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

Osni Silva Junior (), Jose Carlos Pereira Coninck, Fabiano Gustavo Silveira Magrin, Francisco Itamarati Secolo Ganacim, Anselmo Pombeiro, Leonardo Göbel Fernandes and Eduardo Félix Ribeiro Romaneli
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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
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

Energies, 2023, vol. 16, issue 11, 1-15

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.

Keywords: atmospheric variables; thermographic; power substation; weather station; regression model; time series (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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