A High-Precision Diagnosis Method for Damp Status of OIP Bushing

Abstract

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.