IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i11p4295-d1154634.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/11/4295/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/11/4295/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alam, Md Rafayet & Forhad, Md. Abdur Rahman & Sah, Nilesh B., 2022. "Consumption- and speculation-led change in demand for oil and the response of base metals: A Markov-switching approach," Finance Research Letters, Elsevier, vol. 47(PB).
    2. Sarah Arndt & Zeno Enders, 2023. "The Transmission of Supply Shocks in Different Inflation Regimes," CESifo Working Paper Series 10839, CESifo.
    3. Youssef, Manel & Mokni, Khaled, 2021. "Oil-gold nexus: Evidence from regime switching-quantile regression approach," Resources Policy, Elsevier, vol. 73(C).
    4. Joao Ayres & Constantino Hevia & Juan Pablo Nicolini, 2021. "Real Exchange Rates and Primary Commodity Prices: Mussa Meets Backus-Smith," Working Papers 89, Red Nacional de Investigadores en Economía (RedNIE).
    5. Patrick Zschech & Kai Heinrich & Raphael Bink & Janis S. Neufeld, 2019. "Prognostic Model Development with Missing Labels," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(3), pages 327-343, June.
    6. Mikhail Stolbov & Maria Shchepeleva, 2022. "In Search of Global Determinants of National Credit-to-GDP Gaps," Risks, MDPI, vol. 10(12), pages 1-22, December.
    7. Zhang, Zhikai & Wang, Yudong & Xiao, Jihong & Zhang, Yaojie, 2023. "Not all geopolitical shocks are alike: Identifying price dynamics in the crude oil market under tensions," Resources Policy, Elsevier, vol. 80(C).
    8. Berger, Eva M. & Bialek, Sylwia & Garnadt, Niklas & Grimm, Veronika & Other, Lars & Salzmann, Leonard & Schnitzer, Monika & Truger, Achim & Wieland, Volker, 2022. "A potential sudden stop of energy imports from Russia: Effects on energy security and economic output in Germany and the EU," Working Papers 01/2022, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung.
    9. Bakas, Dimitrios & Triantafyllou, Athanasios, 2020. "Commodity price volatility and the economic uncertainty of pandemics," Economics Letters, Elsevier, vol. 193(C).
    10. Moerman, G.A., 2001. "Unpredictable After All? A short note on exchange rate predictability," ERIM Report Series Research in Management ERS-2001-29-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    11. Peter McAdam, 2007. "USA, Japan and the Euro Area: Comparing Business-Cycle Features," International Review of Applied Economics, Taylor & Francis Journals, vol. 21(1), pages 135-156.
    12. Franck Sédillot, 2001. "La pente des taux contient-elle de l'information sur l'activité économique future ?," Economie & Prévision, La Documentation Française, vol. 147(1), pages 141-157.
    13. Engel, Charles, 1994. "Can the Markov switching model forecast exchange rates?," Journal of International Economics, Elsevier, vol. 36(1-2), pages 151-165, February.
    14. Rafał Weron, 2009. "Heavy-tails and regime-switching in electricity prices," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 457-473, July.
    15. Yip, Pick Schen & Brooks, Robert & Do, Hung Xuan & Nguyen, Duc Khuong, 2020. "Dynamic volatility spillover effects between oil and agricultural products," International Review of Financial Analysis, Elsevier, vol. 69(C).
    16. Vassilios Babalos & Mehmet Balcilar & Rangan Gupta, 2014. "Revisiting Herding Behavior in REITs: A Regime-Switching Approach," Working Papers 201448, University of Pretoria, Department of Economics.
    17. Theobald, Thomas, 2013. "Markov Switching with Endogenous Number of Regimes and Leading Indicators in a Real-Time Business Cycle Forecast," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79911, Verein für Socialpolitik / German Economic Association.
    18. Kal, Süleyman Hilmi & Arslaner, Ferhat & Arslaner, Nuran, 2015. "The dynamic relationship between stock, bond and foreign exchange markets," Economic Systems, Elsevier, vol. 39(4), pages 592-607.
    19. Kun-Huang Huarng & Tiffany Hui-Kuang Yu, 2017. "Using qualitative approach to forecasting regime switches," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(5), pages 2035-2048, September.
    20. Dimitris Kirikos, 2000. "Forecasting exchange rates out of sample: random walk vs Markov switching regimes," Applied Economics Letters, Taylor & Francis Journals, vol. 7(2), pages 133-136.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4295-:d:1154634. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.