|3/2016 - 4|
Thermal Response Estimation in Substation Connectors Using Data-Driven ModelsGIACOMETTO, F. , CAPELLI, F. , ROMERAL, L. , RIBA, J.-R. , SALA, E.
|View the paper record and citations in|
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,247 KB) | Citation | Downloads: 632 | Views: 2,015|
computer simulation, connectors, finite element methods, predictive models, thermal analysis
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
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|
| 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 72] [SCOPUS Times Cited 107]
 National electrical Manufacturers Association, "ANSI/NEMA CC 1-2009 Electric Power Connection for Substations Standart", NEMA Communications Department, Arlington, Virginia, 2009
 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 12] [SCOPUS Times Cited 12]
 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 13] [SCOPUS Times Cited 14]
 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] [SCOPUS Times Cited 4]
 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] [SCOPUS Times Cited 9]
 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] [SCOPUS Times Cited 7]
 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 12] [SCOPUS Times Cited 19]
 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] [SCOPUS Times Cited 63]
 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] [SCOPUS Times Cited 17]
 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 55] [SCOPUS Times Cited 62]
 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 43] [SCOPUS Times Cited 53]
 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] [SCOPUS Times Cited 13]
 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
 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 110] [SCOPUS Times Cited 134]
 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 10] [SCOPUS Times Cited 13]
 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 28] [SCOPUS Times Cited 40]
 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 24] [SCOPUS Times Cited 28]
 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 96] [SCOPUS Times Cited 113]
 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 90] [SCOPUS Times Cited 106]
 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 13] [SCOPUS Times Cited 15]
 H. Cruse, "Neural Networks as Cybernetic Systems", pp. 89-99, Brains, Minds & Media, 2009.
 R. Rojas, "Neural networks: a systematic introduction", pp. 336-348, Springer-verlag, 1996.
 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 12574] [SCOPUS Times Cited 15788]
 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 9621] [SCOPUS Times Cited 12246]
 F. Wong, "Time Series Forecasting Using Back-Propagation Neural Networks", Neurocomputing, Vol 2, no. 4, 1991, pp. 147-159,
[CrossRef] [SCOPUS Times Cited 122]
 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 612]
 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] [SCOPUS Times Cited 4]
Web of Science® Citations for all references: 23,412 TCR
SCOPUS® Citations for all references: 28,989 TCR
Web of Science® Average Citations per reference: 807 ACR
SCOPUS® Average Citations per reference: 1,000 ACR
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 2022-05-23 10:27 in 147 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.
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.