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Commonly, wake models are calibrated in wind tunnels or using flow simulations with a wide degree of physical details. In general, it is assumed that these methods cannot fully reproduce the real operating conditions of wind turbines. This research aims at investigating the calibration of an analytical single wake model in relation to full-scale measurements. Within this scope, we fitted the wake model to wake measurements realised with a lidar installed on the nacelle of an offshore wind turbine. We studied the parameters returned by the fit separating cases at different levels of atmospheric turbulence and thrust on the wind turbine rotor. Comparing the results with a published calibration based on few LES wind fields representative for partial load conditions, we achieved good agreement when the considered wind turbines operated in similar conditions. For other situations, i.e. at full load, we found different calibrations of the model parameters. Our results show that and how nacelle-based lidar measurements can be complementary in the development of wake models. [ABSTRACT FROM AUTHOR]

Lifetime evaluation with fatigue loads is commonly used in the design phase of wind turbines, but rarely during operation due to the cost of extra measurements. Fatigue load prediction with neural networks, using existing SCADA signals, is a potential cost-effective alternative to continuously monitor lifetime consumption. However, although assessments for turbines in wind farm flow have been pointed out as deficient, the evaluations were limited to single cases and the implication for the design of a monitoring system was not discussed. Hence, we proposed metrics to evaluate prediction quality and, using one year of measurements at two wind turbines, we evaluated predictions in six different flow conditions. The quality of fatigue load predictions were evaluated for bending moments of two blades, in edgewise and flapwise directions. Results, based on 48 analyses, demonstrated that prediction quality varies marginally with varying flow conditions. Predictions were accurate in all cases and had an average error below 1.5%, but their precision slightly deteriorated in wake flow conditions. In general, results demonstrated that a reasonable monitoring system can be based on a neural network model without the need to distinguish between inflow conditions. [ABSTRACT FROM AUTHOR]

Advanced model-based control of wind turbines requires knowledge of the states and the wind speed. This paper benchmarks a nonlinear Takagi–Sugeno observer for wind speed estimation with enhanced Kalman Filter techniques: The performance and robustness towards model-structure uncertainties of the Takagi–Sugeno observer, a Linear, Extended and Unscented Kalman Filter are assessed. Hence the Takagi–Sugeno observer and enhanced Kalman Filter techniques are compared based on reduced-order models of a reference wind turbine with different modelling details. The objective is the systematic comparison with different design assumptions and requirements and the numerical evaluation of the reconstruction quality of the wind speed. Exemplified by a feedforward loop employing the reconstructed wind speed, the benefit of wind speed estimation within wind turbine control is illustrated. [ABSTRACT FROM AUTHOR]

The use of statistics from standard SCADA signals as inputs to monitor fatigue loads in wind turbines is a promising alternative to its continuous measurement with extra sensors and is the topic of this contribution. One of the fundamental challenges is the selection of input variables, a problem reflected by the various set of predictors used in previous investigations; a situation that hinders the generalization of the methodology. This work attempts to establish a single optimum set. First, it evaluates the impact of using different methods to select input variables on the accuracy of their predictions and then performs statistical tests on their residuals to establish a selection criterion between sets. It discusses the findings and relates the similarities and differences to the high cross-correlation found in the set of candidates. Finally, it drafts recommendations for the development of a robust system. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a model predictive control (MPC) is proposed for wind farms to minimize wake-induced power losses. A constrained optimization problem is formulated to maximize the total power production of a wind farm. The developed controller employs a two-dimensional dynamic wind farm model to predict wake interactions in advance. An adjoint approach as an efficient tool is utilized to compute the gradient of the performance index for such a large-scale system. The wind turbine axial induction factors are considered as the control inputs to influence the overall performance by taking the wake interactions into account. A layout of a 2 × 3 wind farm is considered in this study. The parameterization of the controller is discussed in detail for a practical optimal energy extraction. The performance of the adjoint-based model predictive control (AMPC) is investigated with time-varying changes in wind direction. The simulation results show the effectiveness of the proposed approach. The computational complexity of the developed AMPC is also outlined with respect to the real time control implementation. [ABSTRACT FROM AUTHOR]

In terms of predicting wind turbine wakes, the stably stratified atmospheric boundary layer (SABL) is taking an exceptional position as wake effects and thus loads on subsequent turbines are stronger. In this study we show the impact of the SABL on power production and wake effects (power deficits) in offshore wind farms by means of measurements as well as large-eddy simulations (LES). Measurements show enhanced wake effects in the SABL compared to the unstable situation. Another influence on the power generation of an offshore wind farm is the distance of the wind farm to the shore. This is accounted for in the LES by a modification of surface characteristics at the coastal discontinuity. In addition to the effect of the coast, the numerical case study also shows the existence of local jets between the turbines of the wind farm. [ABSTRACT FROM AUTHOR]

Highlights: [•] Vanadium redox flow battery utilized for wind power grid integration was studied. [•] Technical and financial analyses at single wind farm level were performed. [•] 2MW/6MWh VRFB is suitable for mitigating power deviations for a 10MW wind farm. [•] Economic incentives might be required in the short-term until the VRFB prices drop. [ABSTRACT FROM AUTHOR]

Owing to the aerodynamic conversion process, wind turbines exhibit a nonlinear behavior and encounter turbulent operating conditions that demand well-defined closed-loop dynamics to withstand accumulated loading over the lifetime. Wind excitation is the main disturbance and driving force for the system and determines the necessary operating strategy, but it usually represents an unmeasurable quantity. In this study, we used a linear-matrix-inequalities-based control and observer design to operate a variable-speed, variable-pitch wind turbine in a wind tunnel experiment at different reproducible inflow conditions while relying on a wind speed estimate obtained from a disturbance observer. The computational complexity of the stability framework incorporating the reconstruction of the unknown wind speed is reduced by exploiting characteristics of the modeling approach based on a convex combination of linear submodels. The assumption used in the proposed stability consideration is evaluated based on measurement data. We introduced an extended operating range compared to the commonly considered operating trajectory of wind turbines in the control design. A controller based on Takagi–Sugeno modeling is used to operate the turbine at challenging power tracking requirements demonstrating the capability to support fast stabilization of the electrical grid while discussing the loading and operational constraints observed during the experiments. [Display omitted] • Nonlinear model-based control design using linear matrix inequalities was performed. • The control design was applied to a wind turbine to provide power tracking capability. • Proof of stability of the observer-based strategy considering unmeasurable wind speed was realized. • Experimental wind tunnel validation in a large operational range under reproducible turbulent inflow conditions was attained. [ABSTRACT FROM AUTHOR]