Wind farms face a fundamental challenge: turbines positioned downwind of others operate in weakened flow conditions created by upstream machines, reducing their power output. Two established techniques address this problem differently, and new research shows combining them yields superior results.
Wake steering involves yawing upstream turbines to redirect wake flows away from downstream machines, sacrificing some local output to benefit the farm as a whole. The helix method, a newer approach, enhances wake recovery by increasing turbulent mixing between the wake and surrounding free-stream air, accelerating flow restoration without requiring yaw adjustments.
Researchers at Dutch and international institutions studied a 69-turbine offshore wind farm configuration using IEA 22 MW turbines. They developed the multi-strategy serial-refine (MSR) optimization algorithm, which allows each turbine to independently select either wake steering or helix mixing based on real-time conditions.
Key findings showed the hybrid strategy increased annual energy production more than either single method. This advantage grew when multiple downstream turbines faced misaligned flow, a common scenario in large farms. Critically, the combined approach proved more robust under wind direction uncertainty—wake steering's effectiveness drops significantly when wind direction predictions prove inaccurate, whereas the helix method maintains consistent gains.
The research employed engineering wake models suitable for large-scale farm simulation, balancing computational speed with physical accuracy. Results remained consistent across multiple wind direction uncertainty scenarios, suggesting practical applicability.
These findings have immediate relevance for offshore wind operators seeking to maximize energy yields from existing infrastructure. Rather than committing exclusively to one control philosophy, operators could implement dynamic switching logic that assesses local flow conditions and selects the optimal strategy. This flexibility could generate additional revenue without hardware modifications, supporting the economic case for wind farm control systems in an increasingly competitive renewable energy market.
