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Combined Duval Pentagons: A Simplified Approach

Author

Listed:
  • Luiz Cheim

    (Transformer Technology Center, ABB Power Grids, St. Louis, MO 63127, USA)

  • Michel Duval

    (Institut de Recherche d’Hydro-Québec (IREQ), Varennes, QC J3X 1S1, Canada)

  • Saad Haider

    (Transformer Technology Center, ABB Power Grids, St. Louis, MO 63127, USA)

Abstract

The paper describes a newly proposed combination of the two existing Duval Pentagons method utilized for the identification of mineral oil-insulated transformers. The aim of the combination is to facilitate automatic fault identification through computer programs, and at the same time, apply the full capability of both original Pentagons, now reduced to a single geometry. The thorough classification of a given fault (say, of the electrical or thermal kind), employing individual Pentagons 1 and 2, as originally defined, involves a complex geometrical problem that requires the build-up of a convoluted geometry (a regular Pentagon whose axes represent each of five possible combustible gases) to be constructed using computer language code and programming, followed by the logical localization of the geometrical centroid of an irregular pentagon, formed by the partial contribution of individual combustibles, inside two similar structures (Pentagons 1 and 2) that, nonetheless, have different classification zones and boundaries, as more thoroughly explained and exemplified in the main body of this article. The proposed combined approach results in a lower number of total fault zones (10 in the combined Pentagons against 14 when considering Pentagons 1 and 2 separately, although zones PD, S, D1 and D2 are common to both Pentagons 1 and 2), and therefore eliminates the need to solve for two separate Pentagons.

Suggested Citation

  • Luiz Cheim & Michel Duval & Saad Haider, 2020. "Combined Duval Pentagons: A Simplified Approach," Energies, MDPI, vol. 13(11), pages 1-12, June.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2859-:d:367044
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    Citations

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    Cited by:

    1. Ancuța-Mihaela Aciu & Claudiu-Ionel Nicola & Marcel Nicola & Maria-Cristina Nițu, 2021. "Complementary Analysis for DGA Based on Duval Methods and Furan Compounds Using Artificial Neural Networks," Energies, MDPI, vol. 14(3), pages 1-22, January.
    2. Michel Duval & Constantin Ene, 2021. "Identification of Stray Gassing of Dodecylbenzene in Bushings," Energies, MDPI, vol. 14(9), pages 1-7, April.
    3. George Kimani Irungu & Aloys Oriedi Akumu, 2020. "Application of Dissolved Gas Analysis in Assessing Degree of Healthiness or Faultiness with Fault Identification in Oil-Immersed Equipment," Energies, MDPI, vol. 13(18), pages 1-24, September.
    4. Tomasz Piotrowski & Pawel Rozga & Ryszard Kozak & Zbigniew Szymanski, 2020. "Using the Analysis of the Gases Dissolved in Oil in Diagnosis of Transformer Bushings with Paper-Oil Insulation—A Case Study," Energies, MDPI, vol. 13(24), pages 1-12, December.
    5. Firas B. Ismail & Maisarah Mazwan & Hussein Al-Faiz & Marayati Marsadek & Hasril Hasini & Ammar Al-Bazi & Young Zaidey Yang Ghazali, 2022. "An Offline and Online Approach to the OLTC Condition Monitoring: A Review," Energies, MDPI, vol. 15(17), pages 1-18, September.
    6. Youcef Benmahamed & Omar Kherif & Madjid Teguar & Ahmed Boubakeur & Sherif S. M. Ghoneim, 2021. "Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier," Energies, MDPI, vol. 14(10), pages 1-17, May.
    7. El-Sayed M. El-kenawy & Fahad Albalawi & Sayed A. Ward & Sherif S. M. Ghoneim & Marwa M. Eid & Abdelaziz A. Abdelhamid & Nadjem Bailek & Abdelhameed Ibrahim, 2022. "Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-28, September.
    8. James Dukarm & Zachary Draper & Tomasz Piotrowski, 2020. "Diagnostic Simplexes for Dissolved-Gas Analysis," Energies, MDPI, vol. 13(23), pages 1-16, December.

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