Blade-pitch Control for Wind Turbine Load Reductions

Large wind turbines are subjected to the harmful loads that arise from the spatially uneven and temporally unsteady oncoming wind. Such loads are the known sources of fatigue damage that reduce the turbine operational lifetime, ultimately increasing the cost of wind energy to the end users. In recent years, a substantial amount of studies has focused on blade pitch control and the use of real-time wind measurements, with the aim of attenuating the structural loads on the turbine blades and rotor. However, many of the research challenges still remain unsolved. For example, there exist many classes of blade individual pitch control (IPC) techniques but the link between these different but competing IPC strategies was not well investigated. In addition, another example is that many studies employed model predictive control (MPC) for its capability to handle the constraints of the blade pitch actuators and the measurement of the approaching wind, but often, wind turbine control design specifications are provided in frequency-domain that is not well taken into account by the standard MPC. To address the missing links in various classes of the IPCs, this thesis aims to investigate and understand the similarities and differences between each of their performance. The results suggest that the choice of IPC designs rests largely with preferences and implementation simplicity. Based on these insights, a particular class of the IPCs lends itself readily for extracting tower motion from measurements of the blade loads. Thus, this thesis further proposes a tower load reduction control strategy based solely upon the blade load sensors. To tackle the problem of MPC on wind turbines, this thesis presents an MPC layer design upon a pre-determined robust output-feedback controller. The MPC layer handles purely the feed-forward and constraint knowledge, whilst retaining the nominal robustness and frequency-domain properties of the pre-determined closed-loop. Thus, from an industrial perspective, the separate nature of the proposed control structure offers many immediate benefits. Firstly, the MPC control can be implemented without replacing the existing feedback controller. Furthermore, it provides a clear framework to quantify the benefits in the use of advance real-time measurements over the nominal output-feedback strategy

Active power dispatch for supporting grid frequency regulation in wind farms considering fatigue load

This paper proposes an active power control method for supporting grid frequency regulation in wind farms (WF) considering improved fatigue load sensitivity of wind turbines (WT). The control method is concluded into two parts: frequency adjustment control (FAC) and power reference dispatch (PRD). On one hand, the proposed Fuzzy-PID control method can actively maintain the balance between power generation and grid load, by which the grid frequency is regulated when plenty of winds are available. The fast power response can be provided and frequency error can be reduced by the proposed method. On the other hand, the sensitivity of the WT fatigue loads to the power references is improved. The explicit analytical equations of the fatigue load sensitivity are re-derived to improve calculation accuracy. In the process of the optimization dispatch, the re-defined fatigue load sensitivity will be used to minimize fatigue load. Case studies were conducted with a WF under different grid loads and turbulent wind with different intensities. By comparing the frequency response of the WF, rainflow cycle, and Damage Equivalent Load (DEL) of the WT, the efficacy of the proposed method is verified

Fundamental performance similarities between individual pitch control strategies for wind turbines.

The use of blade individual pitch control (IPC) offers a means of reducing the harmful turbine structural loads that arise from the uneven and unsteady forcing from the oncoming wind. In recent years two different and competing IPC techniques have emerged that are characterised by the specific loads that they are primarily designed to attenuate. In the first instance, methodologies such as single-blade control and Clarke Transform-based control have been developed to reduce the unsteady loads on the rotating blades, whilst tilt-yaw control and its many variants instead target load reductions in the non rotating turbine structures, such as the tower and main bearing. Given the seeming disparities between these controllers, the aim of this paper is to show the fundamental performance similarities that exist between them and hence unify research in this area. Specifically, we show that single-blade controllers are equivalent to a particular class of tilt-yaw controller, which itself is equivalent to Clarke~Transform-based control. This means that three architecturally dissimilar IPC controllers exist that yield exactly the same performance in terms of load reductions on fixed and rotating turbine structures. We further demonstrate this outcome by presenting results obtained from high-fidelity closed-loop turbine simulations

Modular Model Predictive Control upon an Existing Controller

The availability of predictions of future system inputs has motivated research into preview control to improve set-point tracking and disturbance rejection beyond that achievable via conventional feedback control. The design of preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, is often performed in a monolithic nature, coupling the feedback and feed-forward problems. This can create problems, such as: (i) an additional feedback loop is introduced by MPC, which alters the closed-loop dynamics of the existing feedback compensator, potentially resulting in a deterioration of the nominal sensitivities and robustness properties of an existing closed-loop and (ii) the default preview action from MPC can be poor, degrading the original feedback control performance. In our previous work, the former problem is addressed by presenting a modular MPC design on top of a given output-feedback controller, which retains the nominal closed-loop robustness and frequency-domain properties of the latter, despite the addition of the preview design. In this paper, we address the second problem; the preview compensator design in the modular MPC formulation. Specifically, we derive the key conditions that ensure, under a given closed-loop tuning, the preview compensator within the modular MPC formulation is systematic and well-designed in a sense that the preview control actions complement the existing feedback control law rather than opposing it. In addition, we also derive some important results, showing that the modular MPC can be implemented in a cascade over any given linear controllers and the proposed conditions hold, regardless of the observer design for the modular MPC. The key benefit of the modular MPC is that the preview control with constraint handling can be implemented without replacing the existing feedback controller. This is illustrated through some numerical examples

Kalman-based interacting multiple-model wind speed estimator for wind turbines

The use of state estimation technique offers a means of inferring the rotor-effective wind speed based upon solely standard measurements of the turbine. For the ease of design and computational concerns, such estimators are typically built based upon simplified turbine models that characterise the turbine with rigid blades. Large model mismatch, particularly in the power coefficient, could lead to degradation in estimation performance. Therefore, in order to effectively reduce the adverse impact of parameter uncertainties in the estimator model, this paper develops a wind sped estimator based on the concept of interacting multiple-model adaptive estimation. The proposed estimator is composed of a bank of extended Kalman filters and each filter model is developed based on different power coefficient mapping to match the operating turbine parameter. Subsequently, the algorithm combines the wind speed estimates provided by each filter based on their statistical properties. In addition, the proposed estimator not only can infer the rotor-effective wind speed, but also the uncertain system parameters, namely, the power coefficient. Simulation results demonstrate the proposed estimator achieved better improvement in estimating the rotor-effective wind speed and power coefficient compared to the standard Kalman filter approach.Comment: 6 pages, 7 figures, Accepted in IFAC World Congress 2020, in Berlin, German