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

Quasilinear Control Theory for Systems with Asymmetric Actuators and Sensors.

Quasilinear Control (QLC) theory provides a set of methods for analysis and design of systems with nonlinear actuators and sensors. In practice, actuators always saturate and sensors often have deadzone or quantization. One limitation of the current QLC theory is that it is applicable only to systems with symmetric nonlinearities. In many situations, however, nonlinearities are asymmetric. Examples of such systems abound: air-conditioning/heating systems, automotive torque and idle speed control, wind turbine control, etc. In this work, we provide an extension of the QLC theory to the asymmetric case. Similar to the symmetric case, the approach is based on the method of stochastic linearization, which replaces nonlinear systems by quasilinear ones. Unlike the symmetric case, however, stochastic linearization in the asymmetric case replaces each nonlinearity not only by an equivalent gain, but also by an equivalent bias. The latter leads to steady state errors incompatible with the usual error coefficients predicted by linear systems theory. For this reason, the extension to the asymmetric case is non-trivial. Specific problems addressed in this dissertation with regards to asymmetric systems are: (i) Introduction and investigation of the notion of asymmetry. (ii) Development of a formalism of stochastic linearization for systems at hand. (iii) Analysis of tracking and disturbance rejection performance. (iv) Introduction and investigation of performance loci, i.e., root locus and tracking error locus. (v) Utilization of the performance loci for random reference and step reference tracking controller design. (vi) Recovery of linear performance in nonlinear systems. (vii) Disturbance rejection controller design using an LQR-type approach. (viii) Application of the methods developed to a wind farm controller design. In addition, a Matlab-based toolbox that implements most of the QLC methods has been developed and is available at www.QuasilinearControl.com.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99805/1/hamido_1.pd