Fault-tolerant load reduction control for large offshore wind turbines

Offshore wind turbines suffer from asymmetrical loading (blades, tower etc.), leading to enhanced structural fatigue. As well as asymmetrical loading different types of faults (pitch system faults etc.) can occur simultaneously, causing degradation of load mitigation performance and enhanced fatigue. Individual pitch control (IPC) provides an important method to achieve mitigation of rotor asymmetric loads, but this may be accompanied by a resulting enhancement of pitch movement leading to increased possibility of pitch system faults, which negative effects on IPC performance.This thesis focuses on combining the fault tolerant control (FTC) techniques with load reduction strategies by a more intelligent pitch control system (i.e. collective pitch control and IPC) for offshore wind turbines in a system level to reduce the operation & maintenance costs and improve the system reliability. The scenario of load mitigation is analogous to the FTC problem because the action of rotor/tower bending can be considered as a fault effect. The essential concept is to attempt to account for all the "fault effects" in the rotor and tower systems which can weaken the effect of bending moment reduction through the use of IPC.Motivated by the above, this thesis focuses on four aspects to fill the gap of the combination between FTC and IPC schemes. Firstly, a preview control system using model predictive control with future wind speed is proposed, which could be a possible alternative to using LiDAR technology when using preview control for load reduction. Secondly, a multivariable IPC controller for both blade and tower load mitigation considering the inherent couplings is investigated. Thirdly, appropriate control-based fault monitoring strategies including fault detection and fault estimation FE-based FTC scheme are proposed for several different pitch actuator/sensor faults. Furthermore, the combined analysis of an FE-based FTC strategy with the IPC system at a system level is provided and the robustness of the proposed strategy is verified

Characterization and Optimization of Control System and Extreme Value Analysis of a Wind Turbine

The global hunger for energy has only been rising every day as we progress more into adopting superior and better technology to make lives better and easier. As more countries tread the path of industrialization, the need for cleaner and greener energy is imperative to reduce damage to an already deteriorating environment. Wind energy accounts for about 15% of renewable energy and is one of the fastest-growing renewable technologies. The energy produced by wind has doubled in the past decade. The wind industry is only expected to grow with an infinite supply of natural wind. Wind turbines in the 1980s had a capacity of 0.1MW, but today they average at around 10MW, with the largest wind turbines having a capacity of 15MW. Due to a surge in demand and potential for growth, it has become more critical than ever to find newer strategies to maximize turbine efficiency. Numerous approaches can be adopted to improve a turbine's efficiency, including design, material, or control system changes. Other options include using novel or better prediction models to estimate extreme values that can be useful in fine-tuning designs of wind turbines. In this thesis, two main strategies are adopted in an attempt to optimize the efficiency of wind turbines. Firstly, in Paper-I, a novel change to the pitch controller is adopted by adding and optimizing the bending moments to reduce the bending moment in the low-speed shaft. A reduction in the bending moment will reduce the internal drive train loads within the gearbox, thus extending its lifespan. A reduction of bending moment with minimal loss in shaft rotational speed was observed through this optimization. While in, Paper-II and -III, the novel ACER1D and 2D (univariate and bivariant analysis) models were used to estimate extreme load values. Paper-II presented the ACER1D results but focused on the ACER2D as it fitted it against other models, such as the optimized Asymmetric and Gumbel logistic models. This paper showed that ACER2D was advantageous since it could produce very accurate results compared to the other models with very little data set. While in Paper-III, extreme values estimate from ACER2D were compared against the Gumbel model, and the results obtained were positive, showing that ACER1D was better at estimating extreme values with a small data set. Optimizing the extreme values is critical when designing wind turbines as proper values enable better and more reliable turbine designs. Thus, both the strategies adopted in this thesis showed that through proper optimization, a reduction of load or a better design could be achieved, resulting in better efficiency in wind turbines

Wind Turbine Controls for Farm and Offshore Operation

Development of advanced control techniques is a critical measure for reducing the cost of energy for wind power generation, in terms of both enhancing energy capture and reducing fatigue load. There are two remarkable trends for wind energy. First, more and more large wind farms are developed in order to reduce the unit-power cost in installation, operation, maintenance and transmission. Second, offshore wind energy has received significant attention when the scarcity of land resource has appeared to be a major bottleneck for next level of wind penetration, especially for Europe and Asia. This dissertation study investigates on several wind turbine control issues in the context of wind farm and offshore operation scenarios. Traditional wind farm control strategies emphasize the effect of the deficit of average wind speed, i.e. on how to guarantee the power quality from grid integration angle by the control of the electrical systems or maximize the energy capture of the whole wind farm by optimizing the setting points of rotor speed and blade pitch angle, based on the use of simple wake models, such as Jensen wake model. In this study, more complex wake models including detailed wind speed deficit distribution across the rotor plane and wake meandering are used for load reduction control of wind turbine. A periodic control scheme is adopted for individual pitch control including static wake interaction, while for the case with wake meandering considered, both a dual-mode model predictive control and a multiple model predictive control is applied to the corresponding individual pitch control problem, based on the use of the computationally efficient quadratic programming solver qpOASES. Simulation results validated the effectiveness of the proposed control schemes. Besides, as an innovative nearly model-free strategy, the nested-loop extremum seeking control (NLESC) scheme is designed to maximize energy capture of a wind farm under both steady and turbulent wind. The NLESC scheme is evaluated with a simple wind turbine array consisting of three cascaded variable-speed turbines using the SimWindFarm simulation platform. For each turbine, the torque gain is adjusted to vary/control the corresponding axial induction factor. Simulation under smooth and turbulent winds shows the effectiveness of the proposed scheme. Analysis shows that the optimal torque gain of each turbine in a cascade of turbines is invariant with wind speed if the wind direction does not change, which is supported by simulation results for smooth wind inputs. As changes of upstream turbine operation affects the downstream turbines with significant delays due to wind propagation, a cross-covariance based delay estimate is proposed as adaptive phase compensation between the dither and demodulation signals. Another subject of investigation in this research is the evaluation of an innovative scheme of actuation for stabilization of offshore floating wind turbines based on actively controlled aerodynamic vane actuators. For offshore floating wind turbines, underactuation has become a major issue and stabilization of tower/platform adds complexity to the control problem in addition to the general power/speed regulation and rotor load reduction controls. However, due to the design constraints and the significant power involved in the wind turbine structure, a unique challenge is presented to achieve low-cost, high-bandwidth and low power consumption design of actuation schemes. A recently proposed concept of vertical and horizontal vanes is evaluated to increase damping in roll motion and pitch motion, respectively. The simulation platform FAST has been modified including vertical and horizontal vane control. Simulation results validated the effectiveness of the proposed vertical and horizontal active vane actuators

Fault-tolerant individual pitch control using adaptive sliding mode observer

Due to the increasing size of wind turbines, the unbalanced loads caused by the uneven spatial distribution of wind speed and turbulence are becoming larger and larger. As has been proved, individual pitch control (IPC) can mitigate the blade asymmetric loads greatly in region 3. On the other hand, the pitch actuator faults can affect the pitching performance with slow dynamics, resulting in generator power instability and even deteriorating the unbalanced loads of blades. However, the effects of unbalanced blade loads deterioration caused by pitch actuator faults have not been taken into account by the traditional IPC design. In the present paper, a fault-tolerant control (FTC) strategy using adaptive sliding mode estimation is combined with a traditional IPC system based on two different control methods (Proportional-Integral and H∞ loop-shaping). It maintains the nominal pitch performance and removes the negative effects of pitch actuator faults on generator power and unbalanced blade loads perfectly. The effectiveness of the proposed strategy is verified on the 5MW NREL wind turbine system