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Diagnosis of Static Eccentricity in 3-Phase Synchronous Machines using a Pseudo Zero-Sequence Current

Author

Listed:
  • Konstantinos N. Gyftakis

    (School of Engineering, The University of Edinburgh, Edinburgh EH9 3DW, UK)

  • Carlos A. Platero

    (Departamento de Ingeniería Electrica, Universidad Politecnica de Madrid, 28006 Madrid, Spain)

  • Yucheng Zhang

    (School of Computing, Electronics and Mathematics, Coventry University, Coventry CV1 2JH, UK)

  • Santiago Bernal

    (Departamento de Ingeniería Electrica, Universidad Politecnica de Madrid, 28006 Madrid, Spain)

Abstract

Large synchronous generators are the heart of the modern world, while producing the vast majority of the electric power consumed globally. Although they are robust devices, they are prone to degradation and failure. If such failures are not detected at an early stage, then the negative impact may be catastrophic in terms of financial costs, repair times, human lives and quality of life. This is the reason for continuous research in the field of condition monitoring aiming towards the reliable operation of synchronous generators. This paper proposes a novel technique for the diagnosis of the static eccentricity in synchronous generators. The proposed approach is off-line and non-intrusive, allowing the estimation of the fault severity with stator current measurements only. The performed work has been carried out with Finite Element Simulations and extensive experimental testing.

Suggested Citation

  • Konstantinos N. Gyftakis & Carlos A. Platero & Yucheng Zhang & Santiago Bernal, 2019. "Diagnosis of Static Eccentricity in 3-Phase Synchronous Machines using a Pseudo Zero-Sequence Current," Energies, MDPI, vol. 12(13), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2476-:d:243403
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    References listed on IDEAS

    as
    1. Xueping Xu & Qinkai Han & Fulei Chu, 2018. "Review of Electromagnetic Vibration in Electrical Machines," Energies, MDPI, vol. 11(7), pages 1-33, July.
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    Cited by:

    1. Tomas A. Garcia-Calva & Daniel Morinigo-Sotelo & Oscar Duque-Perez & Arturo Garcia-Perez & Rene de J. Romero-Troncoso, 2020. "Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults," Energies, MDPI, vol. 13(16), pages 1-12, August.
    2. Petr Kacor & Petr Bernat & Petr Moldrik, 2021. "Utilization of Two Sensors in Offline Diagnosis of Squirrel-Cage Rotors of Asynchronous Motors," Energies, MDPI, vol. 14(20), pages 1-23, October.

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