According to the disclosure, an artificial intelligence (AI) model receives a power production amount, a power production efficiency, a control variable and the like states as input information through information exchange between a wind turbine and the AI model, and therefore it is possible to provide a control method using the AI model with regard to even the wind turbine given no power coefficient.

Wind turbine farms are presented including: a number of steerable wind turbines each having a turbine diameter, where the number of steerable wind turbines is grouped pairwise into a number of monopole wind tower pairs, where each monopole wind tower pair is placed in a fixed pair placement and oriented in one of a number of fixed pair orientations, where each one of the number of fixed pair orientations corresponds with one of a number of prevailing wind directions, and where the number of monopole wind tower pairs is placed in a number of fixed pair positions.

Wind turbine farms are presented including: a number of steerable wind turbines, where each of the number of steerable wind turbines includes a turbine diameter, where each of the number of steerable wind turbines are steerable about a vertical axis, where each of the number of steerable wind turbines includes a wake centerline, where the number of steerable wind turbines is grouped, where each group is defined by at least two steerable wind turbines, where each of the number of steerable wind turbines is positioned in a fixed position such that wake centerlines are separated by approximately 0.5 and 6.0 turbine diameters.

A method (300) for the control of a wind farm (10) is disclosed. The method (300) comprises: read-in of data from at least one first wind power plant (200) of the wind farm; supply of the read-in data from the at least one first wind power plant to a statistical prediction model for the control of at least one second wind power plant (200) of the wind farm based on the read-in data from the at least one first wind power plant; and use of the statistical prediction model to control the at least one second wind power plant (200).

Wind turbine farms are presented including: a number of steerable wind turbines each having a turbine diameter, where the number of steerable wind turbines is grouped pairwise into a number of monopole wind tower pairs, where each monopole wind tower pair is placed in a fixed pair placement and oriented in one of a number of fixed pair orientations, where each one of the number of fixed pair orientations corresponds with one of a number of prevailing wind directions, and where the number of monopole wind tower pairs is placed in a number of fixed pair positions.

The invention relates to a method for determining the energy production to be expected for a wind power installation for a forecast time period, which may be an expected annual energy production. The installation has installation components. In the method, at least one of the installation components is selected as a thermally relevant component and chronologically distributed wind speed values are specified for the forecast time period. An expected power output level of the installation is determined for one of the wind speed values. In the power output level determining step, a component temperature which is assigned to this wind speed value is taken into account by the at least one thermally relevant component. The expected power output level of the installation, which is determined for the wind speed value, is used to determine the energy production of the installation which is to be expected for the forecast time period.

A method for improving power production of a wind turbine includes obtaining, by a controller having one or more processors, wind forecast data of the wind turbine. The method also includes scheduling, by the controller, one or more health checks for one or more components of the wind turbine based, at least in part, on the wind forecast data. Moreover, the method includes implementing, via the controller, the one or more health checks based on the scheduling such that the one or more health checks are implemented during time periods having wind speeds below a predetermined threshold.

An energy-harvesting compute grid includes computing assemblies that cooperate with mobile energy harvesters configured to be deployed on a body of water. The plurality of energy harvesters are positioned on and move adjacent to an upper surface of a body of water, and the locations of the energy harvesters can be monitored and controlled. The wide-spread gathering by the harvesters of environmental data within that geospatial area permits the forecasting of environmental factors, the discovery of advantageous energy-harvesting opportunities, the observation and tracking of hazardous objects and conditions, the efficient distribution of data and/or tasks to and between the harvesters included in the compute grid, the efficient execution of logistical operations to support, upgrade, maintain, and repair the cluster, and the opportunity to execute data-gathering across an area much larger than that afforded by an individual harvester (e.g., radio astronomy, 3D tracking of and recording of the communication patterns of marine mammals, etc.). The computational tasks can be shared and distributed among a compute grid implemented in part by a collection of individual floating self-propelled energy harvesters thereby providing many benefits related to cost and efficiency that are unavailable to relatively isolated energy harvesters, and likewise unavailable to terrestrial compute grids of the prior art.

A control method for increasing reactive power generation of a wind turbine having a Doubly-Fed Induction Generator (DFIG) includes obtaining, by a control device having one or more processors and one or more memory devices, wind forecast data of the wind turbine. Further, the method includes generating, by the control device, a real-time thermal model of the DFIG of the wind turbine using the wind forecast data. More specifically, the thermal model defines a thermal capacity for the DFIG that does not exceed system limits. Thus, the method also includes dynamically adjusting, by the control device, a reactive power set point of the DFIG of the wind turbine based on the real-time thermal model.

Irregular motion of waves creates a challenge to obtain energy efficiently. Heave type devices have been found to have high efficiencies, but they are limited to capturing energy along one or two directions of freedom. A new system and method for obtaining energy from the heaving motion of the waves is presented. It consists of base and heave structures connected through arm devices comprising three degrees of freedom, said arms powered by the motion of the heave structure in the fluid. These arm devices allow capture of wave energy by mechanical, hydraulic, or pneumatic systems.