Embodiments herein describe optimizations for hybrid power plants that include wind turbine generators and photovoltaic generators by determining solar tracking setpoints for a field of photovoltaic generators co-located with a wind turbine generator, wherein the solar tracking setpoints orient a collector face for each photovoltaic generator sunward; in response to determining based on data received from the wind turbine generator that an airborne impactor event is occurring, adjusting the solar tracking setpoints to reposition the collector face out of a trajectory for airborne impactors; and transmitting the setpoints to tracking motors for the photovoltaic generators of the field.

A method for feeding electric power into an electric supply network using a wind park having wind power installations is provided. An expected power is determined for a predetermined feed-in period, where the expected power indicates a power value or temporal profile of power expected to be available to the park as power from wind in the predetermined feed-in period. An expected accuracy is determined and is a measure of how accurately the power reaches the expected power in the feed-in period. To determine the expected power, at least one expected wind variable representative of the expected wind speed is determined using a weather forecast, and the expected wind variable is additionally determined or verified, proceeding from the weather forecast, using a correction rule based on local weather data and/or operating data of the park. The expected power is determined on the basis of the expected wind variable.

The present disclosure is directed to a system and method for estimating an overall wind coherence acting on a wind turbine and using same to dynamically adapt the gain or bandwidth of pitch or torque or yaw control logic within a wind turbine. The method includes generating, via sensors, a plurality of sensor signals reflective of wind conditions near the wind turbine. The method also includes filtering, via at least one filter, the sensor signals at a predetermined frequency range considered damaging for turbine sub-system loading. Thus, the method also includes estimating an overall damaging wind coherence acting on the wind turbine as a function of distance-normalized wind coherences, which themselves are derived from auto and cross-covariances of pairs of filtered signals. The distance normalization uses a model of natural coherence dissipation with distance.

The present invention is concerned with an operation of a wind farm with a plurality of wind turbines in view of a dynamic frequency response. According to the invention, dynamic frequency support and power production for all wind turbines in a wind farm are handled concurrently in a single optimization step and taking into account wake effects within the wind farm as well as optional wind forecast information. The dynamic frequency support capability of the entire wind farm is planned in advance according to grid requirements and power system condition changes. While existing methods de-load wind turbines with a static percentage in order to supply additional power when needed, the proposed method incorporates the dynamic frequency support into the optimal operation system of wind farm.

The present disclosure is directed to a system and method for estimating an overall wind coherence acting on a wind turbine and using same to dynamically adapt the gain or bandwidth of pitch or torque or yaw control logic within a wind turbine. The method includes generating, via sensors, a plurality of sensor signals reflective of wind conditions near the wind turbine. The method also includes filtering, via at least one filter, the sensor signals at a predetermined frequency range considered damaging for turbine sub-system loading. Thus, the method also includes estimating an overall damaging wind coherence acting on the wind turbine as a function of distance-normalized wind coherences, which themselves are derived from auto and cross-covariances of pairs of filtered signals. The distance normalization uses a model of natural coherence dissipation with distance.

A weather predicting method includes: selecting, from weather information related to areas and times and including temperature data, weather information sets related to multiple times over a fixed period concerning a first area containing a location where the air utilizing apparatus is placed; by solving, with selected weather information sets as input data, differential equations expressing the weather information based on analysis models for conducting weather simulations, generating a first narrow area weather information sets related to smaller second areas disposed within the first area; selecting a second narrow-area weather information set concerning a second area containing the location of the air utilizing apparatus from among generated first narrow-area weather information sets; and generating a temperature cumulative frequency distribution or a temperature exceedance probability distribution during the fixed period by using temperature data contained in the second narrow-area weather information set for calculating a design temperature of the air utilizing apparatus.

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.

The present disclosure relates to a method and a system for repairing a wind generator based on weather information. According to an exemplary embodiment of the present disclosure, it is possible to effectively calculate a repairing time of the wind generator based on weather information.

Embodiments of the invention generally relate to using remote sensing equipment such as a Light Detection and Ranging (LIDAR) device to detect wind characteristics for use by wind turbines of a wind park. A wind park controller may received raw wind data from the remote sensing device and determine one or more turbines that can use the raw wind data. The raw wind data may be converted to customized data for each of the one or more wind turbines. Upon being provided the customized wind data, the one or more wind turbines may adjust one or more operational characteristics to improve power production or avoid damage to turbine components.

A method for controlling a power output of a power generating unit is disclosed. An accumulated power output of the power generating unit during a predefined time interval is forecasted. An actual power output of the power generating unit is measured during the predefined time interval, and an actual accumulated power output is estimated for the predefined time interval on the basis of the measured actual power output of the power generating unit. A difference between the forecasted accumulated power output and the estimated actual accumulated power output is derived. The power output of the power generating unit is boosted, in the case that the estimated actual accumulated power output is below the forecasted accumulated power output, and the difference between the forecasted accumulated power output and the estimated actual accumulated power output is larger than a predefined threshold value.