Neural network-based integrated reactive power optimization study for power grids containing large-scale wind power

The high uncertainty of wind power output greatly affects the rapid reactive power optimization of power systems. This paper proposes a neural network-based comprehensive reactive power optimization method for large-scale wind power grids, effectively addressing the challenges of rapid reactive power optimization in power systems. Firstly, by constructing typical wind-power-load scenarios, the generalization ability of the neural network is improved. Then, focusing on the comprehensive reactive power optimization problem after integrating typical wind-power-load scenarios into the system, the improved Harris hawks optimization algorithm (HHO) is compared with the particle swarm optimization algorithm and traditional HHO algorithm, highlighting its advantages. Finally, HHO is utilized for solving, thereby constructing a comprehensive reactive power optimization strategy tag set. Furthermore, through deep fitting of the neural network between the power grid operating state and the comprehensive reactive power optimization strategy, the computational complexity and decision-making time of reactive power optimization are reduced.