Moisture detection of oil-immersed bushing insulation based on color level analysis

Hongmin Ren; Denan Wang; Danni Duan

doi:10.1117/12.3014683

9 January 2024 Moisture detection of oil-immersed bushing insulation based on color level analysis

Hongmin Ren, Denan Wang, Danni Duan

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129692Q (2024) https://doi.org/10.1117/12.3014683
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

The operational level and service life of oil-immersed bushings are related to the moisture content in their cellulosic insulation layers. The authors proposed a novel method based on Taylor series modeling and grey relational analysis to assess the moisture content in the cellulosic insulation of oil-immersed bushings. Unlike conventional methods relying solely on frequency domain spectroscopy (FDS) data from certain frequency ranges, this method uses Taylor series to model the full spectrum and extracts a set of moisture-related feature parameters, thus avoiding dependence on specific measurement points or ranges. To establish the relationship between feature parameters and moisture levels, a database was constructed in this study. Grey relational analysis was used to propose an alternative method for assessing the moisture in bushing cellulosic insulation. Results show the average relative error between the estimated and measured moisture was less than 15.7%. Overall, the proposed method in this study provides an effective approach for assessing the moisture in the cellulosic insulation of oil-immersed bushings.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongmin Ren, Denan Wang, and Danni Duan "Moisture detection of oil-immersed bushing insulation based on color level analysis", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129692Q (9 January 2024); https://doi.org/10.1117/12.3014683

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