Model-based predictive control method (MPC) for the reduction of structural load in wind turbines comprising: exclusively proposing a single internal linear model for the MPC for the entire operating range of the turbine; obtaining the adjustable parameters of the linear internal model from the experimental data previously measured in the turbine; choosing the discrete time values for the control and prediction horizons; adjusting the MPC controller and performing a practical implementation test.

The present invention is a method of determining an induction factor of the wind for a wind turbine (1) equipped with a LiDAR sensor (2). For this method, wind speed measurements are performed in measurement planes (PM) by use of LiDAR sensor (2), then induction factors between measurement planes (PM) are determined by use of the measurements and of a first linear Kalman filter, and the induction factor between a measurement plane (PM) and the rotor plane (PR) of wind turbine (1) is determined by a second linear Kalman filter.

A method for dispatching a power system based on optimal load transfer ratio and optimal grid connection ratio of wind power and photovoltaic power includes: acquiring load data; drawing a load curve; defining a peak load period, a flat load period and a low load period, and calculating average loads of the peak load period, the flat load period and the low load period before a load transfer; determining value ranges of a peak-low load transfer ratio, a peak-flat load transfer ratio and a flat-low load transfer ratio; establishing an objective function considering generation cost of thermal power unit, wind power purchase cost, PV power purchase cost and compensation cost for consumer load transfer; introducing an immune algorithm to calculate grid connection ratio of wind power, grid connection ratio of PV power, peak-low load transfer ratio, peak-flat load transfer ratio and flat-low load ratio corresponding to a minimum operating cost.

A method for iteratively estimating a wind speed at a wind turbine is disclosed, said wind turbine comprising a rotor carrying a set of wind turbine blades, each wind turbine blade having a variable pitch angle. A minimum tip speed ratio, λ.sub.min, is derived, based on an obtained pitch angle, θ. The minimum tip speed ratio, λ.sub.min, defines a limit between a stable and an unstable control region. An initial tip speed ratio, λ.sub.init>λ.sub.min, is selected, and an initial estimated wind speed, v.sub.init, is derived based on the initial tip speed ratio, λ.sub.int, and an obtained rotational speed, ω, of the rotor. An estimated wind speed, v.sub.est, is iteratively derived, based on the obtained rotational speed, ω, and the obtained pitch angle, ω, and using the derived initial estimated wind speed, v.sub.init, as a starting point. The iterative process converges fast and reliably.

A logistics system for a multirotor wind turbine (1) is disclosed. The multirotor wind turbine (1) comprises two or more energy generating units (4), each mounted on an arm (3) extending from a tower (2) of the multirotor wind turbine (1). A transport system (14, 30, 31, 32, 33, 34, 36) interconnects a lower interior part of the tower (2) with each of the energy generating units (4). A plurality of transport containers (15) is connectable to the transport system (14, 30, 31, 32, 33, 34, 36) and configured to hold equipment (26) to be transported. A control unit is configured to receive information regarding contents and position of the transport containers (15), and to plan transport of the transport containers (15) via the transport system (14, 30, 31, 32, 33, 34, 36), based on a service plan for the multirotor wind turbine (1).

Information indicating a source of electric current can be received alongside or detected within the electric current itself, enabling recipients to identify a source of their electricity. The information may be determined based on analysis of the received electric current, such as by detecting a modulated carrier signal embedded within a received alternating current.

The invention relates to a power plant controller for controlling wind turbine generators. More particularly, the invention relates to a method for compensating data obtained from measurements at a connection point to the grid in case of a communication failure where communication of such data is lost or becomes unreliable. The measured data are used in the power plant controller for determining setpoints for controlling the wind turbine generators' production of active and reactive power. In response to detection of a communication fault a new setpoint is determined independently of new measured grid data by reconfiguring parts of the power plant controller.

An apparatus and method is disclosed for over-rating a wind turbine using turbulence prediction. Weather forecast information is used to determine whether there is a risk of turbulent conditions occurring at the site of the wind turbine. The wind turbine is over-rated if turbulent conditions are not predicted, and conversely over-rating is cancelled or reduced if turbulent conditions are expected. This allows an increase in the annual energy production of the wind turbine to be realised. The weather forecast information may be combined with real time measurements of operating conditions to supplement the predictions.

Methods, systems, and devices for controlling a yaw offset of an upstream wind turbine based on reinforcement learning are provided. The method includes receiving data indicative of a current state of the first wind turbine and of a current state of a second wind turbine adjacent to the first wind turbine downstream along a wind direction, determining one or more controlling actions associated with the yaw offset of the first wind turbine based on the current state of the first wind turbine, the current state of the second wind turbine, and a reinforcement learning algorithm, and applying the determined one or more controlling actions to the first wind turbine.

The invention relates to a method for controlling power generation from a power plant which comprises power generating units such as wind turbine generators. The power generating units are controllable to produce power dependent on individual power set-points. The method comprises steps of obtaining desired target power set-points for one or more prioritized power generating units, determining final target power set-points for the one or more prioritized power generating units based on the target power set-points and an available power adjustment margin of the power plant which is available for adjustment of the power set-points of the one or more prioritized power generating units, and dispatching the final target power set-points to the prioritized power generating units.