Evaluation of a wind speed estimator for effective hub-height and shear components

Abstract

Estimates of the effective wind speed disturbances acting on a wind turbine are useful in a variety of control applications. With some simplifications, it is shown that for zero yaw error, any wind field interacting with a turbine can be equivalently described using a hub-height (uniform) component as well as linear horizontal and vertical shear components. A Kalman filter-based wind speed estimator is presented for estimation of these effective hub-height and shear components. The wind speed estimator is evaluated in the frequency domain using the FAST aeroelastic simulator with the National Renewable Energy Laboratory's 5 MW reference wind turbine model and realistic hub-height and shear disturbances. In addition, the impact of the inflow model, used to simulate the rotor aerodynamics, on the Kalman filter performance is investigated. It is found that the estimator accuracy strongly depends on the inflow model used. In general, the estimator performs well up to a bandwidth of 1 Hz when the inflow model used for simulation matches the model used to create the linear Kalman filter model and blade pitch angle remains close to the linearization operating point. However, inaccuracies in the linear model of the turbine when dynamic inflow is used for simulation as well as nonlinearities in the turbine dynamics due to blade pitch actuation cause performance to degrade. Finally, the improvement gained by employing a non-causal wind speed estimator is assessed, showing a minor increase in performance.

Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

“…For estimation of U ∞ in above-rated conditions, one may, for example, require the 15 implementation of a wind speed estimator on each individual turbine, from which the local wind speed in front of each turbine can be estimated, as demonstrated by Simley and Pao (2016) Combining these elements yields an efficient, modular, and accurate model calibration solution for low-and medium-fidelity dynamic wind farm models. The natural model states are estimated using SCADA and/or LIDAR data inside a wind farm, of which the former is readily available, and the latter becoming more popular.…”

“…While some of these models have shown success in wind tunnel tests (e.g., Schreiber et al, 2017) and field tests (e.g., Fleming et al, 2017a, b) for power maximization, the actuation frequency is limited to the minutes-scale, since the flow and turbine dynamics are predicted on the minute-scale. Furthermore,5 time-ahead predictions with these models are limited to the time-invariant steady-state, limiting their use for APC. 1 There is a smaller yet significant number of dynamic surrogate wind farm models (e.g., Munters and Meyers, 2017;Boersma et al, 2017b;Shapiro et al, 2017a), which attempt to model the dominant temporal dynamics inside the farm.…”

Wind inflow observation from load harmonics

“…In principle, the resulting input-output relationship should also include the dependency on other parameters, such as blade pitch and rotor speed, as shown for example in Simley and Pao (2014). However, all these quantities depend in turn on the environmental and operating conditions according to the particular regulation strategy adopted by the onboard controller.…”

“…Therefore, in this work the model is assumed to depend only on θ , V and . Vector θ is to be estimated with the proposed observer, while V , which is a scheduling parameter for the model, can either be measured or observed using a rotor-equivalent wind speed estimator (Soltani et al, 2013;Simley and Pao, 2014;Bottasso et al, , 2018. This approach leads to a white box model, i.e., a model using analytical formulas to express relationships among the relevant variables based on physical principles (Ljung, 2010).…”

“…Following the idea described in Bottasso et al (2010), an estimator based on a linearized wind turbine model was proposed in Simley and Pao (2014). The formulation used generator speed, fore-aft nacelle acceleration and collective cosine and sine components of the blade out-of-plane bending moments to estimate, with a Kalman filter, the equivalent wind speed together with the linear vertical and horizontal shears.…”