| Title |
Active Power Optimization Control of Wind Farm Integrating K-Means Clustering and Improved Particle Swarm Optimization Algorithm |
| Authors |
(Yan Zhao) ; (Jianghao Yang) ; (Xiaofeng Chen) |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.5.727 |
| Keywords |
Wind farm; Active power optimization control; K-means clustering; Particle swarm optimization algorithm; Wake superposition effect |
| Abstract |
The wake effect of wind turbines in wind farms has a significant impact on wind energy utilization. Existing optimization methods suffer from low efficiency and large errors in handling coordinated control of multiple units. To this end, a mathematical model for the superposition effect of wind turbine wake is established. Then a multi-unit coordinated optimization active power control method is proposed by combining K-means clustering and improved particle swarm optimization algorithm to address this challenge. The results showed that the wind speeds of the six wind turbines in the wind farm exhibited a downward trend under the influence of wake flow. When the wind turbine operated at reduced load and reduced the wind energy conversion, the wind energy utilization coefficient decreased. The active power of the improved model gradually increased at 1.22s-1.60s, and remained consistent with the true value. The relative error was 0.41%, while the relative error of the unimproved model was 2.26%. After 1.628s, the active power curve of the improved model remained consistent with the true value. The relative errors between the improved model, K-means model, and the true values were 0.41% and 1.82%, respectively.
This method can flexibly switch the active power target of the wind farm, optimize the unit speed and pitch angle, improve the system frequency response, and increase the output power of the wind farm. This achieves optimized computational efficiency and accuracy, reduces reliance on traditional energy sources, and promotes the development of sustainable energy. Applying this method to actual wind farms can ensure its feasibility and effectiveness in practical engineering, promoting the development and application of wind power generation technology. |