A lightweight generative adversarial network interpolation strategy based on adaptive transformer slim GAIN(AT-SGAIN) is proposed to address the common problem of data loss in the collection and transmission of supervisory control and data acquisition(SCADA) data in wind farms, aiming to enhance data integrity. AT-SGAIN simplifies the GAIN model structure, significantly improves computational efficiency, and adopts a dual discriminator structure for distinguishing between real data and generated data, ensuring the maintenance of interpolation accuracy during the speed improvement process. This model integrates a Transformer encoder, enhancing the ability to capture time series features of wind power data. Through an adaptive dual branch attention mechanism, it accurately adjusts channel and spatial attention weights, improving the network's sensitivity to local information. The experimental results show that, in multiple comparative tests, the algorithm proposed is significantly better than existing classical methods.