Despite the continuous development of wind power forecasting methods, forecasting errors remain unavoidable, especially during extreme weather events. However, current research on quantifying these errors is quite limited. This paper proposes an adaptive error risk early warning method that can directly predict the magnitude of forecast errors and classify and warn of risks, thereby achieving proactive risk management. This method comprises three core designs. First, mechanism-based feature engineering captures the driving factors of error generation, including numerical weather prediction bias, atmospheric instability, and meteorological dynamics, all of which are key factors leading to forecast bias. Second, a stacked ensemble method integrates quantile regression, random forest, and gradient booster, utilizing complementary learning capabilities to handle high-dimensional non-stationary error patterns. Third, K-means clustering establishes a dynamic risk threshold that adapts to changes in seasonal error distribution, overcoming the limitations of fixed thresholds. Validation using actual wind farm operation data demonstrates significant improvements: the proposed ensemble model reduces the Root Mean Square Error (RMSE) by 2.5% compared to the best single model, and the dynamic threshold mechanism increases the High-Risk Recall rate from 89.7% to 96.9%. These results confirm that the method can effectively warn of high-error events and provide timely and actionable decision support to enhance grid stability and security.