Abstract:
The present disclosure is directed to a system and method for assessing farm-level performance of a wind farm. The method includes operating the wind farm in a first operational mode and identifying one or more pairs of wind turbines having wake interaction. The method also includes generating a pairwise dataset for the wind turbines pairs. Further, the method includes generating a first wake model based on the pairwise dataset and predicting a first farm-level performance parameter based on the first wake model. The method also includes operating the wind farm in a second operational mode and collecting operational data during the second operational mode. Moreover, the method includes predicting a first farm-level performance parameter for the second operational mode using the first wake model and the operational data from the second operational mode. The method further includes determining a second farm-level performance parameter during the second operational mode. Thus, the method includes determining a difference in the farm-level performance of the wind farm as a function of the first and second farm-level performance parameters.
Abstract:
A method of calculating theoretical power of wind farm based on extrapolation of anemometer tower data includes following steps. A number of anemometer towers in a wind farm is selected, and analyzing historical data acquired by the number of anemometer towers. An air density is calculated based on the historical data of the number of anemometer towers. A power curve is calibrated based on the historical data of the number of anemometer towers. The power curve is fit based on a wind speed and a wind power of a fan head of each wind turbine based on the historical data. An theoretical power calculation extrapolation model of anemometer tower data is constructed. Real-time anemometer tower wind data and a calibrated air density are inputted into the theoretical power calculation model and calculating the wind power. The theoretical power is obtained.
Abstract:
A wind turbine has a Lidar device to sense wind conditions upstream of the wind turbine including wind speed, direction and turbulence. Signals from the Lidar are processed to detect an event which could give rise to low cycle fatigue loading on one or more components of the wind turbine. On detection the system controller takes the necessary evasive action depending on the nature and severity of the extreme condition detected. This may include a significant reduction in power generated through reduction in rotor speed or torque, complete shutdown of the generator and yawing of the nacelle and rotor in response to a change in wind direction.
Abstract:
A method for determining wind conditions within a geographic area based on a plurality of input wind resource grids. The input wind resource grids include input points associated with a geographic position and a wind condition. An output wind resource grid having a plurality of output points is defined. Each output point is associated with a geographic position within the geographic area. For each output point in the output wind resource grid, a wind condition is calculated based at least in part on wind conditions associated with at least some of the input points. A wind condition associated with an input point may be weighted based on the proximity of the output point to a meteorological instrument associated with the input point.
Abstract:
An object is to provide an off-shore wind turbine generator capable of obtaining accurate information about the situation of a wind turbine itself, surrounding weather conditions, and the like. The off-shore wind turbine generator of the present invention generates power by driving a generator mechanism through the rotation of a rotor head to which wind turbine blades are attached and includes a monitoring apparatus for monitoring the wind turbine generator itself and its surrounding circumstances.
Abstract:
There is provided a method of avoiding edgewise vibrations during a non-operational period of a wind turbine. The method comprises defining a non-operational period for a wind turbine arranged at a specific site, determining expected wind conditions at the specific site during the non-operational period and defining a plurality of potential yaw orientations for the wind turbine. The method further comprises determining the relative probability of edgewise vibrations occurring during the non-operational period for each potential yaw orientation based upon the expected wind conditions during the non-operational period, determining one or more preferred yaw orientations, which are the yaw orientations in which the probability of edgewise vibrations occurring is lowest, and arranging the wind turbine in one of the preferred yaw orientations during the non-operational period.
Abstract:
A radar system for a wind turbine is provided. The radar system comprises a first radar unit (42) and a control unit (41) arranged to receive an output from the radar unit, the control unit comprising a central processing unit. The central processing unit is configured to perform a first function of determining at least one property of aircraft within a monitoring zone in the vicinity of the wind turbine and controlling a warning device to output a warning signal to detected aircraft based on the determined property; and perform a second function of determining at least one parameter of prevailing weather in the vicinity of the wind turbine. A corresponding method is also provided.
Abstract:
A wind turbine includes a number of blades and an optical measurement system comprising a light source, such as a laser, an optical transmitter part, an optical receiver part, and a signal processor. The light source is optically coupled to the optical transmitter part, which includes an emission point for emitting light in a probing direction. The optical receiver part comprises a receiving point and a detector. The optical receiver part is adapted for receiving a reflected part of light from a probing region along the probing direction and directing the reflected part of light to the detector to generate a signal used to determine a first velocity component of the inflow. The emission point is located in a first blade at a first radial distance from a center axis, and the receiving point is located in the first blade at a second radial distance from the center axis.
Abstract:
The present invention relates to methods, controllers, wind turbines and computer program products for controlling a wind turbine. One or more wind speed measurements upstream of a wind turbine are received 202 and a determination of an indication of a current wind speed at the wind turbine is made 204. The indication may include below rated wind speed or above rated wind speed. It is determined 205 if the wind speed is in an up transition region or a down transition region based on the received one or more wind speed measurements and the indication of said current wind speed. If determined that said wind speed is in an up transition region or a down transition region then a boost action is performed 206.
Abstract:
A wind turbine has a Lidar device to sense wind conditions upstream of the wind turbine. Signals from the wind turbine are processed to detect an extreme event. On detection the system controller takes the necessary evasive action depending on the nature and severity of the extreme condition detected. This may include a significant reduction in power generated, complete shutdown of the generator and yawing of the nacelle and rotor to reduce loading on the rotor blades.