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Automatic PRPD Image Recognition of Multiple Simultaneous Partial Discharge Sources in On-Line Hydro-Generator Stator Bars

Author

Listed:
  • Ramon C. F. Araújo

    (Electrical Engineering Graduate Program, Federal University of Pará, Belém 66075-110, Brazil)

  • Rodrigo M. S. de Oliveira

    (Electrical Engineering Graduate Program, Federal University of Pará, Belém 66075-110, Brazil)

  • Fabrício J. B. Barros

    (Electrical Engineering Graduate Program, Federal University of Pará, Belém 66075-110, Brazil)

Abstract

In this study, a methodology for automatic recognition of multiple simultaneous types of partial discharges (PDs) in hydro-generator stator windings was proposed. All the seven PD sources typical in rotating machines were considered, and up to three simultaneous sources could be identified. The functionality of identifying samples with no valid PDs was also incorporated using a new technique. The data set was composed of phase-resolved partial discharge (PRPD) patterns obtained from on-line measurements of hydro-generators. From an input PRPD, noise and interference were removed with an improved version of an image-based denoising algorithm previously proposed by the authors. Then, a novel image-based algorithm that separates partially superposed PD clouds was proposed, by decomposing the input pattern into two sub-PRPDs containing discharges of different natures. From the sub-PRPDs, one extracts features quantifying the PD distribution over amplitudes and the contour of PD clouds. Those features are fed as inputs to several artificial neural networks (ANNs), each of which solves a part of the classification problem and acts as a block of a larger system. Once trained, ANNs work collaboratively to identify an unknown sample. Good results were obtained, with overall accuracies ranging from 88% to 94.8% for all the considered PD sources.

Suggested Citation

  • Ramon C. F. Araújo & Rodrigo M. S. de Oliveira & Fabrício J. B. Barros, 2022. "Automatic PRPD Image Recognition of Multiple Simultaneous Partial Discharge Sources in On-Line Hydro-Generator Stator Bars," Energies, MDPI, vol. 15(1), pages 1-26, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:1:p:326-:d:717111
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    References listed on IDEAS

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    1. Ana C. N. Pardauil & Thiago P. Nascimento & Marcelo R. S. Siqueira & Ubiratan H. Bezerra & Werbeston D. Oliveira, 2020. "Combined Approach Using Clustering-Random Forest to Evaluate Partial Discharge Patterns in Hydro Generators," Energies, MDPI, vol. 13(22), pages 1-18, November.
    2. Marek Florkowski, 2020. "Classification of Partial Discharge Images Using Deep Convolutional Neural Networks," Energies, MDPI, vol. 13(20), pages 1-17, October.
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    Cited by:

    1. Gustavo de Oliveira Machado & Luciano Coutinho Gomes & Augusto Wohlgemuth Fleury Veloso da Silveira & Carlos Eduardo Tavares & Darizon Alves de Andrade, 2022. "Impacts of Harmonic Voltage Distortions on the Dynamic Behavior and the PRPD Patterns of Partial Discharges in an Air Cavity Inside a Solid Dielectric Material," Energies, MDPI, vol. 15(7), pages 1-20, April.

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