Hybrid Forecast-Enabled Adaptive Crowbar Coordination for LVRT Enhancement in DFIG Wind Turbines
Xianlong Su, Hankil Kim, Changsu Kim, Mingxue Zhang, Hoekyung Jung
TL;DR
This paper introduces a new adaptive crowbar strategy for wind turbines to improve their performance during low-voltage events using forecasting and control techniques.
Contribution
A hybrid forecast-enabled adaptive crowbar coordination strategy is proposed for enhanced LVRT in DFIG wind turbines.
Findings
The hybrid forecaster outperforms five baselines in forecasting accuracy metrics.
The strategy reduces DC-link voltage deviation and rotor current peaks during low-voltage events.
Reactive-support peaks are increased, improving grid-friendliness without hardware changes.
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.…
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Taxonomy
TopicsWind Turbine Control Systems · Energy Load and Power Forecasting · Wind Energy Research and Development