Views: 0 Author: Site Editor Publish Time: 2026-10-08 Origin: Site
Transmission line insulators operate under continuous exposure to pollution deposition, humidity cycling, and long-term electromechanical stress. These factors progressively degrade insulation performance, leading to surface contamination, internal cracks, low-resistance and zero-resistance defects, and ultimately pollution flashover. Manual inspection remains the dominant practice in many utilities, but its high labor cost and low detection efficiency are increasingly untenable as transmission networks expand.
Leakage current monitoring has emerged as a primary online detection method because the magnitude and harmonic content of leakage current respond directly to surface contamination, dielectric stress, and dry-band formation. As pollution accumulates and moisture forms a conductive surface layer, leakage current rises, making it a sensitive early indicator of flashover risk.
Recent hardware advances have improved the practical viability of this approach. Tunneling magnetoresistance (TMR) sensors offer passive operation, strong electromagnetic interference resistance, and high sensitivity in the complex electromagnetic environment of transmission lines. IoT-enabled LoRa systems further enable low-cost, long-range wireless data transmission, making distributed leakage current monitoring feasible without costly infrastructure deployment. Quantitative correction models incorporating multiple environmental parameters can suppress interference from temperature and humidity variation, improving the accuracy of pollution severity assessment.
However, leakage current alone cannot localize the degradation within an insulator string, nor can it distinguish between surface contamination and internal defects—both of which produce elevated current but require fundamentally different maintenance responses.
Passive infrared thermography complements leakage current measurement by providing spatially resolved temperature data without contact or external excitation. Degradation in insulator bodies or at material interfaces generates abnormal temperature rise that IR imaging can capture directly. For low-resistance and zero-resistance insulators, the steel cap region exhibits characteristic heating patterns that distinguish degraded units from adjacent healthy ones.
The physical coupling between leakage current and thermal behavior is well established. Under wet-contaminated conditions, surface leakage current heating produces abnormal temperature rises and hotspot distributions detectable through infrared imaging. For internal defects—particularly at the rod–housing interface of composite insulators—conductive channel formation allows increased leakage current flow and electrical discharges, with temperature rising along the insulator in a manner correlated with the severity of interfacial degradation. A unique leakage current waveform signature has been observed for aged insulators, accompanied by shank-region temperature elevation when operating voltage is applied.
The diagnostic power of combining these two modalities lies in their complementary strengths. Leakage current provides a continuous, quantitative electrical indicator sensitive to environmental and surface conditions, while infrared thermography delivers spatial localization and discrimination between surface and internal degradation mechanisms.
The most technically rigorous fusion approach operates at the feature level. Heterologous entropy features extracted from infrared images and leakage current signals respectively characterize the energy release degree of pollution flashover faults from different physical perspectives. Fisher criterion-based feature selection identifies quantities that most effectively represent operating conditions, and Bayesian decision theory fuses these heterogeneous features for diagnosis. Experimental results demonstrate that the accuracy of this fusion method is significantly higher than that of infrared or leakage current features used alone.
Multi-parameter composite perception extends this concept further by integrating infrared images with flexible temperature sensor data, pin pressure sensor data, and leakage current characteristic values—including effective value, maximum value, third-harmonic-to-fundamental ratio, and total harmonic distortion rate—alongside relative environmental humidity. This multimodal approach enables comprehensive state evaluation covering pin separation, abnormal heating at connections, body heating, and pollution severity.
A practical online monitoring system requires hierarchical data processing. At the sensing layer, TMR leakage current sensors and infrared thermal cameras operate synchronously, with environmental sensors recording humidity and temperature for correction model inputs. At the edge layer, lightweight algorithms perform initial feature extraction and anomaly flagging, reducing data transmission volume and enabling real-time response. At the platform layer, fused diagnostic models produce structured reports and maintenance recommendations.
Adaptive Neuro-Fuzzy Inference System (ANFIS) approaches have demonstrated particular promise for establishing nonlinear correlations between surface temperature distribution and electrical stress, enabling leakage current prediction from thermal imaging under varying humidity conditions. Digital twin platforms further enhance visualization by displaying fault insulators in real time and simulating grid stability under different disposal scenarios, supporting maintenance decision-making.
Laboratory and field tests confirm that integrated leakage current and infrared thermography systems achieve continuous live-line monitoring with good accuracy, effectively detecting early defects and excessive pollution to provide reliable data support for condition-based maintenance. The non-invasive nature of both modalities—passive IR thermography requiring no external heat source and leakage current sensing requiring no physical intervention—makes the combined approach suitable for deployment on energised transmission lines.
