Numerical optimal control of a wind turbine system

textWith the development of wind turbine technology and the need for maximizing wind energy harvesting, more wind turbines operate in the partial load region. Among many control algorithms developed for this region, controllers based on feedback of the global maximum power coefficient have been widely used. These control schemes offer good performance with simple implementations, but they may not be suited for wind turbines with limited rotor speed ranges. In such cases, the controller is challenged because the main feature ---the global maximum power coefficient--- is not achievable due to the turbine speed constraint. It is necessary to develop a controller to seek the achievable maximum power coefficient that leads to optimal wind energy capture. In this dissertation, the development of an optimal control framework to maximize wind energy capture for wind turbines with constrained turbine speed is first presented. Numerical optimal control techniques are applied to search for the achievable maximum power coefficient, with proposed modifications to make this task more computationally feasible. Mitigating the turbine generator torque variation, thus reducing the fatigue loading on turbine generator shaft, is also important for the partial load region operation. Including this aspect in the optimal control is then discussed. Furthermore, an approach of incorporating time-varying weightings into developing the optimal controller is introduced to seek further improvement on turbine generator torque variation reduction, thus fatigue reduction. In addition, the power generated by the wind turbine varies due to variation in the wind speed. Depending on the load demand and the wind speed, the wind turbine's operation switches between two modes: a multi-input-single-output (MISO) mode and a single-input-single-output (SISO) mode. Due to the wind turbine changes its dynamic behavior during the switching process, applying the traditional control methods to each corresponding mode may not be capable of maximizing the overall wind energy capture throughout the entire turbine's operation. Therefore, the development of an optimal control framework to maximize the overall wind energy capture for a switched wind turbine system is subsequently presented.Mechanical Engineerin

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

Wind Turbine Wake Interactions - Characterization of Unsteady Blade Forces and the Role of Wake Interactions in Power Variability Control

Growing concerns about the environmental impact of fossil fuel energy and improvements in both the cost and performance of wind turbine technologies has spurred a sharp expansion in wind energy generation. However, both the increasing size of wind farms and the increased contribution of wind energy to the overall electricity generation market has created new challenges. As wind farms grow in size and power density, the aerodynamic wake interactions that occur between neighboring turbines become increasingly important in characterizing the unsteady turbine loads and power output of the farm. Turbine wake interactions also impact variability of farm power generation, acting either to increase variability or decrease variability depending on the wind farm control algorithm. In this dissertation, both the unsteady vortex wake loading and the effect of wake interaction on farm power variability are investigated in order to better understand the fundamental physics that govern these processes and to better control wind farm operations to mitigate negative effects of wake interaction. The first part of the dissertation examines the effect of wake interactions between neighboring turbines on the variability in power output of a wind farm, demonstrating that turbine wake interactions can have a beneficial effect on reducing wind farm variability if the farm is properly controlled. In order to balance multiple objectives, such as maximizing farm power generation while reducing power variability, a model predictive control (MPC) technique with a novel farm power variability minimization objective function is utilized. The controller operation is influenced by a number of different time scales, including the MPC time horizon, the delay time between turbines, and the fluctuation time scales inherent in the incident wind. In the current research, a non-linear MPC technique is developed and used to investigate the effect of three time scales on wind farm operation and on variability in farm power output. The goal of the proposed controller is to explore the behavior of an ideal farm-level MPC controller with different wind, delay and horizon time scales and to examine the reduction of system power variability that is possible in such a controller by effective use of wake interactions. The second part of the dissertation addresses the unsteady vortex loading on a downstream turbine caused by the interaction of the turbine blades with coherent vortex structures found within the upstream turbine wake. Periodic, stochastic, and transient loads all have an impact on the lifetime of the wind turbine blades and drivetrain. Vortex cutting (or vortex chopping) is a type of stochastic load that is commonly observed when a propeller or blade passes through a vortex structure and the blade width is of the same order of magnitude as the vortex core diameter. A series of Navier-Stokes simulations of vortex cutting with and without axial flow are presented. The goal of this research is to better understand the challenging physics of vortex cutting by the blade rotor, as well as to develop a simple, physics-based, validated expression to characterize the unsteady force induced by vorte

Modelling and control of wind turbines with aeroelastically tailoring blades

The increased size of wind turbines (WTs) improves power generation efficiency but also imposes larger loading effects on the turbine system. A wind turbine with an aeroelastic tailoring blade (ATB) is proposed to alleviate the loading effect in wind turbine blades. A turbine with ATB is designed to respond to the incoming wind forces by deforming the shape of the blade and then reforming to its initial formation. The blade is manufactured with composite materials, incorporated with pre-twist angle and bend twist coupling (BTC) characteristics. Wind turbines with ATB are a new development that needs a better understanding of their operational performance and their potential when properly controlled. This PhD project aims to investigate the modelling and control of industrial-scale ATB WTs and assess the control performance with systematic studies. The thesis work includes two connected parts, model development, and control system design. A set of models has been developed for system analysis and controller design. To start with, a baseline model is revisited that covers key modelling elements of a 5MW standard HAWT wind turbine. This model is indexed as Model 0 in this thesis, it is the basis for other ATB WTs. To characterise ATB features, firstly the static BTC distribution is added to the turbine aerodynamics to account for the blade’s pre-bend-twist design. This static ATB model is integrated to the baseline model giving the full nonlinear turbine model, called Model 1, which will be used for the gain-scheduling baseline controller. Next, the ATB dynamics is approximated by a spring damper model to describe the blade structural dynamic response to wind speed variations. The developed turbine model combining the static ATB and dynamic ATB is called Model 2, based on which a linearised and discretised state-space model is developed for adaptive model predictive control (MPC). Additionally, a composite ATB model is established, in which the power coefficient values are generated from physical laboratory experiments for a composite materials blade. This model is referred to as Model 3, will also be used for adaptive MPC. Two controllers are investigated for the above-rated ATB WT operational control. The first controller is the gain scheduling baseline controller developed by the Wind Energy and Control Centre, initially for full envelope WT control of a standard machine without ATB. This baseline controller is redeveloped for the ATB WT using Model 1. The second controller is the adaptive MPC proposed and developed in this thesis work, which includes a general predictive controller enhanced by the use of a Kalman filter and online model update. This adaptive MPC is applied to Model 2 and Model 3 to examine the control performance. Several tools are used to support model development and controller design. Model 0 (including the baseline controller) is a nonlinear full-envelope model developed in Simulink (Chapter 3). Model 1 is developed by introducing the pre-twist angle and BTC in GL Bladed software, the generated power coefficients are then imported to the Simulink model. The simulation of Model 1 and the adapted baseline controller is made in Simulink (Chapter 3). Model 2 is developed by combining the data generated for static ATB in Model 1 and the dynamic ATB model. The full model for baseline control (Chapter 4) and the simplified state-space model for adaptive MPC (Chapter 5) are implemented in Matlab and Simulink. Model 3 is used for adaptive MPC, also realised in Matlab and Simulink (Chapter 6). Based on the comprehensive investigation, it is concluded that the ATB WT models developed in this work are suitable for controller design. Both the adapted gain scheduling baseline controller and the proposed adaptive MPC can be applied to achieve satisfactory control performance, that is, to mitigate fatigue load without compromising the power generation of the turbine system. With adaptive MPC, the system demonstrates improvement in reducing pitch activity, tower acceleration and blade root bending moment.The increased size of wind turbines (WTs) improves power generation efficiency but also imposes larger loading effects on the turbine system. A wind turbine with an aeroelastic tailoring blade (ATB) is proposed to alleviate the loading effect in wind turbine blades. A turbine with ATB is designed to respond to the incoming wind forces by deforming the shape of the blade and then reforming to its initial formation. The blade is manufactured with composite materials, incorporated with pre-twist angle and bend twist coupling (BTC) characteristics. Wind turbines with ATB are a new development that needs a better understanding of their operational performance and their potential when properly controlled. This PhD project aims to investigate the modelling and control of industrial-scale ATB WTs and assess the control performance with systematic studies. The thesis work includes two connected parts, model development, and control system design. A set of models has been developed for system analysis and controller design. To start with, a baseline model is revisited that covers key modelling elements of a 5MW standard HAWT wind turbine. This model is indexed as Model 0 in this thesis, it is the basis for other ATB WTs. To characterise ATB features, firstly the static BTC distribution is added to the turbine aerodynamics to account for the blade’s pre-bend-twist design. This static ATB model is integrated to the baseline model giving the full nonlinear turbine model, called Model 1, which will be used for the gain-scheduling baseline controller. Next, the ATB dynamics is approximated by a spring damper model to describe the blade structural dynamic response to wind speed variations. The developed turbine model combining the static ATB and dynamic ATB is called Model 2, based on which a linearised and discretised state-space model is developed for adaptive model predictive control (MPC). Additionally, a composite ATB model is established, in which the power coefficient values are generated from physical laboratory experiments for a composite materials blade. This model is referred to as Model 3, will also be used for adaptive MPC. Two controllers are investigated for the above-rated ATB WT operational control. The first controller is the gain scheduling baseline controller developed by the Wind Energy and Control Centre, initially for full envelope WT control of a standard machine without ATB. This baseline controller is redeveloped for the ATB WT using Model 1. The second controller is the adaptive MPC proposed and developed in this thesis work, which includes a general predictive controller enhanced by the use of a Kalman filter and online model update. This adaptive MPC is applied to Model 2 and Model 3 to examine the control performance. Several tools are used to support model development and controller design. Model 0 (including the baseline controller) is a nonlinear full-envelope model developed in Simulink (Chapter 3). Model 1 is developed by introducing the pre-twist angle and BTC in GL Bladed software, the generated power coefficients are then imported to the Simulink model. The simulation of Model 1 and the adapted baseline controller is made in Simulink (Chapter 3). Model 2 is developed by combining the data generated for static ATB in Model 1 and the dynamic ATB model. The full model for baseline control (Chapter 4) and the simplified state-space model for adaptive MPC (Chapter 5) are implemented in Matlab and Simulink. Model 3 is used for adaptive MPC, also realised in Matlab and Simulink (Chapter 6). Based on the comprehensive investigation, it is concluded that the ATB WT models developed in this work are suitable for controller design. Both the adapted gain scheduling baseline controller and the proposed adaptive MPC can be applied to achieve satisfactory control performance, that is, to mitigate fatigue load without compromising the power generation of the turbine system. With adaptive MPC, the system demonstrates improvement in reducing pitch activity, tower acceleration and blade root bending moment

Development of a self-tuned drive-train damper for utility-scale variable-speed wind turbines

This thesis describes the development of a procedure that tunes a wind turbine drivetrain damper (DTD) automatically. This procedure, when integrated into the controller of any utility-scale variable-speed wind turbine, will allow the turbine to autonomously and automatically tune its DTD on site. In practice this means that the effectiveness of the damper becomes independent on the accuracy of the model or the simulations used by the control engineers in order to tune the damper. This research is motivated by the fact that drive-train failures are still one of the biggest problems that stigmatises the wind turbine industry. The development of an automatically tuned DTD that alleviates the drive-train fatigue loads and thus increases the reliability and lifetime of the drive-train is thus considered very beneficial for the wind turbine industry. The procedure developed begins by running an experimental procedure to collect data that is then used to automatically system identify a linear model describing the drivetrain. Based on this model a single band-pass filter acting as a DTD is automatically tuned. This procedure is run for a number of times, and the resulting DTDs are compared in order to select the optimal one. The thesis demonstrates the effectiveness of the developed procedure and presents alternative procedures devised during research. Finally, insight into future work that could be performed is indicated in the last chapter of the thesis