Given the complex operational environment of the ocean, wind turbine faults are diverse, and the effective sample data of faults are obviously insufficient. This seriously affects fault diagnosis results. To solve the problem of insufficient accumulation of offshore wind turbine operational data and fault samples, a data enhancement method based on a GRA-rACGAN generative adversarial network is proposed. This can effectively expand offshore wind turbine abnormal working condition data and carry out diagnosis validation through actual operational data. First, grey relation analysis (GRA) is performed on the data collected by the SCADA system to screen out the state variables that are highly correlated with the operating state of the wind turbines, normalize the data, and add the minimum and maximum ranges of the features as two additional attributes for each sample to avoid the interference of abnormal data and to improve the ability of data generation. Then, the filtered state-variable dataset is fed into an improved auxiliary classifier that employs a generative adversarial network for learning and expanding the fault data. Finally, the reliability of the fault data enhancement method is tested using the enhancement results of actual offshore wind turbine operation data as a sample for fault diagnosis. The measured results of the actual operation data of offshore wind farms show that this model can more effectively generate fault samples and improve the accuracy and stability of fault diagnosis than traditional data enhancement techniques, providing technical support for the accurate early warning of offshore wind turbine faults.