With the development of deep fusion intelligent control technology and the application of low-carbon energy, the number of renewable energy sources connected to the distribution grid has been increasing year by year, gradually replacing traditional distribution grids with active distribution grids. In addition, as an important component of the distribution grid, substations have a complex internal environment and numerous devices. The problems of untimely defect detection and slow response during intelligent inspections are particularly prominent, posing risks and challenges to the safe and stable operation of active distribution grids. To address these issues, this paper proposes a high-performance and lightweight substation defect detection model called YOLO-Substation-large (YOLO-SS-large) based on YOLOv5m. The model improves lightweight performance based upon the FasterNet network structure and obtains the F-YOLOv5m model. Furthermore, in order to enhance the detection performance of the model for small object defects in substations, the normalized Wasserstein distance (NWD) and complete intersection over union (CIoU) loss functions are weighted and fused to design a novel loss function called NWD-CIoU. Lastly, based on the improved model mentioned above, the dynamic head module is introduced to unify the scale-aware, spatial-aware, and task-aware attention of the object detection heads of the model. Compared to the YOLOv5m model, the YOLO-SS-Large model achieves an average precision improvement of 0.3%, FPS enhancement of 43.5%, and parameter reduction of 41.0%. This improved model demonstrates significantly enhanced comprehensive performance, better meeting the requirements of the speed and precision for substation defect detection, and plays an important role in promoting the informatization and intelligent construction of active distribution grids.

The measurement of the surface charge on the insulating dielectrics is critical to estimate the insulation strength of electrical equipment in operation. Unavoidable interactions with the electromagnetic field, however, generally result in distortion measurement of active sensor. Thus, it is of great significance to investigate a passive sensor for monitoring the deposited charge. Here, a passive optical fibre electrostatic sensor is developed on the principle of Fabry–Perot interference to quantify the surface potential of the insulating dielectrics. The sensitivity of quantifying the electrostatic charge is explored in terms of the wavelength shift in the interference spectrum, and the linear correlation is 0.99. Meanwhile, the sensor can identify a negative quadratic trend with the increase of the surface roughness of the insulating dielectrics. The merits of the proposed sensor such as passive monitoring, bipolar electrostatic detection, and the excellent ability of anti‐electromagnetic interference are highlighted in the results. Hence, this sensor can serve as an effective tool to monitor the electrostatic charge in the fields of smart electrical equipment, fuel transportation, aerospace, integrate circuit etc.

Photovoltaic (PV) power generation plays a significant role with the increase of installed capacity of renewable energy. The effects of environmental stress on insulating backsheets have been considered as the main cause of failure in PV systems. However, traditional aging models are difficult to realize the comprehensive evaluation of the lifetime of insulating backsheets. In this paper, the analytical method of complex chemical degradation related to the insulation was replaced by a physics-based method to quantify the elongation at the break as a function of time corresponding to temperature and radiation. In contrast to traditional aging models, this model simply used one parameter, namely drop-off rate (v), to reflect the degradation of polymers under various environmental stresses. The effect of ultraviolet (UV) radiation on the model was considered. Moreover, the electrical degradation, chemical changes, and mechanical properties caused by UV radiation were investigated to provide the reference for the lifetime of evaluation. The research is significant for comprehensively evaluating the lifetime of insulating materials for PV systems and other power equipment.