Transformer Oil Acid Value Prediction Method Based on Infrared Spectroscopy and Deep Neural Network

Linjie Fang, Chuanshuai Zong, Zhenguo Pang, Ye Tian, Xuezeng Huang, Yining Zhang, Xiaolong Wang () and Shiji Zhang
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Linjie Fang: State Grid Weifang Power Supply Company, State Grid Shandong Electric Power Company, Weifang 261000, China
Chuanshuai Zong: State Grid Weifang Power Supply Company, State Grid Shandong Electric Power Company, Weifang 261000, China
Zhenguo Pang: State Grid Weifang Power Supply Company, State Grid Shandong Electric Power Company, Weifang 261000, China
Ye Tian: State Grid Weifang Power Supply Company, State Grid Shandong Electric Power Company, Weifang 261000, China
Xuezeng Huang: State Grid Weifang Power Supply Company, State Grid Shandong Electric Power Company, Weifang 261000, China
Yining Zhang: State Grid Weifang Power Supply Company, State Grid Shandong Electric Power Company, Weifang 261000, China
Xiaolong Wang: School of Electrical Engineering, Shandong University, Jinan 250061, China
Shiji Zhang: School of Electrical Engineering, Shandong University, Jinan 250061, China

Energies, 2025, vol. 18, issue 13, 1-20

Abstract: The traditional detection method of transformer oil acid value has limitations, such as long detection period and toxicity of reagents; while, with the traditional spectral analysis, it is difficult to realize the efficient extraction of key features related to the acid value content. Early detection of rising acid levels is critical to prevent transformer insulation degradation, corrosion, and failure. Conversely, delayed detection accelerates aging and can cause costly repairs or unplanned outages. To address this need, this paper proposes a new method for predicting the acid value content of the transformer oil based on the infrared spectra in the transformer oil and a deep neural network (DNN). The infrared spectral data of the transformer oil is acquired by ALPHA II FT-IR spectrometer, the high frequency noise effect of the spectrum is reduced by wavelet packet decomposition (WPD), and the bootstrapping soft shrinkage (BOSS) algorithm is used to extract the spectra with the highest correlation with the acid value content. The BOSS algorithm is used to extract the feature parameters with the highest correlation with the acid value content in the spectrum, and the DNN prediction model is established to realize the fast prediction of the acid value content of the transformer oil. In comparison with the traditional infrared spectral preprocessing method and regression model, the proposed prediction model has a coefficient of determination (R 2 ) of 97.12% and 95.99% for the prediction set and validation set, respectively, which is 4.96% higher than that of the traditional model. In addition, the accuracy is 5.45% higher than the traditional model, and the R 2 of the proposed prediction model is 95.04% after complete external data validation, indicating that it has good accuracy. The results show that the infrared spectral analysis method combining WPD noise reduction, BOSS feature extraction, and DNN modeling can realize the rapid prediction of the acid value content of the transformer oil based on infrared spectroscopy technology, and the prediction model can be used to realize the analytical study of transformer oils. The model can be further applied to the monitoring field of the transformer oil characteristic parameter to realize the rapid monitoring of the transformer oil parameters based on a portable infrared spectrometer.

Keywords: transformer oil; infrared spectroscopy technique; wavelet packet decomposition; bootstrapping soft shrinkage; deep neural network (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: 2025
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