To address the issue of poor data quality in wind turbine operational data collected by the supervisory control and data acquisition system, a method combining an improved imputation diffusion model and long short-term memory (IDM-LSTM) was proposed. A dual-mask collaborative strategy was employed in the training process of the imputation diffusion model, which helped the model focus on key abnormal distribution regions and enhanced its robustness against abnormal disturbances. A hierarchical residual inverted Transformer (HRIformer) was used as the denoising model, combining the iTransformer with residual connections to improve the model’s ability to capture complex features. During the inference phase of the imputation diffusion model, the periodic visibility reconstruction mask (PVRM) strategy was applied, controlling the mask range by setting an appropriate mask cycle, ensuring the consistency of sequence reconstruction and temporal integrity. The imputation diffusion model is responsible for anomaly detection, while LSTM handles the correction, resulting in an integrated data cleaning framework for unlabeled wind power data. Experimental results from a real wind farm show that IDM-LSTM cleaning improved the Pearson correlation coefficients for wind speed-power and rotational speed-power by 3.78% and 3.43%, respectively, compared with the original data, significantly enhancing wind power data quality.