Hybrid Forecast-Enabled Adaptive Crowbar Coordination for LVRT Enhancement in DFIG Wind Turbines
Year: 2026
Type: article
Abstract: This study proposes a hybrid forecast-enabled adaptive crowbar coordination strategy to enhance low-voltage ride-through (LVRT) performance of doubly fed induction generator (DFIG) wind turbines. A unified electro-mechanical model in the αβ/dq frames with dual closed-loop control for rotor- and grid-side converters is built in MATLAB/Simulink (R2018b), and LVRT constraints on current safety and DC-link energy are explicitly formulated, yielding an engineering crowbar-resistance range of 0.4–0.8 p.u. On the forecasting side, a CEEMDAN-based decomposition–modeling–reconstruction pipeline is adopted: high- and mid-frequency components are predicted by a dual-stream Informer–LSTM, while low-frequency components are modeled by XGBoost. Using six months of wind-farm data, the hybrid forecaster achieves best or tied-best MSE, RMSE, MAE, and R2 compared with five representative baselines. Forecasted power, ramp rate, and residual-based uncertainty are mapped to overcurrent and DC-link overvoltage risk indices, which adapt crowbar triggering, holding, and release in coordination with converter control. In a 9 MW three-phase deep-sag scenario, the strategy confines DC-link voltage within ±3% of nominal, shortens re-synchronization from ≈0.35 s to ≈0.15 s, reduces rotor-current peaks by ≈5.1%, and raises the reactive-support peak to 1.7 Mvar, thereby improving LVRT safety margins and grid-friendliness without hardware modification.
Source: Entropy
DOI: 10.3390/e28020138
Authors: Xianlong Su, Hankil Kim, Changsu Kim, Mingxue Zhang, Hoekyung Jung
Institutions: Pai Chai University, Chongqing University of Technology
Language: English
Topic: Wind Turbine Control Systems
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G4700831490
OpenAlex ID: W7125677589