An optical fiber composite overhead ground wire (OPGW) distributed ice-coating monitoring scheme based on Brillouin optical time domain reflectometer (BOTDR) temperature measurement is proposed and demonstrated. The thermodynamic factors affecting the temperature of the OPGW cable are analyzed, and the temperature variation characteristics of the icing and non-icing sections of the OPGW cable with different icing thicknesses are analyzed via finite-element analysis. The results of an OPGW icing experiment indicate that when the ambient temperature changes, there is an obvious hysteresis characteristic between the temperature changes of the icing and non-icing sections of the OPGW cable, which is related to the thickness of the ice. Thus, we propose a distributed OPGW cable icing monitoring system based on BOTDR temperature monitoring.

A novel monitoring method based on fiber Bragg grating sensor for detecting the ice coating on power transmission lines is proposed and demonstrated in this study. Sensors are installed on transmission line towers to monitor micrometeorological information and machine learning algorithms based on Gaussian processes use the real-time monitoring results to predict the ice thickness on the lines. The experimental results prove that the mean squared error and mean absolute percentage error values between the predicted and actual values are both less than 1%. This is the first time that only micrometeorological information has been used to monitor ice thickness.

A novel insulator string leakage current-monitoring sensor based on a fiber Bragg grating (FBG), cantilever beam, and spiral coil is demonstrated. The spiral coil is used to convert the leakage current into the force exerted on the cantilever structure, which is obtained by measuring the strain of the FBG. Experimental results show that the linear regression correlation coefficient is 0.99964. In addition, although the leakage current measurement is not sufficiently accurate, the 50-Hz component measurement is accurate. The sensor is suitable for use as an insulator string flashover warning and has promising prospects for future grid security protection applications.

The measurement of the leakage current and tower tension of transmission lines using fiber Bragg grating is proposed and demonstrated in this paper. The resonant frequency of the proposed leakage current sensor is about 100 Hz, the system sensitivity is about 1.3 nm/A and can measure the 50 Hz and 150 Hz frequency components of the leakage current accurately. However, it cannot measure the leakage current perfectly due to the complex frequency characteristics of leakage current. The proposed tension sensor has a system sensitivity of 0.054 nm/kN and exhibits good stability, linearity, and recovery.

Despite advancements in optical fiber sensing and power transmission technologies, existing systems face challenges of redundant multi-device deployment and incoordination between power supply and communication under complex working conditions such as strong electromagnetic interference, hazardous areas, or long-distance monitoring. This paper proposes a passive-active hybrid fiber Bragg grating (FBG) fiber-powered system, integrating sensing, power supply, and communication functions through a single-fiber architecture. The system utilizes the reflected spectrum of the FBG for wavelength demodulation, harnesses the transmission spectrum to supply DC power to remote low-power sensors, and establishes a high-speed backhaul channel based on the low-loss transmission characteristics of the FBG at the 1310 nm wavelength. Experimental results demonstrate that the system simultaneously achieves FBG temperature sensing (sensitivity: 9.7 pm/°C), power supply to electronic temperature monitoring nodes, and 1.25 Gbps error-free communication (BER < 10⁻¹²) over a 20-km single-mode fiber. This provides an innovative solution for reliable monitoring in high-risk scenarios such as petrochemical plants and power grids.

The detection of transmission towers in remote sensing images presents significant challenges due to insufficient feature representation and significant object size variations, particularly at low spatial resolutions where robust feature extraction and fusion capabilities are essential. To address these limitations, we propose YOLO with Sobel attention and dynamic branch reweighting (SABR-YOLO), an efficient detector incorporating two innovative lightweight modules: the Sobel-enhanced convo lutional block attention module (SECBAM) for enhanced local perception through edge-aware feature learning, and the branch reweighting feature pyramid network (BRFPN) for improved multi-scale feature fusion with minimal computational overhead. To verify the effectiveness of SABR-YOLO, we construct a new dataset of transmission tower object detection (TTOD), which includes transmission towers of various types, shapes, and spatial resolutions. To evaluate its performance on other multi-scale object detection tasks, we performed experiments on the public datasets of remote sensing object detection (RSOD) and super tiny object detection for remote sensing (RS-STOD). The experi mental results show that SABR-YOLO achieves mAP50 scores of 0.965 on the RSOD dataset, 0.872 on the RS-STOD dataset, and 0.756 on TTOD dataset, outperforming several baseline models and state-of-the-art methods. The related code and TTOD are public available at https://github.com/twiilight/SABR-YOLO