Equivalent method for DFIG wind farms based on modified LightGBM considering voltage deep drop faults

作者：Xuecheng Liu, Peixiao Fan, Jun Yang, Ke Song, Binyu Ma, Yangzhou Pei, Jian Xu · 发表于：International Journal of Electrical Power & Energy Systems · 年份：2025 · DOI：10.1016/j.ijepes.2025.110451 · 被引用次数：11 · 研究领域：Wind Turbine Control Systems、Islanding Detection in Power Systems、Power Systems and Renewable Energy

• Construction of complex operational scenarios considering partial trip-off of wind turbines. • Establishment of a wind turbine operational states identification model based on the mLightGBM. • Proposal of a two-stage clustering method driven by a data-model hybrid approach. To address the challenges of inadequate accuracy in identifying the Crowbar action state and incomplete consideration of operating scenarios in existing methods for Doubly Fed Induction Generator (DFIG) wind farms, a DFIG wind farm equivalent method based on modified Light Gradient Boosting Machine (mLightGBM) considering voltage deep drop faults is proposed. First, the low voltage ride through process of wind turbines is analysed, with particular consideration given to the scenario of partial wind turbines tripping off due to voltage deep drop faults in the wind farm. The factors influencing the Crowbar action state and trip-off state of wind turbines are identified, and a feature vector for wind turbine operating states is constructed. Second, based on simulations to obtain sample data of wind turbine operating states under different operating scenarios in wind farms, a classification model based on mLightGBM is established. Different weights are assigned to various samples and hyperparameter optimization is conducted to enhance the model’s classification accuracy. Finally, a two-stage clustering method driven by a data-model hybrid approach is proposed. Under specific operating conditions, wind turbin...