METHOD FOR PREDICTING WIND SPEED IN THE ROTOR PLANE FOR A WIND TURBINE EQUIPPED WITH A LiDAR SENSOR

Patent metadata

Field	Value
Publication number	US-2020301020-A1
Application number	US-202016821049-A
Country	US
Kind code	A1
Filing date	Mar 17, 2020
Priority date	Mar 18, 2019
Publication date	Sep 24, 2020
Grant date	—

Abstract

The present invention is a method for predicting the wind speed in the rotor plane (PR) of a wind turbine (1), by accounting for an induction factor used in a wind evolution model implemented by a Kalman filter. The invention also is a method for controlling a wind turbine (1), a computer program product, a LiDAR sensor (2) and a wind turbine (1), which uses the wind prediction determined with the method according to the invention.

First claim

1 - 10 . (canceled)

11 . A method for predicting wind speed in a rotor plane of a wind turbine equipped with a LiDAR sensor, comprising:
a) measuring the wind speed in at least one measurement plane distant from the wind turbine by use of the LiDAR sensor; b) determining a wind induction factor, representing a wind deceleration coefficient between the measurement plane and the plane of the rotor; c) determining a delay index between the measurement plane and the rotor plane of the wind turbine by use of the induction factor and the wind speed measurement in the measurement plane; d) constructing a wind evolution model between the measurement plane and the plane of the rotor, the wind evolution model connecting the wind speed in the plane of the rotor at a predetermined time to the measured wind speed in the measurement plane at times prior to the predetermined time, by use of the induction factor with the prior times being determined by use of the delay index; and e) determining the wind speed prediction in the rotor plane by use of the wind evolution model and of a Kalman filter.

12 . A prediction method as claimed in claim 11 , wherein a wind induction factor is determined by carrying out the following steps:
i) measuring wind speed in at least three measurement planes distant from the wind turbine by use of the LiDAR sensor; ii) determining at least two wind induction factors between two of the measurement planes using the wind speed measurements in the measurement planes and a linear Kalman filter; and iii) determining the wind induction factor between a measurement plane and the rotor plane of the wind turbine by use of the determined induction factors between two measurement planes and using a linear Kalman filter.

13 . A prediction method as claimed in claim 11 , wherein the wind speed measurement step comprises reconstructing a wind field in the measurement plane which is used in other steps of the method as for wind speed measurement in the measurement plane.

14 . A prediction method as claimed in claim 12 , wherein the wind speed measurement step comprises reconstructing a wind field in the measurement plane which is used in other steps of the method as for wind speed measurement in the measurement plane.

15 . A prediction method as claimed in claim 11 , wherein the delay index k d0 is determined by an equation:

k

d



0

=

2


x
1

(

U

x



1

+

U
0

)



T
s

,

with U 0 =a 0,x 1 U x 1 , with x 1 being the distance between the measurement plane and the rotor plane, T s being the measurement sampling period, U x1 being an average wind speed measured in the measurement plane, U 0 being an average wind speed in the rotor plane and a 0,x1 being the induction factor between the measurement plane and the rotor plane.

16 . A prediction method as claimed in claim 12 , wherein the delay index kd0 is determined by an equation: ,
with, with x1 being the distance between the measurement plane and the rotor plane, Ts being the measurement sampling period, Ux1 being an average wind speed measured in the measurement plane, U0 being an average wind speed in the rotor plane and a0,x1 being the induction factor between the measurement plane and the rotor plane.

17 . A prediction method as claimed in claim 13 , wherein the delay index k d0 is determined by an equation:

k

d



0

=

2


x
1

(

U

x



1

+

U
0

)



T
s

,

with U 0 =a 0,x 1 U x 1 , with x 1 being the distance between the measurement plane and the rotor plane, T s being the measurement sampling period, U x1 being an average wind speed measured in the measurement plane, U 0 being an average wind speed in the rotor plane and a 0,x1 being the induction factor between the measurement plane and the rotor plane.

18 . A prediction method as claimed in claim 11 , wherein the wind evolution model is expressed as follows:
u 0 ( k+p )= Ũ x 1 ( k−k d0 +p ) T r ( k|k ), with Ũ x 1 ( k−k d0 +p )=[ ũ x 1 ( k−k d0 +p ) ũ x 1 ( k−k d0 +p− 1) ũ x 1 ( k−k d0 +p+ 1) . . . ũ x 1 ( k−k d0 +p+N d )] T and, with u 0 being the wind in rotor plane, k being discretized time, p being a future time step, k d0 being the delay index, r being a state vector determined by the Kalman filter, x 1 being a measurement plane, N d being an order of the wind evolution model, u x1 being wind speed measured in the measurement plane and a 0,x1 being the induction factor between the measurement plane and the rotor plane.

19 . A prediction method as claimed in claim 12 , wherein the wind evolution model is expressed as follows:
u 0 ( k+p )= Ũ x 1 ( k−k d0 +p ) T r ( k|k ), with Ũ x 1 ( k−k d0 +p )=[ ũ x 1 ( k−k d0 +p ) ũ x 1 ( k−k d0 +p− 1) ũ x 1 ( k−k d0 +p+ 1) . . . ũ x 1 ( k−k d0 +p+N d )] T and ũ x 1 (k)=a 0,x

Assignees

• Ifp Energies Now

Inventors

• Guillemin Fabrice
• Nguyen Hoai-Nam

Classifications

• Y02A30/00

  Adapting or protecting infrastructure or their operation · CPC title

• F03D17/00 Primary

  Monitoring or testing of wind motors, e.g. diagnostics (testing during commissioning of wind motors F03D13/30) · CPC title

• Y02E10/72

  Wind turbines with rotation axis in wind direction · CPC title

• F05B2270/32

  Wind speeds · CPC title

• G01P5/26 Primary

  by measuring the direct influence of the streaming fluid on the properties of a detecting optical wave · CPC title