An accurate assessment of the damp status of oil-impregnated paper (OIP) bushings is crucial for the power industry to make informed decisions on the maintenance and replacement schedule of bushings. This article proposes a hybrid of the convolutional neural network (CNN) and the hidden Markov model (HMM) for estimating the damp status (i.e., moisture level and moisture source) of bushings especially when nonuniform moisture distribution exhibits in the bushing. First, simulation models of moisture diffusion and frequency-domain spectroscopy (FDS) of the OIP bushing were constructed using the finite element modeling (FEM) approach. Then, CNN was employed to extract informative features from FDS results of the OIP bushing, which is sensitive to both concentrations and sources of moisture. Finally, HMMs were further utilized as a strong stability tool to recognize the damp status of OIP bushings. The proposed method was implemented to identify the bushing damp status using both simulation data and real-life measurements. Identification results demonstrate that the proposed method has high accuracy in determining the moisture level and moisture source of the OIP bushing insulation.

中文翻译：

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OIP套管受潮状态的高精度诊断方法

准确评估油浸纸 (OIP) 套管的受潮状态对于电力行业就套管的维护和更换计划做出明智的决定至关重要。本文提出了卷积神经网络 (CNN) 和隐马尔可夫模型 (HMM) 的混合体，用于估计套管的潮湿状态（即湿度水平和潮湿源），尤其是当套管中出现不均匀的水分分布时。首先，使用有限元建模 (FEM) 方法构建 OIP 套管的水分扩散和频域光谱 (FDS) 仿真模型。然后，CNN 被用来从 OIP 套管的 FDS 结果中提取信息特征，该结果对水分的浓度和来源都很敏感。最后，HMM 被进一步用作强大的稳定性工具来识别 OIP 套管的潮湿状态。所提出的方法被实施以使用模拟数据和现实生活中的测量来识别套管阻尼状态。识别结果表明，该方法在确定OIP套管绝缘的水分含量和水分来源方面具有较高的准确度。