the operating limits proposed by ISO 10816-21 Evaluation of Machine Vibration by Measurements on Non-Rotating Parts for horizontal-axis wind turbine gearboxes, we analyzed the behavior of nearly one hundred gearboxes from three nearby onshore wind farms (~10 sq km) in northeastern Brazil. Each wind turbine is equipped with an identical mechanical vibration monitoring system, comprising ten sensors and nine features per sensor. First, we assessed whether the equipment operated within the ISO-defined limits. Next, we confirmed the trends detected by the ten gearbox sensors exhibited a strong correlation with one another. However, trends among similar pieces of equipment operating under the same conditions were not strongly correlated. An unsupervised correlation analysis using the Fast Fourier Transform (FFT) was conducted for all wind turbines, considering the zone boundary values proposed by ISO. The unsupervised correlation analysis enhances knowledge for more targeted monitoring, achieving an accuracy score exceeding 70%. This approach contributes to the development of a more effective predictive maintenance program.


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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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