Dynamic Equivalent Modeling of Wind Farm Based on Dominant Variable Hierarchical Clustering Algorithm

The actual operating state of the wind turbine group is influenced by the wake effect and control mode; however, the current models cannot describe the actual operating state very well. A dynamic equivalent modeling method for a doubly fed wind power generator is proposed on the basis of ensuring the accurate description of the wind turbine group. As the clustering index, dominant variables are used in the hierarchical clustering algorithm, which are extracted by principal component analysis. Three dynamic equivalent models of 24 wind turbines are established using PSCAD software platform, which use 13 state variables, wind speed, and dominant variables as clustering indexes, respectively. Furthermore, the active power and reactive power output curves of wind farm are simulated in the case of the three-phase short-circuit fault on the system side or wind speed fluctuation, respectively. The simulation results demonstrate that it is reasonable and effective to extract slip ratio and wind turbine torque as clustering index, and the maximal relative error between the dominant variable equivalent model and 13-state-variable model is only 9.9%, which is greatly lower than that of the wind speed model, K-means clustering model, neural network model, and support vector machine model. This model is easy to implement and has wider application prospect, especially for characteristics analysis of large-scale wind farm connected to power grid.

Automated summary

A dynamic equivalent modeling method based on principal component analysis and hierarchical clustering algorithm is proposed, which accurately describes the actual operating state of the wind turbine group and has good application prospects for characteristics analysis of large-scale wind farms.