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Impacts of Atmospheric and Load Conditions on the Power Substation Equipment Temperature Model

Author

Listed:
  • Osni Silva Junior

    (Graduate Program in Energy Systems, Universidade Tecnológica Federal do Paraná, Avenue Sete de Setembro 3165, Curitiba 80230-901, PR, Brazil)

  • Jose Carlos Pereira Coninck

    (Academic Department of Statistics, Universidade Tecnológica Federal do Paraná, Avenue Sete de Setembro 3165, Curitiba 80230-901, PR, Brazil)

  • Fabiano Gustavo Silveira Magrin

    (Graduate Program in Energy Systems, Universidade Tecnológica Federal do Paraná, Avenue Sete de Setembro 3165, Curitiba 80230-901, PR, Brazil
    Academic Department of Electrotechnics, Universidade Tecnológica Federal do Paraná, Avenue Sete de Setembro 3165, Curitiba 80230-901, PR, Brazil)

  • Francisco Itamarati Secolo Ganacim

    (Academic Department of Mathematics, Universidade Tecnológica Federal do Paraná, Avenue Sete de Setembro 3165, Curitiba 80230-901, PR, Brazil)

  • Anselmo Pombeiro

    (Operation and Maintenance Engineering Superintendence, Copel, Street José Izidoro Biazetto 158, Curitiba 81200-240, PR, Brazil)

  • Leonardo Göbel Fernandes

    (Graduate Program in Electrical and Computer Engineering, Universidade Tecnológica Federal do Paraná, Avenue Sete de Setembro 3165, Curitiba 80230-901, PR, Brazil)

  • Eduardo Félix Ribeiro Romaneli

    (Graduate Program in Energy Systems, Universidade Tecnológica Federal do Paraná, Avenue Sete de Setembro 3165, Curitiba 80230-901, PR, Brazil
    Academic Department of Electrotechnics, Universidade Tecnológica Federal do Paraná, Avenue Sete de Setembro 3165, Curitiba 80230-901, PR, Brazil)

Abstract

Infrared thermography is a predictive maintenance tool used in substations to identify a disturbance in electrical equipment that could lead to poor operation and potential failure in the future. According to Joule’s law, the temperature of electrical equipment is proportional to the current flowing through it. Other external factors, such as solar incidence, air humidity, wind speed, and air temperature, can interfere with its operating temperatures. Based on this premise, this article aims to analyze the influence of atmospheric and load conditions on the operational cycle of thermography-monitored equipment in order to describe the operating temperature of the object using only external data and to show the impacts of external influences on the final temperature reached by the object. Five multivariate time series regression models were developed to describe the maximum equipment temperature. The final model achieved the best fit between the measured and model temperature based on the Akaike information criterion (AIC) metric, where all external variables were used to compose the model. The proposed model shows the impacts of each external factor on equipment temperature and could be used to create a predictive maintenance strategy for power substations to avoid failure.

Suggested Citation

  • Osni Silva Junior & Jose Carlos Pereira Coninck & Fabiano Gustavo Silveira Magrin & Francisco Itamarati Secolo Ganacim & Anselmo Pombeiro & Leonardo Göbel Fernandes & Eduardo Félix Ribeiro Romaneli, 2023. "Impacts of Atmospheric and Load Conditions on the Power Substation Equipment Temperature Model," Energies, MDPI, vol. 16(11), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4295-:d:1154634
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    References listed on IDEAS

    as
    1. Ganesh Kumar Balakrishnan & Chong Tak Yaw & Siaw Paw Koh & Tarek Abedin & Avinash Ashwin Raj & Sieh Kiong Tiong & Chai Phing Chen, 2022. "A Review of Infrared Thermography for Condition-Based Monitoring in Electrical Energy: Applications and Recommendations," Energies, MDPI, vol. 15(16), pages 1-37, August.
    2. Christiane Baumeister & James D. Hamilton, 2019. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks," American Economic Review, American Economic Association, vol. 109(5), pages 1873-1910, May.
    3. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
    4. Irfan Ullah & Fan Yang & Rehanullah Khan & Ling Liu & Haisheng Yang & Bing Gao & Kai Sun, 2017. "Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach," Energies, MDPI, vol. 10(12), pages 1-13, December.
    5. Renan de Oliveira Alves Takeuchi & Leandra Ulbricht & Fabiano Gustavo Silveira Magrin & Francisco Itamarati Secolo Ganacim & Leonardo Göbel Fernandes & Eduardo Félix Ribeiro Romaneli & Jair Urbanetz J, 2022. "Comparison of Traditional Image Segmentation Methods Applied to Thermograms of Power Substation Equipment," Energies, MDPI, vol. 15(20), pages 1-17, October.
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