Optimal Arrangement of Wind Farms in Complex Terrain Based on Improved Particle Swarm Optimization Algorithm and Wind Speed Distribution Regression Function Method

Aiming at the problems that traditional methods rely on field measurements and complex mathematical models, and intelligent optimization algorithms tend to fall into local optima, this study first improves the particle swarm optimization (PSO) algorithm, which improves the global search ability and convergence speed by dynamically adjusting inertia weight, introducing mutation operation and dynamically adjusting neighborhood topology. At the same time, the regression function of wind speed distribution based on historical wind speed data, terrain characteristics and meteorological conditions is constructed to accurately predict the wind speed distribution in the wind farm. Combining the improved PSO algorithm and the regression function of wind speed distribution, this study constructs an optimization model of wind farm layout. In this model, the particle swarm representing different layout schemes is initialized, and the wind speed distribution of each scheme is predicted by using the regression function of wind speed distribution and the annual power generation is calculated, which is used as the fitness index to evaluate the advantages and disadvantages of particles. By iteratively updating the particle position and velocity, the model finally outputs the optimal wind farm layout scheme. The experimental results show that, compared with the original layout, the annual power generation is significantly improved by adopting the optimized wind farm layout scheme, and the highest can reach 23.0%. The optimized layout not only improves the power generation efficiency of the wind farm, but also reduces the mutual interference and wake effect between the wind turbines. Compared with the traditional methods, the method proposed in this study shows higher efficiency and accuracy in the layout optimization of wind farms with complex terrain. This study successfully combines the improved PSO algorithm with the regression function of wind speed distribution, which provides an effective solution for the optimal layout of wind farms in complex terrain and has important guiding significance for the construction and operation of wind farms.