LIDAR-based wind speed modelling and control system design

Abstract—The main objective of this work is to explore the feasibility of using LIght Detection And Ranging (LIDAR) measurement and develop feedforward control strategy to improve wind turbine operation. Firstly the Pseudo LIDAR measurement data is produced using software package GH Bladed across a distance from the turbine to the wind measurement points. Next the transfer function representing the evolution of wind speed is developed. Based on this wind evolution model, a model-inverse feedforward control strategy is employed for the pitch control at above-rated wind conditions, in which LIDAR measured wind speed is fed into the feedforward. Finally the baseline feedback controller is augmented by the developed feedforward control. This control system is developed based on a Supergen 5MW wind turbine model linearised at the operating point, but tested with the nonlinear model of the same system. The system performances with and without the feedforward control channel are compared. Simulation results suggest that with LIDAR information, the added feedforward control has the potential to reduce blade and tower loads in comparison to a baseline feedback control alone

Pseudo-LIDAR data analysis and feed-forward wind turbine control design

To investigate potential improvement in wind turbine control employing LIDAR measurement, pseudo-LIDAR wind speed data is produced with Bladed using a designed sampling strategy, and assessed with preliminary frequency-domain analysis. A model-inverse feed-forward controller is adapted to combine with feedback control so as to enhance pitch control performance at high wind speed. This controller is applied to an industrial-scale 5MW wind turbine model and the control performance is compared with a baseline feedback controller. Simulation study demonstrates that the combined feed-forward/feedback control scheme has improvements in reducing pitch angle variation and reduction of load relevant metrics

An Economic NMPC Formulation for Wind Turbine Control

Model Predictive Control (MPC) is a strong candidate for the control of large Multi-MegaWatt Wind Turbine Generators. Several MPC and some Nonlinear MPC scheme have been proposed in the literature, based on reference-tracking objective functions. While the resulting schemes offer very promising results, the difficulty of tuning a reference-tracking NMPC scheme for performance is likely to be a hindrance to the industrial success of NMPC-based WTG control. Because they directly maximize the system performance, economic NMPC schemes are more straightforward to tune. Economic NMPC schemes present, however, some known difficulties that are a serious obstacle to their real-time deployment. This paper presents an economic NMPC formulation for maximizing the generated power of wind turbine generators, which does not suffer from such difficulties. The relationship between the proposed and more classical reference-tracking approaches is formally established

A data-driven approach for fatigue-based individual blade pitch controller selection from wind conditions

International audienceIn a context of wind power production growth, it is necessary to optimize the levelized cost of energy by reducing the wind turbine operation and maintenance costs. This paper addresses these issues through an innovative data-driven approach, applied to individual pitch control and based on wind conditions clustering, from light detection and ranging (LiDAR) wind field reconstruction. A set of controllers is first designed, and a surrogate model is fitted to predict the economic fatigue cost of the wind turbine in closed-loop for each of these controllers, given a cluster of wind conditions. This allows on-line selection of the controller minimizing mechanical fatigue loads among the candidates for each wind condition. Preliminary tests show promising results regarding the effectiveness of this method in reducing wind turbine fatigue when compared to a single optimized individual pitch controller. The main advantages of this approach are to limit the sensitivities to controller tuning procedure and to provide an economically driven control strategy based on fatigue theory that can be effectively adapted to different wind turbine systems

Comparison of linear and nonlinear model predictive control of wind turbines using LIDAR

Recent developments in remote sensing are offering a promising opportunity to rethink conventional control strategies of wind turbines. With technologies such as LIDAR, the information about the incoming wind field - the main disturbance to the system - can be made available ahead of time. Feedforward control can be easily combined with traditional collective pitch feedback controllers and has been successfully tested on real systems. Nonlinear model predictive controllers adjusting both collective pitch and generator torque can further reduce structural loads in simulations but have higher computational times compared to feedforward or linear model predictive controller. This paper compares a linear and a commercial nonlinear model predictive controller to a baseline controller. On the one hand simulations show that both controller have significant improvements if used along with the preview of the rotor effective wind speed. On the other hand the nonlinear model predictive controller can achieve better results compared to the linear model close to the rated wind speed

Nonlinear model predictive control of floating wind turbines with individual pitch control

In this work a nonlinear model predictive controller with individual pitch control for a floating offshore wind turbine is presented. An aerodynamic model of the collective pitch control approach is extended by describing pitching and yawing moments based on rotor disk theory. This extension is implemented in a reduced nonlinear model of the floating wind turbine including disturbance preview of wind speed, linear vertical and horizontal wind shear, and wave height to compute optimal input trajectories for the individual pitch control inputs and the generator torque. An extended cost functional for individual pitch control is proposed based on the collective pitch control approach. The controller is evaluated in aero-servo-hydro-elastic simulations of a 5MW reference wind turbine disturbed by a three-dimensional stochastic turbulent wind field. Results show a significant blade fatigue load reduction compared to a baseline controller through minimizing yawing and pitching moments on the rotor hub while maintaining the advantages of the model predictive control approach with collective pitch control

Multi-Objective Control Strategies and Prognostic-Based Lifetime Extension of Utility-Scale Wind Turbines

Windenergie wird zunehmend als erneuerbare Energiequellen attraktiv, da Wind nachhaltig genutzt werden kann. In vielen Ländern gibt es umfangreiche Anstrengungen, die Produktion von elektrischer Energie aus Wind zu steigern. Im Vergleich zu anderen erneuerbaren Energiequellen wie Sonne, Gezeiten, Wasserkraft o.ä. ist die Energiegewinnung aus Wind technologisch ausgereifter. Daher ist die Energiegewinnung aus Wind stärker gewachsen ist als andere Technologien. Windkraft verursacht weniger nachteilige Auswirkungen auf die Umwelt als konventionelle Energiequellen. Aufgrund der vergleichsweise hohen Investitions-, Betriebs- und Wartungskosten sind trotz einer weltweit starken Verbreitung von Windenergieanlagen die Produktionskosten von Windenergie im Vergleich mit anderen alternativen Energiequellen hoch. Um die wachsende Nachfrage nachWindkraft zu befriedigen, werdenWindkraftanlagen in Größe und Leistung zunehmend skaliert. Bei zunehmender Größe dominieren die strukturellen Lasten der Turbine. Dies führt vermehrt zu Materialermüdung und Ausfällen. Ein weiterer Schwerpunkt in der Entwicklung von Windtechologie ist die Forderung nach Senkung der Produktionskosten, um einen Wettbewerbsvorteil gegenüber anderen alternativen Energiequellen zu schaffen. Im Bereich der Steuerung können niedrigere Produktionskosten durch den Betrieb der Windturbine am/oder in der Nähe der optimalen Energieeffizienz im Teillastbetrieb erreicht werden. Dies erhöht die Zuverlässigkeit durch Verringerung des Verschleißes und die erzeugte Nennleistung auf ihrem Nennwert im hohen Windregime. Häufig ist es schwierig, einen Steueralgorithmus zu realisieren, der sowohl Effizienz als auch Zuverlässigkeit gewährleistet, da diese beiden Ziele widersprechen. In dieser Arbeit werden Mehrzielsteuerungsstrategien sowohl für den Teillastbereich als auch für hohe Windgeschwindigkeits bereiche vorgestellt. Im Bereich geringer Windgeschwindigkeiten ist eine Steuerungsstrategie so zu konzipieren, dass die Stromerzeugung sowie die strukturelle Belastung im Sinne einer Balance zwischen maximalen Stromproduktion und verlängerter Lebensdauer der Windturbine optimal ist. Für den Bereich hoher Windgeschwindigkeiten wird ein multivariates Steuerungsverfahren vorgeschlagen, um das Verhältnis von Leistung/Geschwindigkeit und struktureller Lastreduzierung zu optimieren. Es wird ein Regler zur Einzelblattverstellung entworfen, um sowohl die unausgewogene Strukturlasten als auch durch die Variation des Windgeschwindigkeit verursachte Rotorscheibe, vertikale Windscherung und Gierversatz fehler zu reduzieren. Um die Zuverlässigkeit derWindturbine zu gewährleisten, ist ein Online-Schadensbewertungsmodell in den Hauptwindturbinenregelkreis integriert, so dass die Windturbine entsprechend ihres aktuellen Verschleißzustandes betrieben wird. In Abhängigkeit von der akkumulierten Schadenshöhe werden Regler zur Einzelblattverstellung mit unterschiedlichen Lastreduktionen und Kompromissen bei der Stromerzeugung eingesetzt, um zwischen den beiden Zielen verlängerte Lebensdauer und Leistungsregelung einen geeigneten Kompromiss zu erzielten. Aufgrund der Herausforderungen die mit Offshore-Windpark Standorten verbunden sind, ist diese Art von prognose-basierter Regelung im Windturbinenbetrieb vor allem im Offshore-Einsatz vorteilhaft. Insbesondere im Kontext output-basierter Vertragsformen z.B. power purchase agreement (PPA) kann dieser Ansatz zur Optimierung der Wartungsplanung genutzt werden. Die Ergebnisse zeigen, dass die vorgeschlagenen Strategien die Auflast auf Windturbinen reduzieren kann ohne sich auf andere Ziele wie die Leistungsoptimierung und Leistung/Drehzahlregelung auszuwirken. Es konnte außerdem gezeigt werden, dass eine prognostisch basierte Steuerung effektiv die Lebensdauer von Windenergieanalagen verlängern kann, ohne das Ziel der Leistungsregelung einzuschränken.Wind energy is one of the rapidly growing renewable sources of energy due to the fact that wind is abundantly available and unlikely to be exhausted like fossil fuel. Additionally, there are deliberate effort to sensitize many countries to develop polices that support production of electrical power from wind. Maturity of wind energy technology has made power production from wind grow significantly compared to other renewable energy sources such as solar, tidal, hydro among others. Like many other renewable energy sources, production of power from wind has less adverse effects on the environment. Despite the growth of global cumulative installed wind capacity, its cost of production is still higher compared to other alternative energy sources due to high initial investment cost and high operation and maintenance (O&M) costs. To meet the growing demand of wind power, wind turbines are being scaled up both in size and power rating. However, as the size increases, the structural loads of the turbine become more dominant, causing increased fatigue stress on the turbine components and consequent loss of functionality before the end of lifetime. Another area of focus in wind power production is lowering its production cost; hence, making it more competitive compared to other alternative power sources. From the control point of view, low production cost of wind energy can be achieved by operating wind turbine at/or near the optimum power efficiency during partial load regime, regulating generated power to its rated value in the high wind regime as well as mitigating structural loads so as to guarantee extended lifetime. Often, it is difficult to realize a control algorithm that can effectively guarantee both efficiency and reliability because these two aspects involve conflicting objective. Therefore, it is important to optimize the trade-off between these competing control objectives. In this thesis, multi-objective control strategies for both the partial load region and high wind speed region are presented. During low wind speed, a control strategy that optimizes power production as well as mitigating structural load is designed to balance between power production maximization and extended lifetime of wind turbine. On the other hand, a multivariate control method to balance between power/speed regulation and structural load reduction is proposed for high wind speed region. More specifically, an individual blade pitch controller is designed to eliminate the unbalanced deterministic structural loads across rotor disc caused by variation in wind speed, vertical wind shear, and yaw misalignment error. To guarantee reliability in wind turbine, an online damage evaluation model is also integrated into the main wind turbine control loop such that wind turbine is operated accordance to its structural health status in order to tolerate fault or to extend its service lifetime by a given period of time. Depending on the accumulated damage level, individual pitch controllers with different degrees of load reduction and power production compromise are employed to balance between extended lifetime and power regulation objective. This kind of prognostic-based control is useful in wind turbine operation, especially in offshore application due to challenges associated with offshore wind farm sites. Additionally, in wind farms that are managed through output-based contracts such as power purchase agreement (PPA), this approach can be utilized to optimize maintenance scheduling to avoid unscheduled downtime. The results demonstrated that the proposed multi-objective control strategies can reduce structural load on wind turbine without adversely affecting other objectives of power optimization and power/speed regulation. It has also be shown that a prognostic-based control can be effectively used to extend the lifetime of wind turbine without sacrificing the objective of power regulation