Zhu, Q.; Xiong, W.; Wang, H.; Jin, X. Refined Equivalent Modeling Method for Mixed Wind Farms Based on Small Sample Data. Energies2023, 16, 7191.
Zhu, Q.; Xiong, W.; Wang, H.; Jin, X. Refined Equivalent Modeling Method for Mixed Wind Farms Based on Small Sample Data. Energies 2023, 16, 7191.
Zhu, Q.; Xiong, W.; Wang, H.; Jin, X. Refined Equivalent Modeling Method for Mixed Wind Farms Based on Small Sample Data. Energies2023, 16, 7191.
Zhu, Q.; Xiong, W.; Wang, H.; Jin, X. Refined Equivalent Modeling Method for Mixed Wind Farms Based on Small Sample Data. Energies 2023, 16, 7191.
Abstract
For equivalent modeling of mixed wind farms (WFs), existing clustering indicators cannot consider the complex coupling characteristics between different types of wind turbines (WTs). This paper proposes a refined equivalent modeling method for mixed WFs based on artificial intelligence technology. Firstly, the electromechanical transient performance of mixed WFs is analyzed. The WT type, wind speed and direction, and voltage dip are considered the main factors affecting the external dynamic response of mixed WFs. Secondly, the equivalent node model is established, including the selection of independent and dependent variables. Then, the multiple artificial neural networks (ANNs) are trained one by one based on sample data, to explore the nonlinear relationship between the dependent variables and the independent variables. Finally, the electromechanical transient simulations of the power systems with a mixed WF is carried out by using the MATLAB platform. Simulation results show that the proposed model can reflect the external characteristics of the mixed WF in different wind scenarios and voltage dips.
Keywords
Mixed wind farm; refined equivalent modeling; artificial intelligence technology; small sample data; equivalent node model
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
