Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 1.102
JCR 5-Year IF: 0.734
Issues per year: 4
Current issue: May 2020
Next issue: Aug 2020
Avg review time: 71 days


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


TRAFFIC STATS

2,633,466 unique visits
669,784 downloads
Since November 1, 2009



No robots online now


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 20 (2020)
 
     »   Issue 2 / 2020
 
     »   Issue 1 / 2020
 
 
 Volume 19 (2019)
 
     »   Issue 4 / 2019
 
     »   Issue 3 / 2019
 
     »   Issue 2 / 2019
 
     »   Issue 1 / 2019
 
 
 Volume 18 (2018)
 
     »   Issue 4 / 2018
 
     »   Issue 3 / 2018
 
     »   Issue 2 / 2018
 
     »   Issue 1 / 2018
 
 
 Volume 17 (2017)
 
     »   Issue 4 / 2017
 
     »   Issue 3 / 2017
 
     »   Issue 2 / 2017
 
     »   Issue 1 / 2017
 
 
 Volume 16 (2016)
 
     »   Issue 4 / 2016
 
     »   Issue 3 / 2016
 
     »   Issue 2 / 2016
 
     »   Issue 1 / 2016
 
 
  View all issues  


FEATURED ARTICLE

Improved Wind Speed Prediction Using Empirical Mode Decomposition, ZHANG, Y., ZHANG, C., SUN, J., GUO, J.
Issue 2/2018

AbstractPlus


SAMPLE ARTICLES

The Passive Operating Mode of the Linear Optical Gesture Sensor, CZUSZYNSKI, K., RUMINSKI, J., WTOREK, J.
Issue 1/2018

AbstractPlus

High-Level Crosstalk Model in N-Coupled Through-Silicon Vias (TSVs), LEE, H., PARK, J. K., KIM, J. T.
Issue 3/2018

AbstractPlus

FEM Based Multi-Criterion Design and Implementation of a PM Synchronous Wind Generator by Fully Coupled Co-Simulation, OCAK, C., UYGUN, D., TARIMER, I.
Issue 1/2018

AbstractPlus

High-Level Crosstalk Model in N-Coupled Through-Silicon Vias (TSVs), LEE, H., PARK, J. K., KIM, J. T.
Issue 3/2018

AbstractPlus

Boost Converter with Active Snubber Network, HIMMELSTOSS, F. A., DERIN, A. R., CERNAT, M.
Issue 1/2017

AbstractPlus

A Fuzzy AHP Approach for Security Risk Assessment in SCADA Networks, MARKOVIC-PETROVIC, J. D., STOJANOVIC, M. D., BOSTJANCIC RAKAS, S. V.
Issue 3/2019

AbstractPlus




LATEST NEWS

2020-Jun-29
Clarivate Analytics published the InCites Journal Citations Report for 2019. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.102 (1.023 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.734.

2020-Jun-11
Starting on the 15th of June 2020 we wiil introduce a new policy for reviewers. Reviewers who provide timely and substantial comments will receive a discount voucher entitling them to an APC reduction. Vouchers (worth of 25 EUR or 50 EUR, depending on the review quality) will be assigned to reviewers after the final decision of the reviewed paper is given. Vouchers issued to specific individuals are not transferable.

2019-Dec-16
Starting on the 15th of December 2019 all paper authors are required to enter their SCOPUS IDs. You may use the free SCOPUS ID lookup form to find yours in case you don't remember it.

2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

2018-May-31
Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

Read More »


    
 

  3/2016 - 4

Thermal Response Estimation in Substation Connectors Using Data-Driven Models

GIACOMETTO, F. See more information about GIACOMETTO, F. on SCOPUS See more information about GIACOMETTO, F. on IEEExplore See more information about GIACOMETTO, F. on Web of Science, CAPELLI, F. See more information about  CAPELLI, F. on SCOPUS See more information about  CAPELLI, F. on SCOPUS See more information about CAPELLI, F. on Web of Science, ROMERAL, L. See more information about  ROMERAL, L. on SCOPUS See more information about  ROMERAL, L. on SCOPUS See more information about ROMERAL, L. on Web of Science, RIBA, J.-R. See more information about  RIBA, J.-R. on SCOPUS See more information about  RIBA, J.-R. on SCOPUS See more information about RIBA, J.-R. on Web of Science, SALA, E. See more information about SALA, E. on SCOPUS See more information about SALA, E. on SCOPUS See more information about SALA, E. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,247 KB) | Citation | Downloads: 391 | Views: 1,602

Author keywords
computer simulation, connectors, finite element methods, predictive models, thermal analysis

References keywords
engineer(8), comput(7), neural(6), jcie(6), indust(6), simulation(5), process(5), finite(5), element(5), time(4)
No common words between the references section and the paper title.

About this article
Date of Publication: 2016-08-31
Volume 16, Issue 3, Year 2016, On page(s): 25 - 30
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.03004
Web of Science Accession Number: 000384750000004
SCOPUS ID: 84991096061

Abstract
Quick view
Full text preview
Temperature rise simulations are one of the key steps in the design of high-voltage substation connectors. These simulations help minimizing the number of experimental tests, which are power consuming and expensive. The conventional approach to perform these simulations relies on finite element method (FEM). It is highly desirable to reduce the number of required FEM simulations since they are time-consuming. To this end, this paper presents a data-driven modeling approach to drastically shorten the required simulation time. The data-driven approach estimates the thermal response of substation connectors from the data provided by a reduced number of FEM simulations of different operating conditions, thus allowing extrapolating the thermal response to other operating conditions. In the study, a partitioning method is also applied to enhance the performance of the learning stage of a set of data-driven methods, which are then compared and evaluated in terms of simulation time and accuracy to select the optimal configuration of the data-driven model. Finally, the complete methodology is validated against simulation tests.


References | Cited By  «-- Click to see who has cited this paper

[1] G. Mazzanti, "The Combination of Electro-thermal Stress, Load Cycling and Thermal Transients and its Effects on the Life of High Voltage ac Cables", IEEE Trans. Diel. Electr. Insul., vol. 16, pp. 1168-1179, 2009,
[CrossRef] [Web of Science Times Cited 61]


[2] National electrical Manufacturers Association, "ANSI/NEMA CC 1-2009 Electric Power Connection for Substations Standart", NEMA Communications Department, Arlington, Virginia, 2009

[3] J. J. A. Wang, E. Lara-Curzio, T. King, J. A. Graziano, and J. K. Chan, "The integrity of ACSR full tension splice connector at higher operation temperature", IEEE Trans. Power Deliv., vol. 23, pp. 1158-1165, 2008,
[CrossRef] [Web of Science Times Cited 10]


[4] J. Hernandez-Guiteras, J. R. Riba, and L. Romeral, "Redesign process of a 765 kVRMS AC substation connector by means of 3D-FEM simulations", Sim. Model. Pract. Theory, vol. 42, pp. 1-11, 2014,
[CrossRef] [Web of Science Times Cited 12]


[5] F. Capelli, J. R. Riba, and D. Gonzalez, "Optimization of short-circuit tests based on finite element analysis", in IEEE International Conference on Industrial Technology (ICIT), pp. 1368-1374, 2015,
[CrossRef]


[6] S. Jia, J. F. Bard, R. Chacon, and J. Stuber, "Improving performance of dispatch rules for daily scheduling of assembly and test operations", Comput. Indust. Engineer., vol. 90, pp. 86-106, 2015,
[CrossRef] [Web of Science Times Cited 8]


[7] U. Roy, "An intelligent interface between symbolic and numeric analysis tools required for the development of an integrated CAD system", Comput. Indust. Engineer., vol. 30, pp. 13-26, 1996,
[CrossRef] [Web of Science Times Cited 5]


[8] F. Tian and M. Voskuijl, "Automated generation of multiphysics simulation models to support multidisciplinary design optimization", Advan. Engineer. Informat., 2005,
[CrossRef] [Web of Science Times Cited 8]


[9] T. Altan and V. Vazquez, "Numerical Process Simulation for Tool and Process Design in Bulk Metal Forming", CIRP Annals - Manuf.. Technol., vol. 45, pp. 599-615, 1996,
[CrossRef]


[10] S. Cho, "A distributed time-driven simulation method for enabling real-time manufacturing shop floor control", Comput. Indust. Engineer., vol. 49, pp. 572-590, 2005,
[CrossRef] [Web of Science Times Cited 14]


[11] Y. Zhang, Z.-P. Fan, and Y. Liu, "A method based on stochastic dominance degrees for stochastic multiple criteria decision making", Comput. & Indust. Engineer., vol. 58, pp. 544-552, 2010,
[CrossRef] [Web of Science Times Cited 49]


[12] Z. Lou and H. M. Jin, "A novel dual-field time-domain finite-element domain-decomposition method for computational electromagnetics", IEEE Trans. Antennas Propagat., vol. 54, pp. 1850-1862, 2006,
[CrossRef] [Web of Science Times Cited 41]


[13] M. Nesme, F. Faure, and Y. Payan, "Hierarchical multi-resolution finite element model for soft body simulation", Biomedical Simulation, Proceedings, vol. 4072, pp. 40-47, 2006,
[CrossRef]


[14] U. K. Malte Neumann, S. R. Tiyyagura, W. A. Wall, and E. Ramm, "High Performance Computing on Vector Systems: Computational Efficiency of Parallel Unstructured Finite Element Simulations" , pp. 89-107, Springer-Verlag, 2006

[15] M. Behr and T. E. Tezduyar, "Finite-Element Solution Strategies for Large-Scale Flow Simulations", Comput. Meth. Appl. Mech. Engineer., vol. 112, pp. 3-24, Feb 1994,
[CrossRef] [Web of Science Times Cited 104]


[16] C. Giannetti, R. S. Ransing, M. R. Ransing, D. C. Bould, D. T. Gethin, and J. Sienz, "A novel variable selection approach based on co-linearity index to discover optimal process settings by analysing mixed data", Comput. Indust. Engineer., vol. 72, pp. 217-229, 2014,
[CrossRef] [Web of Science Times Cited 9]


[17] S. Ferreiro, B. Sierra, I. Irigoien, and E. Gorritxategi, "Data mining for quality control: Burr detection in the drilling process", Computers & Industrial Engineering, vol. 60, pp. 801-810, May 2011,
[CrossRef] [Web of Science Times Cited 23]


[18] M. Luo, H.-C. Yan, B. Hu, J.-H. Zhou, and C. K. Pang, "A data-driven two-stage maintenance framework for degradation prediction in semiconductor manufacturing industries", Comput. Indust. Engineer., vol. 85, pp. 414-422, July 2015,
[CrossRef] [Web of Science Times Cited 16]


[19] B. Trawinski, M. Smetek, Z. Telec, and T. Lasota, "Nonparametric Statistical Analysis for Multiple Comparison of Machine Learning Regression Algorithms", Int. J. of Appl. Math. and Comp. Sci., vol. 22, pp. 867-881, 2012,
[CrossRef] [Web of Science Times Cited 69]


[20] J. Luengo, S. Garcia, and F. Herrera, "A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests", Exp. Syst. Appl., vol. 36, pp. 7798-7808, 2009,
[CrossRef] [Web of Science Times Cited 81]


[21] E. Levin, "A Recurrent Neural Network - Limitations and Training", Proceedings of the 22nd Conference on Information Sciences and Systems, vol. 1-2, pp. 296-301, 1988,
[CrossRef] [Web of Science Times Cited 11]


[22] H. Cruse, "Neural Networks as Cybernetic Systems", pp. 89-99, Brains, Minds & Media, 2009.

[23] R. Rojas, "Neural networks: a systematic introduction", pp. 336-348, Springer-verlag, 1996.

[24] T. Takagi and M. Sugeno, "Fuzzy Identification of Systems and Its Applications to Modeling and Control", IEEE Trans. Sys. Man Cybern., vol. 15, pp. 116-132, 1985,
[CrossRef] [Web of Science Times Cited 11339]


[25] J. S. R. Jang, "Anfis - Adaptive-Network-Based Fuzzy Inference System", IEEE Trans. Sys. Man Cybern., vol. 23, pp. 665-685, 1993,
[CrossRef] [Web of Science Times Cited 8187]


[26] F. Wong, "Time Series Forecasting Using Back-Propagation Neural Networks", Neurocomputing, Vol 2, no. 4, 1991, pp. 147-159,
[CrossRef]


[27] R. L. MD Richard, "Neural network classifiers estimate Bayesian a posteriori probabilities", IEEE 4 (2) ASSP Magazine, pp. 4-22, 1987,
[CrossRef] [Web of Science Times Cited 564]


[28] F. Giacometto, E. Sala, K. Kampouropoulos and L. Romeral, "Short-term load forecasting using Cartesian Genetic Programming: An efficient evolutive strategy: Case: Australian electricity market", in IEEE Annual Conference on Industrial Electronics (IECON), pp. 5087-5094, 2015,
[CrossRef]




References Weight

Web of Science® Citations for all references: 20,611 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 711 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2020-07-28 04:15 in 158 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2020
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.




Website loading speed and performance optimization powered by: