Objective Wind turbines bring clean energy but also face a series of technical challenges. Among them the most prominent issues are the stability and reliability of the turbines. Addressing the issue of anomaly detection in wind turbine data a scientific and effective improvement method is proposed—residual-based isolation forest. Methods Given the disturbances from normal operational fluctuations and sensitivity to parameters in traditional isolation forest methods a residual-based isolation forest approach is proposed for anomaly detection. Initially a RANSAC regression model predicts the relationship between wind speed and power calculating the residuals between actual and predicted power. These residuals are then used as input features in the isolation forest model for anomaly detection. This method focuses on parts of the model that are difficult to interpret reducing interference from natural fluctuations during normal operation and enhancing precise identification of potential anomaly points. Results Visualizations of anomaly detection results demonstrate that the improved method excels in identifying local anomalies in power curves. Conclusion The residualbased isolation forest method can more accurately identify anomalies under various operational conditions providing robustdata support for the maintenance management of wind farms. Additionally this method offers new perspectives and references for anomaly detection in other fields with broad application prospects