Complementary Detection of Zero-Value InsulatorsBased on the Fusion of Infrared Images and ElectricField Intensity Features

Abstract

To address the electrical safety hazards caused by missing zero-value insulators in high-voltage transmission lines, this study proposes a complementary detection method based on the fusion of infrared images and electric field intensity features. Infrared images of insulator strings are acquired using a UAV-mounted infrared thermal camera, and the surface temperature of each insulator is extracted via YOLOv8 to construct a temperature anomaly factor. Meanwhile, a three-dimensional electric field model of the insulator string is established using COMSOL Multiphysics 6.2 to extract the axial electric field intensity and calculate the electric field deviation. These two types of features are combined into a two-dimensional vector, and the Minimum Covariance Determinant (MCD) is used to estimate robust mean and covariance, from which a robust Mahalanobis distance is calculated to generate a fusion score for zero-value insulator identification. A three-level classification criterion is established, categorizing insulators as normal, suspicious, or zero-value. Experimental results demonstrate that this method effectively exploits the complementarity of infrared and electric field features, achieving the accurate identification of zero-value insulators, with a precision of 93.8%, zero false-negative and false-positive rates, and a suspicious rate of 6.2%. The method is validated to be highly accurate and robust.