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A Study of Fault Diagnosis Based on Electrical Signature Analysis for Synchronous Generators Predictive Maintenance in Bulk Electric Systems

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  • Camila Paes Salomon

    (Institute of Electric Systems and Energy, Itajuba Federal University, Itajuba 37500-903, Brazil)

  • Claudio Ferreira

    (Institute of Electric Systems and Energy, Itajuba Federal University, Itajuba 37500-903, Brazil)

  • Wilson Cesar Sant’Ana

    (Gnarus Institute, Itajuba 37500-052, Brazil)

  • Germano Lambert-Torres

    (Gnarus Institute, Itajuba 37500-052, Brazil)

  • Luiz Eduardo Borges da Silva

    (Institute of System Engineering and Information Technology, Itajuba Federal University, Itajuba 37500-903, Brazil)

  • Erik Leandro Bonaldi

    (Gnarus Institute, Itajuba 37500-052, Brazil)

  • Levy Ely de Lacerda de Oliveira

    (Gnarus Institute, Itajuba 37500-052, Brazil)

  • Bruno Silva Torres

    (Institute of Electric Systems and Energy, Itajuba Federal University, Itajuba 37500-903, Brazil)

Abstract

The condition of synchronous generators (SGs) is a matter of great attention, because they can be seen as equipment and also as fundamental elements of power systems. Thus, there is a growing interest in new technologies to improve SG protection and maintenance schemes. In this context, electrical signature analysis (ESA) is a non-invasive technique that has been increasingly applied to the predictive maintenance of rotating electrical machines. However, in general, the works applying ESA to SGs are focused on isolated machines. Thus, this paper presents a study on the condition monitoring of SGs in bulk electric systems by using ESA. The main contribution of this work is the practical results of ESA for fault detection in in-service SGs interconnected to a power system. Two types of faults were detected in an SG at a Brazilian hydroelectric power plant by using ESA, including stator electrical unbalance and mechanical misalignment. This paper also addresses peculiarities in the ESA of wound rotor SGs, including recommendations for signal analysis, how to discriminate rotor faults on fault patterns, and the particularities of two-pole SGs.

Suggested Citation

  • Camila Paes Salomon & Claudio Ferreira & Wilson Cesar Sant’Ana & Germano Lambert-Torres & Luiz Eduardo Borges da Silva & Erik Leandro Bonaldi & Levy Ely de Lacerda de Oliveira & Bruno Silva Torres, 2019. "A Study of Fault Diagnosis Based on Electrical Signature Analysis for Synchronous Generators Predictive Maintenance in Bulk Electric Systems," Energies, MDPI, vol. 12(8), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1506-:d:224750
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    References listed on IDEAS

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    1. Yuri Merizalde & Luis Hernández-Callejo & Oscar Duque-Perez, 2017. "State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors," Energies, MDPI, vol. 10(7), pages 1-34, July.
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    Cited by:

    1. Fernanda Mitchelly Vilas Boas & Luiz Eduardo Borges-da-Silva & Helcio Francisco Villa-Nova & Erik Leandro Bonaldi & Levy Ely Lacerda Oliveira & Germano Lambert-Torres & Frederico de Oliveira Assuncao , 2021. "Condition Monitoring of Internal Combustion Engines in Thermal Power Plants Based on Control Charts and Adapted Nelson Rules," Energies, MDPI, vol. 14(16), pages 1-17, August.
    2. Isac Antônio dos Santos Areias & Luiz Eduardo Borges da Silva & Erik Leandro Bonaldi & Levy Ely de Lacerda de Oliveira & Germano Lambert-Torres & Vitor Almeida Bernardes, 2019. "Evaluation of Current Signature in Bearing Defects by Envelope Analysis of the Vibration in Induction Motors," Energies, MDPI, vol. 12(21), pages 1-15, October.
    3. Frederico de Oliveira Assuncao & Luiz Eduardo Borges-da-Silva & Helcio Francisco Villa-Nova & Erik Leandro Bonaldi & Levy Ely Lacerda Oliveira & Germano Lambert-Torres & Carlos Eduardo Teixeira & Wils, 2021. "Reduced Scale Laboratory for Training and Research in Condition-Based Maintenance Strategies for Combustion Engine Power Plants and a Novel Method for Monitoring of Inlet and Exhaust Valves," Energies, MDPI, vol. 14(19), pages 1-23, October.
    4. Jing Tang & Yongheng Yang & Jie Chen & Ruichang Qiu & Zhigang Liu, 2019. "Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection," Energies, MDPI, vol. 13(1), pages 1-17, December.
    5. Akilu Yunusa-Kaltungo & Ruifeng Cao, 2020. "Towards Developing an Automated Faults Characterisation Framework for Rotating Machines. Part 1: Rotor-Related Faults," Energies, MDPI, vol. 13(6), pages 1-20, March.
    6. Sandra Eriksson, 2019. "Permanent Magnet Synchronous Machines," Energies, MDPI, vol. 12(14), pages 1-5, July.
    7. Rodrigo Barbosa de Santis & Marcelo Azevedo Costa, 2020. "Extended Isolation Forests for Fault Detection in Small Hydroelectric Plants," Sustainability, MDPI, vol. 12(16), pages 1-16, August.
    8. N. I. Koteleva & N. A. Korolev & Y. L. Zhukovskiy, 2021. "Identification of the Technical Condition of Induction Motor Groups by the Total Energy Flow," Energies, MDPI, vol. 14(20), pages 1-23, October.
    9. Nikolay Korolev & Anatoly Kozyaruk & Valentin Morenov, 2021. "Efficiency Increase of Energy Systems in Oil and Gas Industry by Evaluation of Electric Drive Lifecycle," Energies, MDPI, vol. 14(19), pages 1-15, September.
    10. Barbosa de Santis, Rodrigo & Silveira Gontijo, Tiago & Azevedo Costa, Marcelo, 2021. "Condition-based maintenance in hydroelectric plants: A systematic literature review," MPRA Paper 115912, University Library of Munich, Germany.

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