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Leading-edge erosion (LEE) can significantly impact the aerodynamic performance of wind turbines and thereby the overall efficiency of a wind farm. Typically, erosion is modeled for individual turbines where aerodynamic effects only impact the energy production through degraded power curves. For wind farms, aerodynamic deficiency has the potential to also alter wake dynamics, which will affect the overall energy production. The objective of this study is to demonstrate this combined effect by coupling LEE damage prediction and aerodynamic loss modeling with steady-state wind farm flow modeling. The modeling workflow is used to simulate the effect of LEE on the Horns Rev 1 wind farm. Based on a 10-year simulation, the aerodynamic effect of LEE was found to be insignificant for the first few years of operation but rapidly increases and reaches a maximum annual energy production (AEP) loss of 2.9 % in the last year for a single turbine. When including the impact of LEE to the wakes behind eroded turbines, the AEP loss is seen to reduce to 2.7 % at the wind farm level, i.e., corresponding to an overestimation of the AEP loss of up to 7 % when only considering a single wind turbine. In addition, it was demonstrated that the modeling framework can be used to prioritize turbines for an optimal repairing strategy.

With the increasing growth of wind farm installations, the impact of wake effects caused by wind turbines on power output, structural loads, and revenue has become more relevant than ever. Consequently, there is a need for precise simulation tools to facilitate efficient and cost-effective design and operation of wind farms. To address this need, we present HAWC2Farm, a dynamic and versatile aeroelastic wind farm simulation methodology that combines state-of-the-art engineering models to accurately capture the complex physical phenomena in wind farms. HAWC2Farm employs the aeroelastic wind turbine simulator, HAWC2, to model each individual turbine within the wind farm. It utilises a shared, large-scale turbulence box to represent atmospheric flow field effects at the farm level. The methodology incorporates a modified version of the dynamic wake meandering model to accurately capture wake interactions. This approach not only ensures computational efficiency but also provides valuable insights for wind farm design and operation. To assess its performance, HAWC2Farm is compared using time series extracted from field measurements at the Lillgrund wind farm, encompassing various scenarios involving wake steering via yaw control and a turbine shutdown. The results indicate that HAWC2Farm effectively addresses the challenges associated with modelling the complex dynamics within wind farms, thereby enabling more precise, informed, and cost-effective design and operation strategies.

Modeling leading-edge erosion has been a challenging task due to its multidisciplinary nature involving several variables such as weather conditions, blade coating properties, and operational characteristics. While the process of wind turbine blade erosion is often described by engineering models that rely on the well-known Springer model, there is a glaring need for modeling approaches supported by field data. This paper presents a data-driven framework for modeling erosion damage based on blade inspections from several wind farms in northern Europe and mesoscale numerical weather prediction (NWP) models. The outcome of the framework is a machine-learning-based model that can be used to predict and/or forecast leading-edge erosion damage based on weather data/simulations and user-specified wind turbine characteristics. The model is based on feedforward artificial neural networks utilizing ensemble learning for robust training and validation. The model output fits directly into the damage terminology used by industry and can therefore support site-specific planning and scheduling of repairs as well as budgeting of operation and maintenance costs.

The EU and UK have made ambitious commitments under the net-zero plans to decarbonise their economies by 2050. For this, offshore wind will play a major role, significantly contributing to a paradigm shift in the power generation and greater volatility of electricity prices. The operating strategy of wind farms should therefore move from power maximisation to profit maximisation which includes income from providing power system services and the reduction of maintenance costs. Wind farm flow control (WFFC) is a key enabler for this shift through mitigation of wake effects in the design and operation phases. The results of the FarmConners market showcases presented here are the first attempt to economically assess WFFC strategies with respect to electricity market prices. Here, we present a conceptual simulation study starting from individual turbine control and extend it to layouts with 10 and 32 turbines operated with WFFC based on the results of five participants. Each participant belonged to a different research group with their respective simulation environments, flow models and WFFC strategies. Via a comparative analysis of relative WFFC benefits estimated per participant, the implications of wind farm size, the applied control strategy and the overall model fidelity are discussed in zero-subsidy scenarios. For all the participants, it is seen that the income gain can differ significantly from the power gain depending on the electricity price under the same inflow, and a favourable control strategy for dominant wind directions can pay off even for low electricity prices. However, a strong correlation between income and power gain is also observed for the analysed high-electricity-price scenarios, underlining the need for additional modelling capabilities to carry out a more comprehensive value optimisation including lower prices and system requirements driven cases.

Wind farm control has been a topic of research for more than two decades. It has been identified as a core component of grand challenges in wind energy science to support accelerated wind energy deployment and to transition to a clean and sustainable energy system for the 21st century. The prospect of collective control of wind turbines in an array, to increase energy extraction, reduce structural loads, improve the balance of systems, reduce operation and maintenance costs, etc. has inspired many researchers over the years to propose innovative ideas and solutions. However, practical demonstration and commercialization of some of the more advanced concepts has been limited by a wide range of challenges, which include the complex physics of turbulent flows in wind farms and the atmosphere, uncertainties related to predicting structural load and failure statistics, and the highly multi-disciplinary nature of the overall design optimization problem, among others. In the current work, we aim at providing a comprehensive overview of the state of the art and outstanding challenges, thus identifying the key research areas that could further enable commercial uptake and success of wind farm control solutions. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight in control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design (co-design).

Wind farm flow control (WFFC) is a topic of interest at several research institutes and industry and certification agencies worldwide. For reliable performance assessment of the technology, the efficiency and the capability of the models applied to WFFC should be carefully evaluated. To address that, the FarmConners consortium has launched a common benchmark for code comparison under controlled operation to demonstrate its potential benefits, such as increased power production. The benchmark builds on available data sets from previous field campaigns, wind tunnel experiments, and high-fidelity simulations. Within that database, four blind tests are defined and 13 participants in total have submitted results for the analysis of single and multiple wakes under WFFC. Here, we present Part I of the FarmConners benchmark results, focusing on the blind tests with large-scale rotors. The observations and/or the model outcomes are evaluated via direct power comparisons at the upstream and downstream turbine(s), as well as the power gain at the wind farm level under wake steering control strategy. Additionally, wake loss reduction is also analysed to support the power performance comparison, where relevant. The majority of the participating models show good agreement with the observations or the reference high-fidelity simulations, especially for lower degrees of upstream misalignment and narrow wake sector. However, the benchmark clearly highlights the importance of the calibration procedure for control-oriented models. The potential effects of limited controlled operation data in calibration are particularly visible via frequent model mismatch for highly deflected wakes, as well as the power loss at the controlled turbine(s). In addition to the flow modelling, the sensitivity of the predicted WFFC benefits to the turbine representation and the implementation of the controller is also underlined. The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of farm flow control benefits. It forms an important basis for more detailed benchmarks in the future with extended control objectives to assess the true value of WFFC.

In order to assess the level of power reserves during down-regulation, the available power of a wind turbine needs to be estimated. The current practice in available power estimation is heavily dependent on the pre-defined performance parameters of the turbine and the curtailment strategy followed. This paper proposes a single-input model-free approach dynamic estimation of the available power using recurrent neural networks. Accordingly, it combines wind turbine control considerations and modern forecasting methodologies for a model-free, single-input estimation of available power. It enables a robust real-time implementation of dynamic delta control, as well as higher-accuracy provision of the reserves to the system operators. The model-free approach requires only 1 Hz wind speed measurements as input and estimates 1 Hz available power as output. The neural network is trained, tested and validated using the DTU 10 MW reference wind turbine HAWC2 model under realistic atmospheric conditions. The unsteady patterns in the turbulent flow are represented via long short-term memory (LSTM) neurons which are trained during a period of normal operation. The adaptability of the network to changing inflow conditions is ensured via transfer learning, where the last LSTM layer is updated using new measurements. It is seen that the sensitivity of the networks to changing wind speed is much higher than that of turbulence, and the updates are to be implemented solely based on the altering inflow velocity. The validation of the trained LSTM networks on time series with 7, 9 and 11 m s−1 mean wind speeds demonstrates high accuracy (less than 1 % bias) and capability of transfer-learning online. Including highly turbulent inflow cases, the networks have shown to comply with the most recent grid codes, which require the quality of the available power estimations to be evaluated with high accuracy (less than 3.3 % standard deviation of the error around zero bias) at 1 min intervals.

This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion (PCE), are built using high-fidelity flow simulations coupled with aeroelastic simulations of the turbine performance and loads. Developing a model for wind farm control is a challenging control problem due to the time-varying dynamics of the wake. The wind farm control strategy is optimized for both the power output and the loading of the turbines. The optimization performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggests that a power gain of almost 3%±1% can be achieved at close spacing by yawing the upstream turbine more than 15∘. At larger spacing the optimization shows that yawing is not beneficial as the optimization reverts to normal operation. Furthermore, it was also identified that a reduction in the equivalent loads was obtained at the cost of power production. The total power gains are discussed in relation to the associated model errors and the uncertainty of the surrogate models used in the optimization, as well as the implications for wind farm control.

In this paper, a new calculation procedure to improve the accuracy of the Jensen wake model for operating wind farms is proposed. In this procedure, the wake decay constant is updated locally at each wind turbine based on the turbulence intensity measurement provided by the nacelle anemometer. This procedure was tested against experimental data at the Sole du Moulin Vieux (SMV) onshore wind farm in France and the Horns Rev-I offshore wind farm in Denmark. Results indicate that the wake deficit at each wind turbine is described more accurately than when using the original model, reducing the error from 15 % to 20 % to approximately 5 %. Furthermore, this new model properly calibrated for the SMV wind farm is then used for coordinated control purposes. Assuming an axial induction control strategy, and following a model predictive approach, new power settings leading to an increased overall power production of the farm are derived. Power gains found are on the order of 2.5 % for a two-wind-turbine case with close spacing and 1 % to 1.5 % for a row of five wind turbines with a larger spacing. Finally, the uncertainty of the updated Jensen model is quantified considering the model inputs. When checked against the predicted power gain, the uncertainty of the model estimations is seen to be excessive, reaching approximately 4 %, which indicates the difficulty of field observations for such a gain. Nevertheless, the optimized settings are to be implemented during a field test campaign at SMV wind farm in the scope of the national project SMARTEOLE.