Adaptive real-time wind farm control based on ultra-short-term wind prediction and wake dynamics
Year: 2026
Type: article
Abstract: The highly random and time-varying nature of wind conditions underscores the need for accurate short-term wind prediction and dynamic wake characterization in real-time wind farm control. This paper presents a novel real-time cooperative control system that integrates data-driven short-term wind speed predictions into a dynamic control framework. The prediction accuracy of the fractal interpolation extrapolation method is evaluated using measured wind data, and a six-turbine wind farm is selected to assess the system’s performance relative to traditional methods. Additionally, a feasibility analysis is conducted with various control intervals considered. Simulation results demonstrate that the improved fractal interpolation prediction method excels in short-term wind speed prediction, with a prediction error of only 5.65%. Dynamic wind farm control based on fractal interpolation prediction significantly outperforms control based on commonly used look-ahead data in terms of power benefits. However, this advantage is diminished in steady-state wind farm control. The proposed control system achieves a power increase of 3.233%, approaching the theoretical optimum, and substantially surpasses the 0.414% increase achieved by traditional steady control based on look-ahead data. The performance of the proposed control system remains robust until the error from fractal interpolation prediction, which accumulates with the control interval, reaches a certain threshold.
Source: Renewable Energy
Language: English
Topic: Wind Turbine Control Systems
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,460
OpenAlex ID: W7135397915