## 1 Introduction  
As a crucial piece of equipment in the power system's transmission and distribution chain, the box-type substation performs a vital role in voltage regulation and electricity distribution, with widespread applications in urban and rural areas, industrial and mining enterprises, and public buildings. Because the box-type substation is mainly installed outdoors, its operating environment is complex and changeable, making it very susceptible to damage from natural factors and external forces. Therefore, combined with its own internal equipment diversity, there will be a variety of fault problems in the operation process, leading to various challenges in maintenance and management. This undoubtedly poses a severe challenge to the stability and reliability of the power supply system, directly affecting the safety of daily electricity use and the production efficiency of enterprises. Therefore, timely, effective, and reliable health monitoring of box-type substations is of great significance for the safe operation of the power system. At present, the traditional manual inspection mode requires that the inspection personnel have certain prior knowledge and experience. Furthermore, the box-type substation structure is complex, and there are numerous components, which makes the inspection task extremely challenging. In addition, traditional regular inspections have inherent lags, which not only seriously reduce work efficiency and increase unnecessary costs but also make it difficult to detect and troubleshoot hidden faults in time. Therefore, the research of fault diagnosis technology has gradually become a research hotspot. Fault diagnosis technology aims to identify both the normal and abnormal conditions of equipment, whether globally or locally, by monitoring and analyzing its operational status. In the case of malfunction, the technology can also classify the fault and pinpoint the faulty component accurately. Currently, the mainstream fault diagnosis techniques primarily include methods based on physical models, statistical models, and artificial intelligence. Among them, fault diagnosis technology based on deep learning has received widespread attention due to its high diagnostic accuracy and the popularity of data acquisition technology, without needing a deep understanding of the physical model of the diagnostic object. However, deep learning-based fault diagnosis methods face a challenge in practical applications: they rely on massive data accumulation. Since box-type power distribution equipment spends most of its time in normal operating conditions, fault samples are scarce, resulting in an imbalance between healthy samples and fault samples, which can affect diagnostic performance. To address this issue, current research tends to adopt generative adversarial networks (GANs). This approach directly addresses the problems of small sample sizes and class imbalance from the input source layer, simplifying complex data sampling and processing procedures while avoiding the tedious task of building specialized diagnostic models for different diagnostic objects. Therefore, for highly integrated and complex equipment, such as box-type substations, this article utilizes collected historical data of box-type substations to construct a data derivation model based on the improved CTGAN. Replacing the two fully connected layers in the CTGAN generator with self-attention layers transforms the static weights generated in the CTGAN generator into dynamic weights that are free from positional dependencies during data input, enabling better preservation of the correlation between different features. By learning the relationship matrix between input features through the self-attention mechanism, the correlation between different features is maintained, thereby improving the drawback of CTGAN's failure to model the dependency relationships between each feature. This approach generates more high-quality data from a limited number of faulty samples.