Adaptive torque feed-forward control for wind turbine MPPT considering predicted wind speed characteristics

• The tracking bandwidth of conventional OT control is studied. • An adaptive torque feed-forward control (ATFC) approach is proposed. • The gain in torque feed-forward path is adaptively scheduled by the mean wind speed and equivalent turbulence frequency. • ATFC achieves a better trade-off between the power improvement and torque fluctuations. The growing inertia exacerbates the conflict between the slow dynamic response of wind turbines and rapidly changing wind speed, thereby diminishing the effectiveness of maximum power point tracking (MPPT). Conventional optimal torque (OT) control exhibits limited MPPT tracking bandwidth under low wind speeds with high frequency due to its exclusive focus on steady-state performance and neglect of dynamic wind characteristics. In this regard, an adaptive feed-forward torque control (AFTC) approach is proposed to dynamically adjust the MPPT tracking bandwidth in response to wind variations. The approach integrates a Kalman observer for aerodynamic torque estimation and employs the Newton-Raphson method to derive real-time wind speed. A single exponential smoothing method predicts future mean wind speed and equivalent turbulence frequency. These predicted values adaptively schedule the feed-forward gain, enabling bandwidth adaptation without altering steady-state equilibrium. Comparative analyses with the conventional OT control and the typical feed-forward control demonstrate that ATFC achieves a better trade-off between the power improvement and torque fluctuations.

Authors

Publication Details

Journal
International Journal of Electrical Power & Energy Systems
Published
2025-03-17
DOI
https://doi.org/10.1016/j.ijepes.2025.110598
Citations
3
Primary Topic
Wind Turbine Control Systems
Type
article
Field-Weighted Citation Impact
2.19
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Adaptive torque feed-forward control for wind turbine MPPT considering predicted wind speed characteristics

Liangwen Qi, Songsong Wu, Min Zhao, Xiaohan Zhang et al.
3 citations
International Journal of Electrical Power & Energy Systems
Wind Turbine Control Systems
2.19
article

Adaptive torque feed-forward control for wind turbine MPPT considering predicted wind speed characteristics

Liangwen Qi, Songsong Wu, Min Zhao, Xiaohan Zhang, Yong Zhao, Wei Deng, Pengfei Meng
article en
3 citations

Abstract

• The tracking bandwidth of conventional OT control is studied. • An adaptive torque feed-forward control (ATFC) approach is proposed. • The gain in torque feed-forward path is adaptively scheduled by the mean wind speed and equivalent turbulence frequency. • ATFC achieves a better trade-off between the power improvement and torque fluctuations. The growing inertia exacerbates the conflict between the slow dynamic response of wind turbines and rapidly changing wind speed, thereby diminishing the effectiveness of maximum power point tracking (MPPT). Conventional optimal torque (OT) control exhibits limited MPPT tracking bandwidth under low wind speeds with high frequency due to its exclusive focus on steady-state performance and neglect of dynamic wind characteristics. In this regard, an adaptive feed-forward torque control (AFTC) approach is proposed to dynamically adjust the MPPT tracking bandwidth in response to wind variations. The approach integrates a Kalman observer for aerodynamic torque estimation and employs the Newton-Raphson method to derive real-time wind speed. A single exponential smoothing method predicts future mean wind speed and equivalent turbulence frequency. These predicted values adaptively schedule the feed-forward gain, enabling bandwidth adaptation without altering steady-state equilibrium. Comparative analyses with the conventional OT control and the typical feed-forward control demonstrate that ATFC achieves a better trade-off between the power improvement and torque fluctuations.

International Journal of Electrical Power & Energy SystemsVol. 167
Affordable and clean energy
Openalex Percentile: Top 13%
Wind Turbine Control Systems
2.19
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.