公开(公告)号：US10138873B2

公开(公告)日：2018-11-27

申请号：US14291140

申请日：2014-05-30

Applicant: General Electric Company

Inventor： Peter Alan Gregg ,  Megan Michela Wilson ,  Siddhanth Chandrashekar ,  Vineel Chandrakanth Gujjar ,  Manisha Srivastava ,  Colin Craig McCulloch

IPC: G01C17/38 ,  F03D11/00 ,  F03D7/04 ,  F03D17/00

Abstract: A computer-implemented method for recalibrating nacelle-positions of a plurality of wind turbines in a wind park is implemented by a nacelle calibration computing device including a processor and a memory device coupled to the processor. The method includes identifying at least two associated wind turbines included within the wind park wherein each associated wind turbine includes location information, determining a plurality of predicted wake features for the associated wind turbines based at least partially on the location information of each associated wind turbine, retrieving a plurality of historical performance data related to the associated wind turbines, determining a plurality of current wake features based on the plurality of historical performance data, identifying a variance between the predicted wake features and the current wake features, and determining a recalibration factor for at least one of the associated wind turbines based on the identified variance.

公开(公告)号：US20150345476A1

公开(公告)日：2015-12-03

申请号：US14291140

申请日：2014-05-30

Applicant: General Electric Company

Inventor： Peter Alan Gregg ,  Megan Michela Wilson ,  Siddhanth Chandrashekar ,  Vineel Chandrakanth Gujjar ,  Manisha Srivastava ,  Colin Craig McCulloch

IPC: F03D11/00

CPC classification number: F03D17/00 ,  F03D7/048 ,  F05B2270/802 ,  Y02E10/723

Abstract: A computer-implemented method for recalibrating nacelle-positions of a plurality of wind turbines in a wind park is implemented by a nacelle calibration computing device including a processor and a memory device coupled to the processor. The method includes identifying at least two associated wind turbines included within the wind park wherein each associated wind turbine includes location information, determining a plurality of predicted wake features for the associated wind turbines based at least partially on the location information of each associated wind turbine, retrieving a plurality of historical performance data related to the associated wind turbines, determining a plurality of current wake features based on the plurality of historical performance data, identifying a variance between the predicted wake features and the current wake features, and determining a recalibration factor for at least one of the associated wind turbines based on the identified variance.

Abstract translation: 用于重新校准风力发电场中的多个风力涡轮机的机舱位置的计算机实现的方法由包括耦合到处理器的处理器和存储器件的机舱校准计算设备实现。 该方法包括识别包括在风力公园内的至少两个相关联的风力涡轮机，其中每个相关联的风力涡轮机包括位置信息，至少部分地基于每个相关联的风力涡轮机的位置信息确定相关风力涡轮机的多个预测的尾流特征， 检索与相关联的风力涡轮机相关的多个历史性能数据，基于所述多个历史性能数据确定多个当前唤醒特征，识别所述预测尾迹​​特征与所述当前唤醒特征之间的差异，以及确定重新校准因子 基于所识别的方差的相关风力涡轮机中的至少一个。