The frequent overtemperature of the final-stage superheater tube wall is a complex issue

and accurately tracing the causes of unit overtemperature is crucial for addressing overtemperature problems at their source

which is of paramount importance for the safe operation of power plants. In this paper

a superheater overtemperature diagnosis method is proposed based on improved generative adversarial networks and deep transfer learning. First

the key feature variables collected by the SIS system are transformed into two-dimensional images using GADF encoding to adapt to the transfer learning model. Second

an improved conditional Wasserstein GAN (CWGAN) is utilized to generate high-quality image samples

significantly enhancing the generalization ability of the Xception network in the sparse and imbalanced source domain. Finally

the pre-trained Xception network on the source domain unit is fine-tuned and transferred to the sparse and imbalanced target domain unit. The proposed method is validated using two in-service 600MW ultra-supercritical units. The results indicate that the average accuracy of the proposed method reaches 99.03%. Compared with traditional deep learning methods

this method exhibits faster convergence and higher accuracy

achieving rapid diagnosis of overtemperature in the final-stage superheater of the target domain unit.