This paper proposes a HILS-based data-driven curtailment weighting control framework for wind farm operation under grid-imposed output constraints. The main objective of the proposed framework is to extract wind farm operating data from a real-time hardware-in-the-loop simulation environment and utilize them for data-driven turbine-level power prediction and objective-function-based curtailment optimization. In the proposed framework, an RTDS-based wind farm model, wind farm management system, and wind turbine controllers are interconnected through Modbus TCP/IP communication to generate and collect operating data under curtailment conditions. The extracted data are processed and used to train a CNN-BiLSTM model for turbine-level power prediction, which provides available power information for the curtailment weighting control module. Unlike conventional forecasting-oriented approaches, the proposed method links data-driven prediction with control-oriented decision-making by incorporating an operational objective into the curtailment allocation process. As a representative case, internal power loss reduction is adopted as the objective function for determining turbine-level curtailment weights. Simulation results show that the proposed framework generates feasible active power references, reduces curtailment-related losses compared with conventional methods, and maintains turbine availability under constrained operating conditions.
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