Abstract
In order to realize the intelligent evaluation of moisture content of transformer oil-paper insulation, a comprehensive evaluation model combining multi-frequency domain characteristic parameters and random forest algorithm was proposed. Firstly, on the basis of in-depth analysis of the variation of dielectric properties in frequency domain of oil-paper insulation samples under different moisture conditions, nine characteristic parameters with significant correlation with moisture content were extracted. Secondly, multiple groups of measured characteristic data in the frequency domain were normalized, and the water content of the measured sample data was evaluated by random forest algorithm under the condition of multiple characteristic data. The experimental results show that the root-mean-square error and mean absolute error of the evaluation model constructed in this paper are significantly reduced compared with the traditional model, which can provide an effective scientific basis for the subsequent quantitative evaluation of the transformer oil-paper insulation state.
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Weng, Z., Zou, Y., Chen, X. (2022). Research on Comprehensive Evaluation of Moisture Content of Oil-Paper Insulation Based on Random Forest. In: Jia, Y., Zhang, W., Fu, Y., Yu, Z., Zheng, S. (eds) Proceedings of 2021 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 803. Springer, Singapore. https://doi.org/10.1007/978-981-16-6328-4_59
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DOI: https://doi.org/10.1007/978-981-16-6328-4_59
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