@article{ZHU2025, 
author = {Qing ZHU and Pengcheng CAI and Weiwei ZHU and Fangru WAN and Caihua LIU and Xia ZHOU and Xuekuan CHEN},
title = {Equivalent Modeling of Wind Farm Based on PSO-LSTM-ECM Method},
year = {2025},
journal = {Distributed Energy},
volume = {10},
number = {3},
pages = {11-22},
keywords = {wind farm, equivalent modeling, deep learning, error correction model, long short-term memory neural network},
url = {https://www.sciopen.com/article/10.16513/j.2096-2185.DE.24090666},
doi = {10.16513/j.2096-2185.DE.24090666},
abstract = {Dynamic equivalent modeling of large-scale wind farms is the foundation for studying wind power grid integration, while the clustering-based equivalent model of wind farms cannot fit the dynamic output characteristics with high accuracy, and the poor generalization ability in its application is an inherent defect of clustering based model. Aiming at this problem, this paper proposes a wind farm equivalent modeling method based on particle swarm optimization-long short term memory neural network-error correction model (PSO-LSTM-ECM). Firstly, K-means clustering algorithm and capacity weighting method are used to cluster wind turbines in wind farms, and a clustering equivalent model of the wind farms is constructed; Then, ECM is constructed based on the transient response errors of the detailed model and the clustering equivalent model, and the correction model is obtained through the LSTM neural network training optimized by PSO, and the output value of the network is compensated to the clustering equivalent model; Finally, a joint simulation is conducted on PSCAD and Matlab platforms to compare and analyze the detailed wind farm model, clustering equivalent model, and the model proposed in this paper. The result proves the effectiveness and superiority of the proposed model.}
}