In the context of wind turbine pitch control for load alleviation or active wake mixing, it is relevant to provide the time- and space-varying wind conditions as an input to the controller. Apart from classical wind measurement techniques, blade-load-based estimators can also be used to sense the incoming wind. These consider blades to be sensors of the flow and rely on having access to the operating parameters and measuring the blade loads. In this paper, we wish to verify how robust such estimators are to the control strategy active on the turbine, as it impacts both operating parameters and loads. We use an extended Kalman filter (EKF) to estimate the incoming wind conditions based on the blade bending moments. The internal model in the EKF relies on the blade element momentum (BEM) theory in which we propose accounting for delays between pitch action and blade loads by including dynamic effects. Using large-eddy simulations (LESs) to test the estimator, we show that accounting for the dynamic effects in the BEM formulation is needed to maintain the estimator accuracy when dynamic wake mixing control is active.

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Wind turbines in farms face challenges such as reduced power output and increased loading when their rows align with the wind direction - a phenomenon known as the wake effect. To address this issue, dynamic induction control has been proposed, which involves dynamically adjusting the induction of upstream turbines to enhance the mixing of the wake with the free stream. As a continuation of this method, downstream turbines could potentially leverage the periodic structure in the upstream turbines' wake to improve power production further downstream by synchronizing their dynamic induction control actions. This study investigates the potential of such an approach using a three-turbine scaled setup in a wind tunnel. The findings reveal that synchronization not only improves wake mixing downstream but also results in a substantial power gain on the synchronizing turbine, suggesting potential for a synchronization controller.

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To justify the use of two single-input single-output (SISO) control loops instead of more complex multi-input multi-output (MIMO) control, the axes in a wind turbine's pitch control system should be fully decoupled using the multi-blade coordinate transform. To achieve that, usually, an azimuth offset is required, correcting for phase lags originating from, e.g., actuator delays and blade flexibility. In wind turbine simulations, this parameter is commonly obtained by analysis of the linearized turbine models. This work, however, demonstrates that analyzing linearized turbine models is not sufficient for correcting the full phase lag when coupling wind turbine simulation tools to large-eddy simulators (LES), since additional phase lags may arise. Instead, this work proposes deriving the azimuth offset using data-driven modelling directly in coupled LES, where data is generated by exciting the structure with pseudo-random binary noise. Using this approach it was found that the optimal azimuth offset is three degrees higher than when using the linearized model, which demonstrates that deriving the optimal azimuth offset from linearized models is not suitable for coupled simulations.

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The performance of wind farms can substantially increase when their individual turbines deviate from their own greedy control strategy and instead also take into account downstream turbines operating in the wake. The helix approach is a recently introduced dynamic wind farm control strategy that tackles this issue by leveraging individual pitch control to accelerate wake recovery. Its effective implementation requires detailed knowledge about the scaling between control input and the resulting power gain and turbine loading across the farm. In the present work this scaling is explored by means of large-eddy simulation of a two-turbine farm in the conventionally neutral atmospheric boundary layer. A parameter sweep for the amplitude of the helix is performed showing monotonous increase of the farm's power output with increasing pitch amplitude within the considered range of zero to six degrees. The scaling of the power gain suggests that a threshold amplitude should be exceeded for effective speed-up of the wake recovery, whereas the damage equivalent loads computed for the turbines indicate an upper limit for the amplitude despite increasing power gains.

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Operational modal analysis (OMA) is an essential tool for understanding the structural dynamics of offshore wind turbines (OWTs). However, the classical OMA algorithms require the excitation of the structure to be stationary white noise, which is often not the case for operational OWTs due to the presence of periodic excitation caused by rotor rotation. To address this issue, several solutions have been proposed in the literature, including the Kalman filter-based stochastic subspace identification (KF-SSI) method which eliminates harmonics through estimation and orthogonal projection. In this paper, an enhanced version of the KF-SSI method is presented that involves a concatenation step, allowing multiple datasets with similar environmental conditions to be used in the identification process, resulting in higher precision. This enhanced framework is applied to an operational OWT and compared to other OMA methods, such as the modified least-squares complex exponential and PolyMAX. Using field data from a multi-megawatt operational OWT, it is shown that the enhanced framework is able to accurately distinguish the first three bending modes with more stable estimates and lower variance compared to the original KF-SSI algorithm and follows a similar trend compared to other approaches.

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The Helix approach is a control technology that reduces the wake effect in wind farms by accelerating wake mixing through individual pitch control, resulting in significant AEP gain. However, this study found that depending on its settings, the controller may increase pitch bearing damage and loads on some turbine components. Using a modified version of NREL’s Reference OpenSource Controller in OpenFAST, this study analysed the sensitivity of loads and pitch bearing damage to different Helix controller settings on the IEA-15MW reference offshore wind turbine. Results showed that loads increased with the excitation signal amplitude but were less affected by its frequency. Additionally, more pitch bearing damage was observed in the counterclockwise Helix direction, while slightly higher loads were observed in the clockwise direction when using the same excitation signal amplitude and frequency for both directions.@en

Wind farm controllers such as the Helix approach have shown potential in increasing plant power production through wake mixing. The concept suggests that actuating the upstream turbines' blade pitching with a specific periodic signal can induce a helix-shaped wake, thereby alleviating wind velocity deficit on downstream turbines. Wake mixing initiation by downstream turbines may also be shown advantageous for power production; however, little to no attention has been given to such an approach. Similar wake mixing is expected to be achievable at lower control costs if the downstream turbine can benefit from the periodic component already present in the wake of the upstream turbine. Such a hypothesis is studied in this work by designing a minimal control scheme where the wake acting on the downstream turbine is simulated by a periodic input disturbance. A Kalman filter is proposed for incoming input disturbance phase estimation using SCADA data. The reconstructed phase information allows synchronization of the downstream control action with the periodic input disturbance by means of a phase synchronization wake mixing controller. The periodic component was estimated with a minimal root-mean-square error and the resulting control action was in phase with the input disturbance and demonstrated satisfactory performance even with a small phase perturbation. Future work will include applications in a high-fidelity wind turbine model and wind tunnel studies.

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To increase the contribution of offshore wind energy to the global energy mix in an economically sustainable manner, it is required to reduce the costs associated with the production and operation of offshore wind turbines (OWTs). One of the largest uncertainties and sources of conservatism in design and lifetime prediction for OWTs is the determination of the global damping level of the OWT. Estimation of OWT damping based on field measurement data has hence been subject to considerable research attention and is based on the use of (preferably operational) vibration data obtained from sensors mounted on the structure. As such, it is an output-only problem and can be addressed using state-of-the-art operational modal analysis (OMA) techniques, reviewed in this paper. The evolution of classical time- and frequency-domain OMA techniques has been reviewed; however, the literature shows that the OWT vibration data are often contaminated by rotor speed harmonics of significantly high energy located close to structural modes, which impede classical damping identification. Recent advances in OMA algorithms for known or unknown harmonic frequencies can be used to improve identification in such cases. Further, the transmissibility family of OMA algorithms is purported to be insensitive to harmonics. Based on this review, a classification of OMA algorithms is made according to a set of novel suitability criteria, such that the OMA technique appropriate to the specific OWT vibration measurement setup may be selected. Finally, based on this literature review, it has been identified that the most attractive future path for OWT damping estimation lies in the combination of uncertain non-stationary harmonic frequency measurements with statistical harmonic isolation to enhance classical OMA techniques, orthogonal removal of harmonics from measured vibration signals, and in the robustification of transmissibility-based techniques.@en