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The power equipment at smart substations plays a key role in transmitting electric energy. The stable operation and the transmission of electric energy greatly impact the substation equipment’s life, performance, safety, and other factors (Wang et al., 2022). During actual operation, power equipment will be affected by overload, overvoltage, internal insulation aging, abnormal natural environment and other events, and abnormal operation status will lead to equipment defects and failures (Ye et al., 2022).
As a key component of the power system, power equipment will not only affect the power equipment itself, but also have an immeasurable impact on the large power grid system when serious faults occur (He et al., 2022). Therefore, the reliability of power equipment in smart substations must receive due attention, particularly in increasingly complex power systems.
Traditional power equipment status recognition is realized through regular inspection by operation and maintenance personnel, which makes it difficult to realize the timely perception of the status of power equipment, and the increasing power load will also cause the lines and supporting power equipment at various voltage levels to go up in the station (Yang et al., 2021). The surge in patrol inspection workload overwhelms the operation and maintenance personnel, and the inspection of some electrical equipment could be wrongly performed.
The traditional planned maintenance method can hardly obtain the operation status and health status of power equipment in smart substations with accuracy and reliability. “Condition-based maintenance” has become a prevailing trend for the maintenance system. Furthermore, the trend of combing information and energy technology has provided new solutions for power equipment condition monitoring (Liu P. et al., 2020).
The power Internet of Things (pIoT) can realize resource integration of substation communication system and power system (Han, 2021; Long, 2022; Hason et al., 2021), collect the effective image data set of the state quantity of the power equipment with multiple types of terminal intelligent devices, and achieve accurate analysis of the equipment state through intelligent algorithms (Lei et al., 2022). Chen et al. (2020) describes the application of pIoT technology in equipment lifecycle research, compares and analyzes the traditional maintenance methods based on time. LuXH et al. (2022) realized the health detection of power equipment by optimizing and upgrading the network security processor. Wang et al. (2021) placed multiple sensors in the power transformer, built the Internet of Things network in the station, and established a mathematical model of power transformer fault diagnosis to achieve state monitoring and analysis.
Machine vision technology and deep learning technology can be introduced to help realize power equipment’s condition monitoring and analysis. Deep learning can realize intelligent analysis of image data sets of acquisition equipment in the pIoT, extract and analyze information features of sample data through multi-layer network structure, and achieve analysis and judgment of equipment status (Hou et al., 2019; Liu et al., 2020; Davari et al., 2022). Zheng et al. (2021) introduced a feature pyramid to obtain image information features, and uses clustering algorithms to change the sliding frame of image analysis adaptively. Su et al. (2022) collected a video dataset and used a pyramid module to capture information of interest, obtaining an effective dataset and reducing the computational complexity of the model. Based on the Yolov4 network model, real-time status evaluation of transformers was achieved. Liu et al. (2022) used Yolov4 network-based analysis for infrared image datasets, analyzed the impact of relevant factors on target detection performance, and established the optimal detection model. Zhao et al. (2021) adopted a limited sliding network (LSNet) to achieve regional and centralized defect detection, and uses the STYLE model and non-maximum suppression method to locate the target and enrich the features of the image, achieving accurate classification. Ullah et al. (2020) combined random forest algorithm and support vector machine, extracts rich feature maps from the convolution layer of AlexNet pre training model, and trains random forest (RF) and support vector machine (SVM) to learn defective and non-defective high-voltage electrical equipment, to achieve early prevention and analysis of thermal anomalies of electrical equipment.