In this study, the effects of two frequent operational errors in wind turbines yaw misalignment and blade pitch offset were quantified and modeled for the turbine aerodynamic response in two operating regions: Region 2 (below-rated) and Region 3 (rated/above-rated). To obtain results that are generalizable and comparable across different scales, three reference turbines (3.4 MW, 5 MW, and 10 MW) were selected and time-domain simulations were performed using OpenFAST/AeroDyn (BEM) under steady wind conditions. Quasi-steady quantities, including the power coefficient and aerodynamic power (and, in intermediate steps, aerodynamic torque), were extracted from the steady-state window. To systematically cover the parameter space , a design of experiments (DOE) approach was applied, and quasi-steady quadratic response-surface surrogate models (RSM/QS) were fitted separately for Regions 2 and 3. Validation was carried out by direct comparison between surrogate predictions and OpenFAST results (including parity plots). In addition to 2D sensitivity curves with respect to yaw and pitch, response maps and 3D surfaces were provided to visualize continuous variations of and across the operating space. Finally, using the final quadratic equations and their coefficients as low-cost relations, the impact of different sensor uncertainties on turbine power output was evaluated via a Monte Carlo method, showing that LiDAR and MEMS sensors yield the smallest power losses. Specifically, for the MEMS sensor, the worst-case (P95) power loss is approximately – for yaw-only misalignment across all three turbines, and approximately – for the combined “wind-speed error + induced pitch miscommand” scenario.