Comparison of Traditional Image Segmentation Methods Applied to Thermograms of Power Substation Equipment

Renan de Oliveira Alves Takeuchi (), Leandra Ulbricht, Fabiano Gustavo Silveira Magrin, Francisco Itamarati Secolo Ganacim, Leonardo Göbel Fernandes, Eduardo Félix Ribeiro Romaneli and Jair Urbanetz Junior
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Renan de Oliveira Alves Takeuchi: Graduate Program in Energy Systems, Universidade Tecnológica Federal do Paraná, Avenue Sete de Setembro 3165, Curitiba 80230-901, PR, Brazil
Leandra Ulbricht: Academic Department of Electronics, 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
Leonardo Göbel Fernandes: Academic Department of Electrotechnics, 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
Jair Urbanetz Junior: Graduate Program in Energy Systems, Universidade Tecnológica Federal do Paraná, Avenue Sete de Setembro 3165, Curitiba 80230-901, PR, Brazil

Energies, 2022, vol. 15, issue 20, 1-17

Abstract: The variation in the thermal state of electrical energy substation equipment is normally associated with natural wear or equipment failure. This can be detected by infrared thermography, but technically it demands a long time to analyze these images. Computational analysis can allow an automated, more agile, and more efficient analysis to detect overheated regions in thermographic images. Therefore, it is necessary to segment the region of interest in the images; however, the results may diverge depending on the technique used. Thus, this article presents the improvement of four different techniques implemented in Python and applied in a substation under real operating conditions for a period of eleven months. The performance of the four methods was compared using eight statistical performance measures, and the efficiency was measured by the runtime. The segmentation results showed that the methods based on a threshold (Otsu and Histogram-Based Threshold) were fast, with processing times of 0.11 to 0.24 s, but caused excessive segmentation, presenting the lowest accuracy (0.160 and 0.444) and precision (0.004 and 0.049, respectively). The clustering-based methods (Cluster K-means and Fuzzy C-means) showed similar results to each other but were more accurate (0.936 to 1.000), more precise (0.965 to 1.000), and slower, with 2.55 and 38.8 s, respectively, compared to the threshold methods. The Fuzzy C-means method obtained the highest values of specificity, accuracy, and precision among the methods under analysis, followed by the Cluster K-means method.

Keywords: infrared thermography; substation equipment; abnormal operation; segmentation; image processing (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: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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