Wind farms offer substantial environmental advantages but pose serious challenges to power grid stability due to the diminishing presence of synchronous generators' inertia. While a wind farm can offer virtual inertia and power reserve support via pitch de-loading, there has been no investigation into the effect on the turbines’ fatigue load. Further, forecasting an accurate wind power reserve is challenging, especially with large wind turbines where the wind speed changes significantly through the rotor disk. This NSF ERI project aims to develop a robust framework for delivering reliable frequency support to the electric grid from wind farms, while considering grid stability and the longevity of wind turbines. The project will introduce transformative advancements in wind power control and forecasting by employing physics-informed machine learning techniques that integrate wind farm forecasting, turbine mechanical safety, and grid support functionalities. The intellectual merits of the project include: (i) Forecasting power for multi-megawatt wind turbines using wind profile data and remote sensing measurements to predict the behavior of each turbine; (ii) Ensuring safe electromechanical operation by predicting turbine fatigue load during frequency support; and (iii) Providing reliable frequency support to the grid through virtual inertia and guaranteed primary frequency response by the wind farm. The broader impacts include extending the operational lifespan of wind turbin