分布式能源2025，Vol.10Issue(3)：11-22,12.DOI:10.16513/j.2096-2185.DE.24090666
基于PSO-LSTM-ECM的风电场等值建模方法
Equivalent Modeling of Wind Farm Based on PSO-LSTM-ECM Method
摘要
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.关键词
风电场/等值建模/深度学习/误差校正模型/长短期记忆神经网络Key words
wind farm/equivalent modeling/deep learning/error correction model/long short-term memory neural network分类
能源科技引用本文复制引用
朱清,蔡鹏程,朱卫卫,万芳茹,刘财华,周霞,陈学宽..基于PSO-LSTM-ECM的风电场等值建模方法[J].分布式能源,2025,10(3):11-22,12.基金项目
国家自然科学基金(重点项目)(61933005)This work is supported by National Natural Science Foundation of China(Key Program)(61933005) （重点项目）
