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

Wenbo Jiang,Mingyue Zhong,Albert Alexander Stonier

doi:10.1155/2021/7629414

Abstract

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.

Highlights

Wind power generation is one of the most important renewable energies, which has attracted more and more attention from most of the countries for its mature technology and low cost in recent years [1]
Literature [6] used the integrating field measurement method to characterize the monthly wind speed and wind direction distributions and investigate the wind characteristics in turbine wakes. e research work shows that good agreement is obtained for both mean wind speed and turbulence intensity, which verifies the possibility of combining actual field measurements and high-fidelity simulations to describe the characterization of utility-scale wind farms
Literature [7] studied large-eddy simulations of coherent structures within and above different wind farm configurations in a neutral atmospheric boundary layer (ABL) using proper orthogonal decomposition (POD) to improve understanding of the flow structures in both physical and spectral space. e research work indicates that wind farm dynamics in the ABL are very complex

Summary

Introduction

Wind power generation is one of the most important renewable energies, which has attracted more and more attention from most of the countries for its mature technology and low cost in recent years [1]. Literatures [11,12,13,14] proposed the single-machine equivalent model of wind farm, but the characteristics of wind turbines change greatly based on actual applications. Literatures [20,21,22] sorted the wind farm by clustering algorithm, which is based on the measured data of all the wind turbines, the wind speed model, and the steady-state model of wind farm This method does not consider dynamic characteristics of wind turbines. Simulation results demonstrated the effectiveness of the model in capturing dynamic behavior of wind farm following large disturbances This kind of classification method has the disadvantages of redundant data, high calculation complexity, low applicability, etc. To overcome the above shortcomings, the multi-machine dynamic equivalent modeling of wind farm based on dominant variables hierarchical clustering algorithm is proposed in this paper. E simulation results verify the effectiveness of dominant variables and the advantages of this proposed model

Mathematical Model of Doubly Fed

Dominant Variable Hierarchical Clustering Algorithm

Simulations

Conclusions