This work implements first-principles simulations in order to investigate the Ni-decorating property on the ZnO monolayer and the sensing property of the Ni-decorated ZnO (Ni–ZnO) monolayer upon CO and HCHO molecules formed in the dry-type transformers. The results reveal that the Ni dopant is stably anchored on the T_O site of the ZnO surface forming the Ni–Zn and Ni–O bonds with the binding energy (E_b) of −1.75 eV. Based on the adsorption energy (E_ad) of −1.49 and −2.22 eV for CO and HCHO on the Ni–ZnO monolayer, we determined the chemisorption for two such systems. The band structure (BS) and atomic density of state (DOS) of the gas adsorbed systems are analyzed to comprehend the electronic property of the Ni–ZnO monolayer in the gas adsorptions. Besides, the change of bandgap and work function uncover the sensing potential of Ni–ZnO monolayer upon CO and HCHO detections, with admirable electrical response (15,394.9% and −84.6%). The findings in this work manifest the potential of Ni–ZnO monolayer for CO and HCHO sensing to evaluate the operation condition of the dry-type transformers.

Winding fault is one of the most common types of transformer faults. The frequency response method is a common diagnosis method for winding fault detection. In order to improve the feature extraction ability of the frequency response curve before and after the winding fault, this paper proposes a winding fault feature extraction method based on the moving window algorithm to improve the Euclidean distance and correlation coefficient and uses a support vector machine to diagnose winding fault. “Moving window meter algorithm” refers to the fixed moving window width and window moving interval, scanning the entire frequency response curve from the initial point to the end point of the frequency response curve, using the correlation coefficient (CC) and Euclidean distance (ED) to calculate the mathematical index of each window. The mathematical index of each window is used as the characteristic quantity of fault type classification. Finally, the grid search algorithm is used to optimize the support vector machine to classify and identify the type of winding fault. At the same time, the standard support vector machine s(SVM) and back propagation neural network algorithm (BPNN) are compared with the support vector machine optimized by the grid search method to diagnose the fault type. The research shows that the improved correlation coefficient and Euclidean distance using the moving window algorithm are more sensitive to winding faults than the traditional calculation methods. The combination of the two calculation methods makes up for the shortcomings of their respective methods. The fault features obtained meet the requirements of the support vector machine for fault diagnosis, and the grid search method-optimized support vector machine classification algorithm has a good classification and recognition effect on the identification of fault types. The effectiveness and superiority of this method are further illustrated.

In the context of the large-scale integration of high-proportion renewable energy into power grids, Power Conversion Systems (PCS) and Static Var Generator (SVG), as the core power electronic devices for ensuring grid frequency stability and power quality, demand high-level safety and reliability. To tackle challenges such as the wide range of defect sizes in PCS and SVG, the low recognition accuracy for microscopic fuzzy defects, and complex background interference, this paper presents a lightweight and high-precision defect recognition model (RD-YOLO) based on the latest YOLOv11 benchmark. First, an improved Mosaic algorithm is introduced. This algorithm utilizes conflict relationship tables to preserve physical context semantics during traditional non-discriminative data augmentation. Second, to surmount the limitations of scale-aware feature extraction, the YOLOv11 is re-engineered within the backbone network by integrating a Res2Net multi-scale cascaded mechanism. This enhances the network’s capacity to capture both fine-grained defect features and large-scale defect boundaries. Third, Focal Loss is employed for difficult sample detection. Nonlinear gradient modulation is utilized to guide the model to focus on ambiguous defect edges. Finally, the Soft-NMS post-processing strategy significantly enhances the regression accuracy in densely corroded regions. Experimental validation on a self-developed dataset consisting of 8500 high-resolution PCS and SVG defect images reveals that the enhanced RD-YOLO attains an average precision of 89.6% and a frame inference rate of 98 FPS (in RTX 3090), offering robust technical support for intelligent visual maintenance in renewable energy facilities.