A correction method and a correction apparatus for a predicted wind speed of a wind farm is provided according to the present disclosure. The correction method includes: establishing wind speed deviation matrixes of a plurality of existing wind farms respectively; establishing a wind speed deviation correction model library based on the plurality of wind speed deviation matrixes of the existing wind farms; determining relevant parameters of the target wind farm, determining a matched wind speed deviation correction model in the wind speed deviation correction model library based on the relevant parameters, and correcting the predicted wind speed of the target wind farm based on the determined wind speed deviation correction model.

A dynamic adaptive mooring system for wave energy converters (WEC) is disclosed that has a mooring configuration that has a set of fixed mooring lines, and a set of movable mooring lines. When an incoming wave train interacts with the fixed WECs, a set of wave interference points that have higher wave amplitudes than the incoming wave train are formed downstream of the fixed WECs. The movable WECs are then positioned at the interface points to optimize wave energy transfer.

A system and method are provided for determining a geographic location of a tower top pivot point (TPP) of a wind turbine tower having a nacelle that includes a machine head and rotor at a top thereof. At least one rover receiver of a global navigation satellite system (GNSS) is configured at a fixed position on the nacelle. A plurality of 360-degree yaw sweeps of the nacelle are conducted and the geo-location signals received by the rover receiver during the yaw sweeps are recorded. With a controller, the geo-location signals are converted into a circular plot and a radius of the plot is determined, the radius being a distance between the rover receiver and the TPP. Based on a GNSS geo-location of the rover receiver and the radius, a geo-location of the TPP is computed.

A multirotor wind turbine (1) comprising a vertical tower and at least two energy generating units (5), a load carrying structure (9, 10) extending transverse to the vertical direction and carrying the at least two energy generating units (5); and at least one escape route extending between a start and an exit. To provide a safe escape route, the load carrying structure forms at least a first section of the escape route from the start to an intermediate location, and the wind turbine comprises an escape opening in the nacelle, the escape opening leading from an interior space of a nacelle of the energy generating unit to a passage structure and the passage structure extending from the escape opening to the start of the escape route.

The disclosed hydroelectric power generation system includes a waterwheel rotated by falling water having multiple curved portions. Multiple circular members each having a cover are loaded in a corresponding one of the multiple curved portions, elevated with the cover in an open position to empty the circular member, filled with water upon reaching a top dead point thereof, and allowed to fall freely with the cover in a closed position. The cover of the circular members are automatically opened and closed. A track extends downwardly from a point at which the curved portion of the waterwheel is turned into a downwardly inclined position. The track guides the circular member to move by gravity along the track. A feed track allows the circular members to be supplied back to respective curved portions during rotation of the waterwheel. An output shaft of a gear train drives a generator.

Embodiments of the present disclosure provide a method for controlling a fan, a system, and an air conditioner. The method includes: turning off a first bridge arm group in an inverter of the fan; applying a preset driving signal to a second bridge arm group in the inverter; detecting an electrical signal of a stator of the fan after the preset driving signal is applied; determining an initial state of the fan according to the electrical signal of the stator of the fan, wherein the initial state of the fan includes a downwind forward state, a static start state, or an unwind reverse state; and providing the fan with a control signal matching the initial state of the fan according to the initial state of the fan.

Systems and methods coordinate drone flights in an operating windfarm. A drone flight path that is removed from any location where the drone is conducting observations of a wind turbine and that extends through at least part of a windfarm to a destination location is determined. A respective wake pattern along at least one portion of the drone fight path is determined based on respective operating parameters for each of at least one wind turbine in the windfarm. Flight time commands to adjust at least one respective operating parameter of the respective wind turbine to reduce the effect of the respective wake pattern at a point ahead of the drone on the drone flight path are sent by a windfarm controller controlling the at least one wind turbine.

A wind turbine system comprising: a wind turbine; and a monitoring system, wherein the wind turbine comprises: a tower; an arm extending from the tower, a rotor-nacelle assembly (RNA) carried by the arm; and a Global Navigation Satellite System (GNSS) sensor carried by the arm or the RNA. The monitoring system is configured to receive position data from the GNSS sensor and obtain a moment or force measurement on the basis of the position data.

Systems and methods for estimating a direction of wind incident on a wind turbine, the wind turbine comprising a tower; a rotor-nacelle-assembly (RNA) carried by the tower; a deflection sensor configured to sense a position of the RNA or a deflection of the tower; and a wind direction sensor. One approach includes: obtaining deflection training data from the deflection sensor; obtaining wind direction training data from the wind direction sensor; training a machine learning model on the basis of the deflection training data and the wind direction training data in order to obtain a trained machine learning model; obtaining further deflection data from the deflection sensor; inputting the further deflection data into the trained machine learning model; and operating the machine learning model to output a wind direction estimate on the basis of the further deflection data.

The device recording the collisions of flying animals with wind turbines and indicating where they fell on the ground includes at least two sensors arranged on at least two different heights of the wind turbine tower. The sensors have a range optimally in a direction perpendicular to the wind turbine tower and are connected with the control and recording unit by wire or wireless data transmission.