The continuous market push for lower levelized cost of energy (LCOE) and competitive return of investment requires wind power plants to be as cost-effective as possible while maintaining safety, performance, and reliability requirements. Degradation of surface quality of wind turbine blades, mainly due to leading edge erosion, has a major impact on wind farm reliability and revenue, due to an increase in energy production losses and maintenance costs. Furthermore, research and development on wind power plant operational optimization has shown potential benefits of the use of wind farm (flow) control. The aim of this work is to improve existing wind plant modelling and control approaches in the event of weather conditions that include the impact of precipitation and/or sand, as well as degraded blades, for reliable operation and optimized revenue. The approach considers the impact of precipitation/sand and associated erosion for turbines in wind farms with combined wind power plant and flow control techniques. An open-source integrated wind farm control toolbox coupled to either PyWake, FLORIS or FOXES wake modeling is presented, which includes the effects of erosion on the turbine(s) response. Apart from basic comparison test cases, a relevant test case is presented, highlighting the effects of erosion levels on wind farm performance, and the impact of erosion-safe control operation, also combined with wake steering flow control.
The developed open-source wind farm control toolbox simulates dynamic wind farm operation including the following features:
Control levels:
• Dynamic wind power plant controller including wind farm flow control capabilities, and power modes such as maximum power point tracking (MPPT), balance and delta modes.
• DTU WEC open-source wind turbine controller including yaw actuation and servos
System modeling:
• Wind turbine drivetrain dynamic model using tabular Cp/Ct=f(tip speed ratio, pitch, yaw, erosion-level)
• Wind power plant collection system considering cable losses
• Coupling with either PyWake, FLORIS or FOXES for quasi-dynamic wake effects with synthetic turbulence and wake advection
The presented toolbox highlights the potential benefits of a dynamic integrated wind farm control toolbox including erosion effects on the turbine response and multiple control levels. A relevant test case is presented, highlighting the impact of erosion level, erosion safe control mode and additional wind farm flow control in a realistic offshore inflow scenario for a reference wind farm of four IEA-15MW-RWT wind turbines at 5D distance. The inflow data comprises 48-hour offshore measurements from a Danish west coast site in the North Sea. The power levels and operational response of the turbines in the wind farm are compared, but also the influence of the different implementations of the wake models.
The results indicate that the loss of power due to erosion safe mode control can be regained with wake steering flow control. In the context of wind power plant design and operational optimization, these promising insights can be utilized for further optimization of wind farm operation with erosion and wind farm control, therefore adapting the plant operation to its actual health status.

Low-turbulence offshore conditions lead to stronger and more persistent wake losses, motivating the development of advanced control strategies for their mitigation. Wake steering control (WSC) has demonstrated strong potential for improving wind farm efficiency by deflecting wakes away from downstream turbines, albeit often at the expense of increased structural loads. In contrast, static induction control (SIC) has shown potential to alleviate loads, depending on the actuation approach and derating level, while yielding modest power gains in closely spaced layouts. Although WSC and SIC have been extensively investigated individually, their synergistic effect on wind turbine wakes has received limited attention. This study examines the potential of simultaneously applying WSC and SIC at a constant tip speed ratio (λ) to enhance wake management beyond the capabilities of either method alone. To this end, wind tunnel experiments were conducted in a single-turbine configuration employing hot-wire measurements to characterise its wake response. To isolate the effects of each control strategy, experiments were performed under uniform inflow conditions in the partial load region at 7 m/s, with turbulence intensity of 1.3 %. The hot-wire array consisted of 19 one-dimensional probes distributed in a horizontal line at hub height, spanning cross-stream positions between -1.25 ≤ y/D ≤ 1.25. Measurements were taken at downstream distances between 1 ≤ x/D ≤ 10. The test matrix included experiments using baseline greedy control, WSC with positive and negative yaw misalignments (ψ = ±20⁰), SIC implemented through pitch-to-stall (β = -2⁰, overinduction) and pitch-to-feather (β = +2⁰, underinduction), and their combinations.

To quantify wake recovery for each control strategy, the downstream evolution of rotor-averaged wind speed was computed assuming alignment with the inflow wind direction. This quantity represents a line-averaged wind speed at hub height, neglecting variations across the rotor-swept area but providing a useful indication. Compared to the baseline greedy mode, all control strategies improve wake recovery to some extent. Overinduction SIC provides the least benefit, with only marginal gains beyond x/D > 7, which would likely vanish owing to power losses at the derated turbine and increased structural loads. Interestingly, underinduction SIC shows similar improvement to negative yaw misalignment up to x/D = 6 but then drops due to slower turbulent recovery. Moreover, the simultaneous use of WSC with overinduction SIC boosts recovery for 4 ≤ x/D ≤ 8 with positive misalignment and for x/D ≥ 5.5 with negative misalignment, while reducing gains outside these ranges. The most promising results are obtained through simultaneous WSC with underinduction SIC, outperforming independent WSC with positive misalignment up to x/D = 5 and independent negative misalignment up to x/D = 8. This preliminary analysis highlights the prospect of the synergistic approach, primarily through WSC with underinduction SIC, in further optimising wind farm power while potentially alleviating loads on upstream and downstream turbines.

The presentation will include further analysis of the downstream development of turbulence and far-wake transition, spectral energy content, wake centre location, and implications for optimising wind farm performance in a two-turbine setup incorporating a virtual turbine.

Accurate, measurement-based, minute-scale forecasting can be utilised to improve wind farm control strategies, including proactive induction control and yaw steering, and enhance the reliability of ancillary services, such as the provision of operating reserve and frequency response. While operation strategies using turbine-data-based estimation are well established in the field, additional integration of LIDAR data to enhance forecasting horizon and accuracy is still an emerging technology under testing and evaluation. Dynamic flow models enable wind field reconstruction and prediction under turbulent inflow and transient wind conditions, including changes in wind speed, direction and atmospheric conditions, which have decisive influence on turbine operation and wake behaviour. Paired with on-line measurement data feeds, they can be incorporated to establish real-time-capable frameworks for power and load forecasting and model predictive control optimisation. In this study, LIDAR and SCADA data from an offshore wind farm measurement campaign are employed to evaluate and compare two dynamic wind farm flow models of distinct fidelity and functionality. An upstream scanning LIDAR provides data to characterise inflow and boundary conditions, while SCADA data and downstream scanning Dual-Doppler LIDARs are used for validation of the flow field and turbine power. In addition, model parameterisation is supported by Large Eddy Simulations of the same wind farm. The two models follow different approaches to integrate the LIDAR-based inflow data and to describe flow evolution and turbine operation. The first model relies on a 3D turbulence box as background field which is characterised by the available measurements and transported through the wind farm domain. Wakes are implemented by a dynamic wake meandering model with turbine-specific particle-based velocity deficit formulation, and wake superposition method. In contrast, the second model utilises the inflow information by generating parcels at the LIDAR sensing positions which carry measurement information downstream and constitute the background field via inverse distance weighting. Wakes are generated by an actuator disk model with thrust force distribution acting directly on the full, wake-influenced, model field, while the background field sets the reference level for local wake diffusion and dissipation. Various scenarios of atmospheric conditions, including different turbulence levels, wind directions and wind speeds, are selected to evaluate both models for certain sections of the wind farm.
Comparison to SCADA and downstream LIDAR measurements enables a joint assessment of power and flow prediction accuracy and resolution. While power time series analysis is used to quantify bias, uncertainty and event responsiveness, comparison to LIDAR measurements provide insights regarding the models' capabilities to represent wake intensity, orientation and recovery. The resulting evidence base supports guidance on case dependent model selection and calibration for operational use, demonstrates the extent to which dynamic scales can be resolved in practice, and highlights the trade‑off between accuracy and computational effort. By grounding the evaluation in real offshore data, this work advances the deployment of measurement‑informed dynamic flow models and contributes to the development of real‑time applications for LIDAR‑based forecasting in wind farm operation.