Research on the method of detecting low-/zero-value insulators using infrared spectroscopy

Insulators are the core components of high-voltage transmission lines, and the occurrence of low-/zero-value defects due to insulation degradation seriously endangers the safe and stable operation of power systems. Traditional detection methods for defective insulators suffer from low efficiency, poor anti-interference performance, and heavy reliance on manual experience, which are difficult to meet the demands of intelligent power grid operation and maintenance. In this work, a non-destructive detection method for low-/zero-value insulators based on infrared spectroscopy was proposed to realize the accurate and automatic identification and localization of defective insulators. Seven XP-100 porcelain suspension insulators were taken as the research objects, and an experimental platform was built in an artificial climate chamber to investigate the heating and discharge characteristics of zero-value insulators under different contamination conditions and spatial positions by combining infrared and ultraviolet imaging technologies, clarifying the typical low-temperature anomaly characteristic of zero-value insulators caused by volume resistance approaching zero. A two-dimensional axisymmetric model of insulator strings was constructed based on the finite element method, and the bidirectional coupling simulation of electric and temperature fields was carried out. The simulation results showed good consistency with the experimentally measured data, with a temperature difference error of less than 2 °C, which verified the reliability of the temperature field distribution law of defective insulators. An intelligent processing scheme for insulator infrared images was designed, which realized the automatic segmentation and feature extraction of insulator strings and key regions under complex backgrounds through Gaussian filtering denoising, histogram equalization, Hough transform angle correction, Canny edge detection and connected region analysis; it also extracted multidimensional features including temperature, texture and spatial distribution. Furthermore, an intelligent diagnostic model for low/zero-value insulators was established based on an improved back propagation (BP) neural network, which adopted a gradient descent optimization algorithm with momentum and adaptive learning rate to solve the problems of slow convergence and local minima of the traditional BP network. Taking corona discharge parameters (frequency, amplitude, and duration) and environmental temperature and humidity as input features, the model achieved automatic identification and precise positioning of low/zero-value insulators. Experimental results demonstrate that the proposed method effectively overcomes the limitations of traditional detection approaches, improves the accuracy and efficiency of defective insulator diagnosis, and realizes the integration of experimental mechanism analysis, multi-physics field simulation, intelligent image processing, and machine learning diagnosis. This research provides a reliable technical approach for the state monitoring of transmission line insulators and has important engineering application value for promoting the intellectualization of power equipment fault detection.