Journal of Electrical Engineering and Automation

Large-scale wind farm clusters face dual challenges of power prediction uncertainty and dispatch optimization during grid-connected operation. To address this issue, a comprehensive method integrating high-precision power prediction with adaptive dispatch optimization is proposed. By leveraging multi-source meteorological data and wind turbine operational characteristics, a deep learning prediction model is constructed to achieve high-precision short-term wind power forecasting. Combining the predicted results with grid operation constraints, a multi-objective optimization algorithm is employed to dynamically adjust the generation plans of wind farm clusters, balancing output stability and economic efficiency. Simulation results demonstrate that this approach significantly reduces prediction errors and enhances overall dispatch efficiency of wind farm clusters, providing effective technical support for large-scale wind power grid integration.

Large-scale wind farms; Power prediction; Dispatch optimization; Deep learning; Multi-objective optimization