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Experimental Evaluation of Magnetostrictive Strain of Electrical Steel

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Abstract

Environmental noise pollution has gained increasing importance, over the past few years. Due to population growth along with a rapid urbanization and the increasing power supply needs, more and more electrical power transformers are set near or inside of urban agglomerations. This fact has generated several complaints regarding the noise produced by this equipment, forcing manufacturers to develop low noise solutions. As it is known, magnetostriction is one of the main sources of electrical machines noise. This research presents an experimental study in which magnetostriction properties of electrical steel are evaluated and analyzed. The magnetic flux density influence on the hysteretic strain behavior of magnetostriction was addressed, as well as the effect of a clamping load on the core joints. This study was addressed by means of an Epstein frame and a data acquisition system, where strain, current and voltage data is obtained and then processed in a data logging software. These measurements gave essential inputs for numerical models which simulate the power transformer core behavior, allowing a faster evaluation of noise mitigation solutions.

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Acknowledgements

This work is supported by the project POCI-01-0247-FEDER-024035, with the acronym Quiet Transformer 2, co-funded by the European Regional Development Fund (ERDF) through COMPETE2020 - Programa Operacional de Competitividade e Internacionalização (POCI) under the “Portugal 2020” Programme.

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Correspondence to João Espírito Santo .

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Vieira, A. et al. (2019). Experimental Evaluation of Magnetostrictive Strain of Electrical Steel. In: Afonso, J., Monteiro, V., Pinto, J. (eds) Green Energy and Networking. GreeNets 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 269. Springer, Cham. https://doi.org/10.1007/978-3-030-12950-7_11

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  • DOI: https://doi.org/10.1007/978-3-030-12950-7_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12949-1

  • Online ISBN: 978-3-030-12950-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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