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A genetic algorithm optimized fractal model to predict the constriction resistance from surface roughness measurements

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1_Manuscript_Fractal_R_contact_2017_03_23.pdf (517,0Kb)
 
10.1109/TIM.2017.2707938
 
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hdl:2117/107586

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Capelli, Francesca
Riba Ruiz, Jordi-RogerMés informacióMés informacióMés informació
Rupérez de Gracia, ElisaMés informacióMés informacióMés informació
Sanllehí, Josep
Document typeArticle
Defense date2017-06-09
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
The electrical contact resistance greatly influences the thermal behavior of substation connectors and other electrical equipment. During the design stage of such electrical devices, it is essential to accurately predict the contact resistance to achieve an optimal thermal behavior, thus ensuring contact stability and extended service life. This paper develops a genetic algorithm (GA) approach to determine the optimal values of the parameters of a fractal model of rough surfaces to accurately predict the measured value of the surface roughness. This GA-optimized fractal model provides an accurate prediction of the contact resistance when the electrical and mechanical properties of the contacting materials, surface roughness, contact pressure, and apparent area of contact are known. Experimental results corroborate the usefulness and accuracy of the proposed approach. Although the proposed model has been validated for substation connectors, it can also be applied in the design stage of many other electrical equipments.
CitationCapelli, F., Riba, J., Rupérez de Gracia, E., Sanllehí, J. A genetic algorithm optimized fractal model to predict the constriction resistance from surface roughness measurements. "IEEE transactions on instrumentation and measurement", Vol. 66, núm. 9, 9 Juny 2017, p. 2437-2447. 
URIhttp://hdl.handle.net/2117/107586
DOI10.1109/TIM.2017.2707938
ISSN0018-9456
Publisher versionhttp://ieeexplore.ieee.org/document/7945532/
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