U.S. patent number 10,385,829 [Application Number 15/151,573] was granted by the patent office on 2019-08-20 for system and method for validating optimization of a wind farm.
This patent grant is currently assigned to General Electric Company. The grantee listed for this patent is General Electric Company. Invention is credited to Akshay Ambekar, Siddhanth Chandrashekar, Sara Delport, Subhankar Ghosh, Stefan Kern, Dongjai Lee, Megan Wilson.
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United States Patent |
10,385,829 |
Wilson , et al. |
August 20, 2019 |
System and method for validating optimization of a wind farm
Abstract
The present disclosure is directed to systems and methods for
generating one or more farm-level power curves for a wind farm that
can be used to validate an upgrade provided to the wind farm. The
method includes operating the wind farm in a first operational
mode. Another step includes collecting turbine-level operational
data from one or more of the wind turbines in the wind farm during
the first operational mode. The method also includes aggregating
the turbine-level operational data into a representative farm-level
time-series. Another step includes analyzing the operational data
collected during the first second operational mode. Thus, the
method also includes generating one or more farm-level power curves
for the first operational mode based on the analyzed operational
data.
Inventors: |
Wilson; Megan (Greenville,
SC), Kern; Stefan (Munich, DE), Chandrashekar;
Siddhanth (Bangalore, IN), Lee; Dongjai (Greer,
SC), Delport; Sara (Munich, DE), Ambekar;
Akshay (Mauldin, SC), Ghosh; Subhankar (Bangalore,
IN) |
Applicant: |
Name |
City |
State |
Country |
Type |
General Electric Company |
Schenectady |
NY |
US |
|
|
Assignee: |
General Electric Company
(Schenectady, NY)
|
Family
ID: |
60295148 |
Appl.
No.: |
15/151,573 |
Filed: |
May 11, 2016 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20170328348 A1 |
Nov 16, 2017 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
F03D
17/00 (20160501); F03D 9/257 (20170201); F03D
7/028 (20130101); F03D 7/048 (20130101); Y02E
10/72 (20130101); F05B 2270/335 (20130101); F05B
2270/20 (20130101) |
Current International
Class: |
F03D
7/04 (20060101); F03D 17/00 (20160101); G05B
15/02 (20060101); F03D 7/02 (20060101); F03D
9/25 (20160101) |
Field of
Search: |
;290/44,55 ;700/286,287
;702/24,181,182,188 |
References Cited
[Referenced By]
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Oct 2013 |
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WO |
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Primary Examiner: Breene; John E
Assistant Examiner: Aiello; Jeffrey P
Attorney, Agent or Firm: Dority & Manning, P.A.
Claims
What is claimed is:
1. A method for controlling a wind farm having a plurality of wind
turbines, the method comprising: operating, via a farm controller,
the wind farm in a first operational mode for a first time period;
operating, via the farm controller, the wind farm in an second
operational mode for a second time period, the second operational
mode being characterized by one or more of the wind turbines being
provided with an upgrade; collecting, via the farm controller,
turbine-level operational data from two or more of the wind
turbines in the wind farm during the first and second operational
modes; aggregating, via the farm controller, the turbine-level
operational data into a representative farm-level time-series;
analyzing, via the farm controller, the operational data collected
during the first and second operational modes, wherein analyzing
the operational data further comprises, at least, mitigating loss
of operational data; generating, via the farm controller, a
farm-level power curve for each of the first and second operational
modes based on the analyzed operational data to assess a benefit of
the upgrade; and, controlling, via the farm controller, an overall
power output of the wind farm based on the farm-level power curve
for the first or second operational mode that optimizes the overall
power output for the wind farm rather than a power output for each
of the plurality of wind turbines.
2. The method of claim 1, wherein analyzing the operational data
collected during the first operational mode further comprises
utilizing at least one of data binning or regression analysis.
3. The method of claim 1, wherein aggregating the turbine-level
operational data into the representative farm-level time-series
further comprises summing power generated by two or more of the
wind turbines in the wind farm for the first and second operational
modes.
4. The method of claim 1, further comprising toggling between the
first and second operational modes and collecting turbine-level
operational data during each of the modes.
5. The method of claim 1, wherein mitigating loss of operational
data comprises at least one of power scaling, sub-clustering,
back-filling the operational data with historic data, evaluating
uncertainty of the operational data, or accounting for individual
turbine operation states.
6. The method of claim 1, wherein generating the farm-level power
curve for the first and second operational modes based on the
analyzed operational data further comprises: binning the
operational data from the first and second operational modes by
wind direction into a plurality of wind sectors, excluding wind
sectors with insufficient operational data, and generating a
sector-specific farm-level power curve for non-excluded wind
sectors.
7. The method of claim 6, further comprising evaluating a
farm-level energy production for the first and second operational
modes based on at least one of the sector-specific farm-level power
curves and an expected wind rose and Weibull distribution.
8. The method of claim 1, further comprising generating a predicted
power curve for the first and second operational modes based on one
or more simulated wind conditions prior to operating the wind farm
in the first and second operational modes.
9. The method of claim 8, further comprising: substituting actual
measurement data in place of the simulated wind conditions where
available, and where measurement data is not available, adjusting
the remaining simulated wind conditions via a realization
factor.
10. The method of claim 8, further comprising: generating a test
equivalent power curve based on observed wind conditions during the
first operational mode, and generating the farm-level power curve
based on the predicted power curve and the test equivalent power
curve.
11. The method of claim 1, wherein the operational data comprises
information regarding at least one of or a combination of the
following parameters: power output, generator speed, torque output,
grid conditions, pitch angle, tip speed ratio, yaw angle, internal
control set points, loading conditions, geographical information,
temperature, pressure, wind turbine location, wind farm location,
weather conditions, wind gusts, wind speed, wind direction wind
acceleration, wind turbulence, wind shear, wind veer, or wake.
12. The method of claim 1, wherein the upgrade comprises any one of
or a combination of the following: a revised pitch or yaw angle,
tip speed ratio, rotor blade chord extensions, software upgrades,
controls upgrades, hardware upgrades, wake controls, aerodynamic
upgrades, blade tip extensions, vortex generators, or winglets.
13. A system for controlling wind farm having a plurality of wind
turbines, the system comprising: a farm controller comprising a
processor communicatively coupled to one or more sensors, the
processor configured to perform one or more operations, the one or
more operations comprising: operating the wind farm in a first
operational mode for a first time period; operating the wind farm
in an second operational mode for a second time period, the second
operational mode being characterized by one or more of the wind
turbines being provided with an upgrade; collecting turbine-level
operational data from two or more of the wind turbines in the wind
farm during the first and second operational modes; aggregating the
turbine-level operational data into a representative farm-level
time-series; analyzing operational data collected during the first
and second operational modes, wherein analyzing the operational
data further comprises, at least, mitigating loss of operational
data; generating a farm-level power curve for the first and second
operational modes based on the analyzed operational data to assess
a benefit of the upgrade; and, controlling an overall power output
of the wind farm based on the farm-level power curve for the first
or second operational mode that optimizes the power output for the
wind farm rather than a power output for each of the plurality of
wind turbines.
14. The system of claim 13, wherein analyzing the operational data
collected during the first and second operational modes further
comprises utilizing at least one of data binning or regression
analysis.
15. The system of claim 13, wherein aggregating the turbine-level
operational data into the representative farm-level time-series
further comprises summing power generated by each wind turbine in
the wind farm for the first and second operational modes.
16. The system of claim 13, wherein mitigating loss of operational
data comprises at least one of power scaling, sub-clustering,
back-filling the operational data with historic data, evaluating
uncertainty of the operational data, accounting for individual
turbine operation states.
Description
FIELD OF THE INVENTION
The present invention relates generally to wind farms, and more
particularly, to systems and methods for validating optimization of
a wind farm.
BACKGROUND OF THE INVENTION
Wind power is considered one of the cleanest, most environmentally
friendly energy sources presently available, and wind turbines have
gained increased attention in this regard. A modern wind turbine
typically includes a tower, a generator, a gearbox, a nacelle, and
a rotor having one or more rotor blades. The rotor blades transform
wind energy into a mechanical rotational torque that drives one or
more generators via the rotor. The generators are sometimes, but
not always, rotationally coupled to the rotor through the gearbox.
The gearbox steps up the inherently low rotational speed of the
rotor for the generator to efficiently convert the rotational
mechanical energy to electrical energy, which is fed into a utility
grid via at least one electrical connection. Such configurations
may also include power converters that are used to convert a
frequency of generated electric power to a frequency substantially
similar to a utility grid frequency.
A plurality of wind turbines are commonly used in conjunction with
one another to generate electricity and are commonly referred to as
a "wind farm." Wind turbines on a wind farm typically include their
own meteorological monitors that perform, for example, temperature,
wind speed, wind direction, barometric pressure, and/or air density
measurements. In addition, a separate meteorological mast or tower
("met mast") having higher quality meteorological instruments that
can provide more accurate measurements at one point in the farm is
commonly provided. The correlation of meteorological data with
power output allows the empirical determination of a "power curve"
for the individual wind turbines.
Traditionally, wind farms are controlled in a decentralized fashion
to generate power such that each turbine is operated to maximize
local energy output and to minimize impacts of local fatigue and
extreme loads. To this end, each turbine includes a control module,
which attempts to maximize power output of the turbine in the face
of varying wind and grid conditions, while satisfying constraints
like sub-system ratings and component loads. Based on the
determined maximum power output, the control module controls the
operation of various turbine components, such as the
generator/power converter, the pitch system, the brakes, and the
yaw mechanism to reach the maximum power efficiency.
However, in practice, such independent optimization of the wind
turbines ignores farm-level performance goals, thereby leading to
sub-optimal performance at the wind farm level. For example,
downwind turbines may experience large wake effects caused by an
upwind turbine. Because of these wake effects, downwind turbines
receive wind at a lower speed, drastically affecting their power
output (as power output increases with wind speed). Consequently,
maximum efficiency of a few wind turbines may lead to sub-optimal
power output, performance, or longevity of other wind turbines in
the wind farm. Thus, modern control technologies attempt to
optimize the wind farm power output rather than the power outputs
of each individual wind turbine.
In addition, there are many products, features, and/or upgrades
available for wind turbines and/or wind farms so as to increase
power output or annual energy production (AEP) of the wind farm.
Once an upgrade has been installed, it is advantageous to
efficiently verify the benefit of the upgrade. For example, a
typical method for assessing wind turbine performance measurements
is to baseline power against wind speed as assessed by the turbine
nacelle anemometer. The nacelle anemometer approach, however, is
sometimes hindered due to imprecision of nacelle anemometer
measurements and the projection of these measurements into AEP
estimates. Further, such an approach may be less preferred than use
of an external met mast in front of a wind turbine, but is in
widespread use due to the generally prohibitive cost of the met
mast approach. In addition, even when nacelle anemometers are
calibrated correctly, individual wind power curve methods are not
able to discern the benefit of upgrades, such as wake minimization
technologies, that can create more wind for the farm to use.
Thus, a system and method for generating one or more farm-level
power curves for a wind farm that can be used to validate an
increase in energy production of a wind farm in response to one or
more upgrades being provided thereto would be advantageous.
BRIEF DESCRIPTION OF THE INVENTION
Aspects and advantages of the invention will be set forth in part
in the following description, or may be obvious from the
description, or may be learned through practice of the
invention.
In one aspect, the present disclosure is directed to a method for
generating one or more farm-level power curves for a wind farm that
can be used to validate an upgrade provided to the wind farm. The
method includes operating the wind farm in a first operational
mode. Another step includes collecting turbine-level operational
data from two or more of the wind turbines in the wind farm during
the first operational mode. The method also includes aggregating
the turbine-level operational data into a representative farm-level
time-series. Another step includes analyzing the operational data
collected during the first operational mode. Thus, the method also
includes generating one or more farm-level power curves for the
first operational mode based on the analyzed operational data.
In one embodiment, the step of aggregating the turbine-level
operational data into a representative farm-level time-series may
include utilizing at least one of data binning or regression
analysis. In another embodiment, the step of analyzing the
operational data collected during the first operational mode may
include summing power generated by two or more of the wind turbines
in the wind farm for the first operational mode.
In further embodiments, the method may further include operating
the wind farm in a second operational mode, the second operational
mode being characterized by one or more of the wind turbines being
provided with the upgrade, collecting turbine-level operational
data from one or more of the wind turbines in the wind farm during
the first operational mode, aggregating the turbine-level
operational data into a representative farm-level time-series,
analyzing the operational data collected during the second
operational mode, and generating one or more farm-level power
curves for the first and second operational modes based on the
analyzed operational data to assess a benefit of the upgrade.
In additional embodiments, the step of aggregating the
turbine-level operational data into the representative farm-level
time-series may include summing power generated by two or more of
the wind turbines in the wind farm for the first operational mode
and the second operational mode.
In another embodiment, the method may further include toggling or
switching between the first and second operational modes and
collecting operational data during each of the modes.
In yet another embodiment, the step of analyzing the operational
data collected during the first and second operational modes may
include mitigating loss of operational data. More specifically, in
certain embodiments, the step of mitigating loss of operational
data loss may include power scaling, sub-clustering, back-filling
the operational data with historic data, evaluating uncertainty of
the operational data, accounting for individual turbine operation
states, or any other suitable method of mitigating data loss.
In further embodiments, the step of generating one or more
farm-level power curves for the first operational mode (and/or the
second operational mode) based on the analyzed operational data may
include: binning the operational data collected during the first
operational mode by wind direction into a plurality of wind
sectors, excluding wind sectors with insufficient operational data,
and generating a sector-specific farm-level power curve for
non-excluded wind sectors.
In still additional embodiments, the method may include evaluating
the farm-level energy production for the first operational mode
based on at least one of the sector-specific farm-level power
curves and an expected wind rose and Weibull distribution.
In another embodiment, the method may further include generating a
predicted power curve for the first operational mode based on one
or more simulated wind conditions prior to operating the wind farm
in the first operational mode. In certain embodiment, the method
may further include substituting actual measurement data in place
of the simulated wind conditions where available during the first
operational mode and, where measurement data is not available,
adjusting the remaining simulated wind conditions via a realization
factor.
In further embodiments, the method may include generating a test
equivalent power curve based on observed wind conditions during the
first operational mode and generating a farm-level power curve
based on the predicted power curve and the test equivalent power
curve.
In certain embodiments, the operational data as described herein
may include any data of the wind farm and/or the individuals wind
turbines, including but not limited to power output, generator
speed, torque output, grid conditions, pitch angle, tip speed
ratio, yaw angle, internal control set points, loading conditions,
geographical information, temperature, pressure, wind turbine
location, wind farm location, weather conditions, wind gusts, wind
speed, wind direction, wind acceleration, wind turbulence, wind
shear, wind veer, wake, or similar.
In particular embodiments, the upgrade(s) as described herein may
include any one of or a combination of the following: a revised
pitch or yaw angle, tip speed ratio, rotor blade chord extensions,
software upgrades, controls upgrades, hardware upgrades, wake
controls, aerodynamic upgrades, blade tip extensions, vortex
generators, winglets, or any other suitable upgrades.
In another aspect, the present disclosure is directed to a method
for validating a benefit of an upgrade provided to a wind farm
having a plurality of wind turbines. The method includes operating
the wind farm in a first operational mode for a first time period.
The method also includes operating the wind farm in a second
operational mode for a second time period, the second operational
mode being characterized by one or more of the wind turbines being
provided with the upgrade. Further, the method includes analyzing
operational data collected during the first operational mode and
the second operational mode. Another step includes generating one
or more farm-level power curves for the first operational mode and
the second operational mode based on the analyzed operational data.
The method also includes determining a farm-level energy production
for the first operational mode and the second operational mode
based, at least in part, on the farm-level power curves for each
mode. Thus, the method also includes evaluating the farm-level
energy production for the first operational mode and the second
operational mode to assess the benefit of the upgrade.
In yet another aspect, the present disclosure is directed to a
system for validating a benefit of an upgrade provided to a wind
farm having a plurality of wind turbines. The system includes a
processor communicatively coupled to one or more sensors. The
processor is configured to perform one or more operations,
including but not limited to operating the wind farm in a first
operational mode, collecting turbine-level operational data from
one or more of the wind turbines in the wind farm during the first
operational mode, aggregating the turbine-level operational data
into a representative farm-level time-series, analyzing the
operational data collected during the first operational mode, and
generating one or more farm-level power curves for the first
operational mode based on the analyzed operational data.
These and other features, aspects and advantages of the present
invention will become better understood with reference the
following description and appended claims. The accompanying
drawings, which are incorporated in and constitute a part of this
specification, illustrate the embodiments of the invention and,
together with the description, serve to explain the principles of
the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
A full and enabling disclosure of the present invention, including
the best mode thereof, directed to one of ordinary skill in the
art, is set forth in the specification, which makes reference to
the appended figures, in which:
FIG. 1 illustrates a perspective view of one embodiment of a wind
turbine;
FIG. 2 illustrates a schematic view of one embodiment of a
controller for use with the wind turbine shown in FIG. 1;
FIG. 3 illustrates a schematic view of one embodiment of a wind
farm according to the present disclosure;
FIG. 4 illustrates a flow diagram of one embodiment of a method for
generating one or more farm-level power curves for a wind farm
having a plurality of wind turbines that can be used to validate an
upgrade provided to the wind farm according to the present
disclosure.
FIG. 5 illustrates a schematic diagram of one embodiment of a wind
turbine layout of a wind farm according to the present disclosure,
particularly illustrating interacting groups or sub-clusters of
wind turbines chosen as a function of wind direction and turbine
spacing;
FIG. 6 illustrates a schematic diagram of one embodiment of a wind
turbine layout of a wind farm according to the present disclosure,
particularly illustrating how operational data can be back-filled
with historic data;
FIG. 7 illustrates a flow diagram of one embodiment of a method for
validating a benefit of an upgrade provided to a wind farm having a
plurality of wind turbines according to the present disclosure;
FIG. 8 illustrates a graph of one embodiment of a cumulative
farm-level power curve generated according to the present
disclosure, particularly illustrating a linear portion of the
cumulative farm-level power curve having a reduced range of wind
speeds;
FIG. 9 illustrates a schematic diagram of one embodiment of a wind
rose and Weibull distribution according to the present
disclosure;
FIG. 10 illustrates one embodiment of a histogram of wind direction
(x-axis) versus number of measured data points (y-axis) according
to the present disclosure, particularly illustrating certain wind
direction sectors being excluded due to lack of data
availability;
FIG. 11 illustrates a graph of one embodiment of a sector-specific
farm-level power curve according to the present disclosure; and
FIG. 12 illustrates a graph of one embodiment of energy production
(x-axis) versus density (y-axis) for the first and second
operational modes according to the present disclosure.
DETAILED DESCRIPTION OF THE INVENTION
Reference now will be made in detail to embodiments of the
invention, one or more examples of which are illustrated in the
drawings. Each example is provided by way of explanation of the
invention, not limitation of the invention. In fact, it will be
apparent to those skilled in the art that various modifications and
variations can be made in the present invention without departing
from the scope or spirit of the invention. For instance, features
illustrated or described as part of one embodiment can be used with
another embodiment to yield a still further embodiment. Thus, it is
intended that the present invention covers such modifications and
variations as come within the scope of the appended claims and
their equivalents.
Generally, the present disclosure is directed to a system and
method for generating one or more farm-level power curves for a
wind farm that can be used to validate an increase in energy
production of a wind farm in response to one or more upgrades being
provided thereto. At the farm-level, several inflow assumptions
should be made before generating the farm-level power curves. Such
inflow assumptions are not necessary for individual or single wind
turbine power curve production. For example, the inflow wind
direction may be assumed to be the median wind direction of all of
the wind turbines in the wind farm. Further, the inflow wind speed
may be the median of all of the freestream wind turbines, i.e. the
forward-most wind turbines in the wind farm. In addition,
farm-level wake losses are highly dependent on the turbine
layout/wind direction and may also be considered when generating
the farm-level power curve. Thus, in one embodiment, the method
includes operating the wind farm in a first operational mode.
Another step includes collecting turbine-level operational data
from one or more of the wind turbines in the wind farm during the
first operational mode and aggregating the turbine-level
operational data into a representative farm-level time-series. The
method also includes analyzing the operational data collected
during the first operational mode. Thus, the method also includes
generating one or more farm-level power curves for the first
operational mode based on the analyzed operational data.
In another embodiment, the method may also include toggling between
the first operational mode and a second, upgraded operational mode
and collecting data during each mode. In such embodiments, the
method may also include generating one or more farm-level power
curves for each of the modes based on the analyzed operational
data. Thus, the method may include determining a farm-level energy
production for each mode based, at least in part, on the farm-level
power curves for each mode and evaluating the farm-level energy
production for each mode to assess a benefit of the upgrade.
The various embodiments of the system and method described herein
provide numerous advantages not present in the prior art. For
example, the present disclosure provides a system and method for
generating farm-level power curves that can be used for assessment
of expected energy production and/or performance differences
between various modes of turbine operation. Validating farm-level
performance, even in the absence of an upgraded operation mode has
advantages. For example, an operator of a wind farm without
upgrades, i.e. Running in baseline operation, may need to estimate
expected energy production relative to a long-term wind resource.
Conventional methods include using a single wind turbine power
curve, e.g. Based on commercial power curves or even a measured
power curvecollected at the site. The single turbine power curve
must then be extrapolated to an expected farm-level production by
accounting for additional farm-level considerations, e.g. wake
interactions, which is often handled with simplified engineering
models. In contrast, the farm-level power curves of the present
disclosure account for such interactions intrinsically.
In addition, the present disclosure addresses data quality analysis
at the farm level. Further, the present disclosure is configured to
use the maximum amount of collected data, while ensuring that the
data quality of the estimated energy production is not affected.
Thus, the present system corrects data quality issues arising at a
farm level, thereby addressing various challenges associated with
farm level power curve estimation.
Referring now to the drawings, FIG. 1 illustrates a perspective
view of one embodiment of a wind turbine 10 configured to implement
the control technology according to the present disclosure. As
shown, the wind turbine 10 generally includes a tower 12 extending
from a support surface 14, a nacelle 16 mounted on the tower 12,
and a rotor 18 coupled to the nacelle 16. The rotor 18 includes a
rotatable hub 20 and at least one rotor blade 22 coupled to and
extending outwardly from the hub 20. For example, in the
illustrated embodiment, the rotor 18 includes three rotor blades
22. However, in an alternative embodiment, the rotor 18 may include
more or less than three rotor blades 22. Each rotor blade 22 may be
spaced about the hub 20 to facilitate rotating the rotor 18 to
enable kinetic energy to be transferred from the wind into usable
mechanical energy, and subsequently, electrical energy. For
instance, the hub 20 may be rotatably coupled to an electric
generator (not shown) positioned within the nacelle 16 to permit
electrical energy to be produced.
The wind turbine 10 may also include a wind turbine controller 26
centralized within the nacelle 16. However, in other embodiments,
the controller 26 may be located within any other component of the
wind turbine 10 or at a location outside the wind turbine. Further,
the controller 26 may be communicatively coupled to any number of
the components of the wind turbine 10 in order to control the
operation of such components and/or to implement a control action.
As such, the controller 26 may include a computer or other suitable
processing unit. Thus, in several embodiments, the controller 26
may include suitable computer-readable instructions that, when
implemented, configure the controller 26 to perform various
different functions, such as receiving, transmitting and/or
executing wind turbine control signals. Accordingly, the controller
26 may generally be configured to control the various operating
modes of the wind turbine 10 (e.g., start-up or shut-down
sequences), de-rate or up-rate the wind turbine 10, and/or control
various components of the wind turbine 10. For example, the
controller 26 may be configured to control the blade pitch or pitch
angle of each of the rotor blades 22 (i.e., an angle that
determines a perspective of the rotor blades 22 with respect to the
direction of the wind) to control the power output generated by the
wind turbine 10 by adjusting an angular position of at least one
rotor blade 22 relative to the wind. For instance, the controller
26 may control the pitch angle of the rotor blades 22 by rotating
the rotor blades 22 about a pitch axis 28, either individually or
simultaneously, by transmitting suitable control signals to a pitch
drive or pitch adjustment mechanism (not shown) of the wind turbine
10.
Referring now to FIG. 2, a block diagram of one embodiment of
suitable components that may be included within the controller 26
is illustrated in accordance with aspects of the present
disclosure. As shown, the controller 26 may include one or more
processor(s) 58 and associated memory device(s) 60 configured to
perform a variety of computer-implemented functions (e.g.,
performing the methods, steps, calculations and the like disclosed
herein). As used herein, the term "processor" refers not only to
integrated circuits referred to in the art as being included in a
computer, but also refers to a controller, a microcontroller, a
microcomputer, a programmable logic controller (PLC), an
application specific integrated circuit, application-specific
processors, digital signal processors (DSPs), Application Specific
Integrated Circuits (ASICs), Field Programmable Gate Arrays
(FPGAs), and/or any other programmable circuits. Further, the
memory device(s) 60 may generally include memory element(s)
including, but are not limited to, computer readable medium (e.g.,
random access memory (RAM)), computer readable non-volatile medium
(e.g., a flash memory), one or more hard disk drives, a floppy
disk, a compact disc-read only memory (CD-ROM), compact
disk-read/write (CD-R/W) drives, a magneto-optical disk (MOD), a
digital versatile disc (DVD), flash drives, optical drives,
solid-state storage devices, and/or other suitable memory
elements.
Additionally, the controller 26 may also include a communications
module 62 to facilitate communications between the controller 26
and the various components of the wind turbine 10. For instance,
the communications module 62 may include a sensor interface 64
(e.g., one or more analog-to-digital converters) to permit the
signals transmitted by one or more sensors 65, 66, 68 to be
converted into signals that can be understood and processed by the
controller 26. Furthermore, it should be appreciated that the
sensors 65, 66, 68 may be communicatively coupled to the
communications module 62 using any suitable means. For example, as
shown in FIG. 2, the sensors 65, 66, 68 are coupled to the sensor
interface 64 via a wired connection. However, in alternative
embodiments, the sensors 65, 66, 68 may be coupled to the sensor
interface 64 via a wireless connection, such as by using any
suitable wireless communications protocol known in the art. For
example, the communications module 62 may include the Internet, a
local area network (LAN), wireless local area networks (WLAN), wide
area networks (WAN) such as Worldwide Interoperability for
Microwave Access (WiMax) networks, satellite networks, cellular
networks, sensor networks, ad hoc networks, and/or short-range
networks. As such, the processor 58 may be configured to receive
one or more signals from the sensors 65, 66, 68.
The sensors 65, 66, 68 may be any suitable sensors configured to
measure any operational data of the wind turbine 10 and/or wind
parameters of the wind farm 200. For example, the sensors 65, 66,
68 may include blade sensors for measuring a pitch angle of one of
the rotor blades 22 or for measuring a loading acting on one of the
rotor blades 22; generator sensors for monitoring the generator
(e.g. torque, rotational speed, acceleration and/or the power
output); and/or various wind sensors for measuring various wind
parameters (e.g. wind speed, wind direction, etc.). Further, the
sensors 65, 66, 68 may be located near the ground of the wind
turbine 10, on the nacelle 16, on a meteorological mast of the wind
turbine 10, or any other location in the wind farm.
It should also be understood that any other number or type of
sensors may be employed and at any location. For example, the
sensors may be accelerometers, pressure sensors, strain gauges,
angle of attack sensors, vibration sensors, MIMU sensors, camera
systems, fiber optic systems, anemometers, wind vanes, Sonic
Detection and Ranging (SODAR) sensors, infra lasers, Light
Detecting and Ranging (LIDAR) sensors, radiometers, pitot tubes,
rawinsondes, other optical sensors, and/or any other suitable
sensors. It should be appreciated that, as used herein, the term
"monitor" and variations thereof indicates that the various sensors
of the wind turbine 10 may be configured to provide a direct
measurement of the parameters being monitored or an indirect
measurement of such parameters. Thus, the sensors 65, 66, 68 may,
for example, be used to generate signals relating to the parameter
being monitored, which can then be utilized by the controller 26 to
determine the actual condition.
Referring now to FIG. 3, a wind farm 200 that is controlled
according to the system and method of the present disclosure is
illustrated. As shown, the wind farm 200 may include a plurality of
wind turbines 202, including the wind turbine 10 described above,
and a farm controller 220. For example, as shown in the illustrated
embodiment, the wind farm 200 includes twelve wind turbines,
including wind turbine 10. However, in other embodiments, the wind
farm 200 may include any other number of wind turbines, such as
less than twelve wind turbines or greater than twelve wind
turbines. In one embodiment, the controller 26 of the wind turbine
10 may be communicatively coupled to the farm controller 220
through a wired connection, such as by connecting the controller 26
through suitable communicative links 222 (e.g., a suitable cable).
Alternatively, the controller 26 may be communicatively coupled to
the farm controller 220 through a wireless connection, such as by
using any suitable wireless communications protocol known in the
art. In addition, the farm controller 220 may be generally
configured similar to the controllers 26 for each of the individual
wind turbines 202 within the wind farm 200.
In several embodiments, one or more of the wind turbines 202 in the
wind farm 200 may include a plurality of sensors for monitoring
various operational data of the individual wind turbines 202 and/or
one or more wind parameters of the wind farm 200. For example, as
shown, each of the wind turbines 202 includes a wind sensor 216,
such as an anemometer or any other suitable device, configured for
measuring wind speeds or any other wind parameter. For example, in
one embodiment, the wind parameters include information regarding
at least one of or a combination of the following: a wind gust, a
wind speed, a wind direction, a wind acceleration, a wind
turbulence, a wind shear, a wind veer, a wake, SCADA information,
or similar.
As is generally understood, wind speeds may vary significantly
across a wind farm 200. Thus, the wind sensor(s) 216 may allow for
the local wind speed at each wind turbine 202 to be monitored. In
addition, the wind turbine 202 may also include one or more
additional sensors 218. For instance, the sensors 218 may be
configured to monitor electrical properties of the output of the
generator of each wind turbine 202, such as current sensors,
voltage sensors, temperature sensors, or power sensors that monitor
power output directly based on current and voltage measurements.
Alternatively, the sensors 218 may include any other sensors that
may be utilized to monitor the power output of a wind turbine 202.
It should also be understood that the wind turbines 202 in the wind
farm 200 may include any other suitable sensor known in the art for
measuring and/or monitoring wind parameters and/or wind turbine
operational data.
Referring now to FIG. 4, one embodiment of a method 100 for
generating one or more farm-level power curves for a wind farm 200
having a plurality of wind turbines 202 that can be used to
validate an upgrade provided to the wind farm 200 is illustrated.
For example, in one embodiment, the farm controller 220 or the
individual wind turbine controllers 26 may be configured to perform
any of the steps of the method 100 as described herein. Further, in
additional embodiments, the method 100 of the present disclosure
may be performed manually via a separate computer not associated
with the wind farm 200. As independent optimization of the wind
turbines 202 may further actually decrease overall energy
production of the wind farm 200, it is desirable to configure
operation of the wind turbines 202 such that the farm-level energy
output is increased.
Thus, as shown at 102, the method 100 includes operating the wind
farm 200 in a first operational mode. As shown at 104, the method
102 includes collecting turbine-level operational data from one or
more of the wind turbines 202 in the wind farm 200 during the first
operational mode. For example, in certain embodiments, the wind
farm 200 may be operated in the first operational mode for days,
weeks, months, or longer. Thus, in certain embodiments, the
controllers 26, 220 may be configured to collect operational data
from each of the wind turbines 202 in the wind farm 200 during the
first operational mode. In one embodiment, the wind parameters
and/or the operational data may be generated via one or more of the
sensors (e.g. via sensors 65, 66, 68, 216, 218, or any other
suitable sensor). In addition, the wind parameters and/or the
operational data may be determined via a computer model within the
one of the controllers 26, 220 to reflect the real-time conditions
of the wind farm 200.
Thus, the operational data is collected during each of the
operational modes for further analysis. The operational data as
described herein may include information regarding at least one of
or a combination of the following: power output, generator speed,
torque output, grid conditions, tip speed ratio, pitch angle, yaw
angle, internal control set points, an operational state of the
wind turbine, loading conditions, geographical information,
temperature, date/time, pressure, wind turbine location, wind farm
location, weather conditions, wind gusts, wind speed, wind
direction, wind acceleration, wind turbulence, wind shear, wind
veer, wake, or similar.
As shown at 106, the method 100 includes aggregating the
turbine-level operational data into a representative farm-level
time-series. Further, as shown at 108, the method 100 includes
analyzing the operational data collected during the first
operational mode. The controllers 26, 220 or separate computer (not
shown) may be configured to aggregate and/or analyze the
operational data in a variety of ways. For example, in one
embodiment, one or more data quality algorithms may be utilized to
process the operational data. In additional embodiments, the
controllers 26, 220 may be configured to filter, average, and/or
adjust the one or more operational data. More specifically, the
data quality algorithms may be configured so as to filter one or
more outliers, account for missing data points, and/or any other
suitable processing steps. Thus, the data quality algorithms
provide a framework to better manage the trade-off between data
availability (e.g. by parameter, by time) and analysis quality.
The most basic approach for a farm-level power curve/energy
assessment requires 100% of the turbines 202 in the wind farm 200
to simultaneously be operating such that each turbine 202 meets
certain inclusion criteria. For example, in certain instances, the
inclusion criteria may include any one or more of the following: in
full/partial load, without any curtailment (both internal and
external), standard deviation of wind speed at a reference turbine,
or wind direction across all turbines, and without any other
non-nominal behavior active (e.g. iced operation). In other words,
if any one turbine 202 does not meet these inclusion criteria at a
given time, that timestamp is thrown out for all turbines 202
causing a loss of usable operational data.
Thus, in certain embodiments, the step of analyzing the operational
data collected during the various operational modes may include
mitigating loss of operational data, e.g. due to farm-level
filtering of individual wind turbine availability, curtailment,
error in data transmission, non-normal wind turbine operation (such
as during icing events), or any other data that is removed in
single-wind-turbine power curve generation. More specifically, in
certain embodiments, the step of mitigating loss of operational
data loss may include power scaling, sub-clustering, back-filling
the operational data with historic data, evaluating uncertainty of
the operational data, accounting for individual turbine operation
states, or any other suitable method of mitigating data loss.
Power scaling uses a scalar to scale-up the cumulative power of the
wind turbines 202 that meet the inclusion criteria to a
representative total farm-level power. In certain embodiments, for
each time step, Equation (1) below can be used to determine the
cumulative farm power:
.times..times..times..times..times. ##EQU00001## Where
P.sub.F=cumulative farm power, P.sub.i=power from individual
turbine, N=total number of wind turbines in the wind farm, and
n=number of wind turbines that meet inclusion criteria.
Further, the controllers 26, 220 or separate computer may be
configured to set a threshold of the wind farm 200 (i.e. a number
of wind turbines) that must meet the inclusion criteria in order to
use power scaling to maintain accuracy.
Sub-clustering involves dividing the wind farm 200 into smaller
groups of interacting turbines 202, processing each group
individually, and then summing or aggregating back up to
farm-level. Sub-clusters may be chosen based on a variety of
criteria, including for example, location of a wind turbine 202 in
the wind farm 200 (i.e. upstream or downstream), wake interactions,
geographical conditions, wind conditions, turbine type, or any
other suitable criteria. Interacting groups of wind turbines 202
may vary as a function of wind direction and turbine spacing as
shown in FIGS. 5(A) and 5(B). Thus, as shown, each wind turbine 202
can be assigned to a sub-cluster 70 based on interaction with
neighboring wind turbines 202 for each wind direction. Isolated
wind turbines 202 that do not interact can be in their own
sub-group, i.e. with one turbine 202 in each group. Thus, if a wind
turbine 202 does not pass the inclusion criteria, only that
sub-cluster loses that point in the time-series, rather than the
entire wind farm 200. As will be discussed in more detail below,
for each wind direction, a power curve can be developed for each
sub-cluster 70. The sub-cluster power curves can then be combined
to equate to a farm-level power curve. Alternatively, a cluster of
wind turbines 202 can be defined per wind turbine x. Thus, the
cluster encloses all turbines that impact the performance of
turbine x, for example due to wake effects or operational
decisions. As the cluster per wind turbine is per definition
smaller or equal to the above mentioned sub-cluster, more data can
be retained. As such, the farm-level power curve can then be a
combination of the turbine-level power curves.
Back-filling the operational data generally refers to replacing
missing data with surrogate data that is similar in nature.
Further, back-filling the operational data with historic data can
be better understood with reference to FIG. 6. More specifically,
replacing a wind turbine 202 that fails the inclusion criteria with
surrogate data from another time period representing either the
same inflow or local conditions, allows for another method of
mitigating loss of data. In a preferred embodiment, the wind
turbine(s) 202 that fails the inclusion criteria and all
interacting neighboring wind turbines 202 can be replaced with data
from another time period representing the same inflow conditions.
For example, as shown in FIG. 6, four neighboring wind turbines 202
are illustrated for two different time periods (i.e. Time A and
Time B) having the same wind conditions (i.e. wind speed, wind
direction, etc.). Only three of the wind turbines 202 are close
enough to be considered neighbors as indicated by the dotted box
72. The only difference between Time A and Time B is that the
middle turbine in Time B does not meet the inclusion criteria, as
indicated by the X. As such, given the wind direction, the excluded
turbine 202 has two neighbors it would interact with, and so all
three turbines in Time B are replaced with power produced in Time
A. It should be understood that such a substitution may be done on
any size group of impacted turbines 202 in the wind farm 200 to
mitigate data loss.
Referring back to FIG. 4, as shown at 110, the method 100 further
includes generating one or more farm-level power curves for the
first operational mode based on the analyzed operational data,
which will be described in more detail below in reference to FIGS.
8-14.
Referring now to FIG. 7, a flow diagram of one embodiment of a
method 250 for validating a benefit, e.g. an increase in energy
production, of a wind farm 200 in response to one or more upgrades
being provided to one or more of the wind turbines 202 therein is
illustrated according to the present disclosure. Thus, as shown at
252, the method 250 includes operating the wind farm 200 in a
baseline or first operational mode for a first time period. As
shown at 254, the method 250 also includes optionally operating the
wind farm 200 in a second operational mode for a second time
period. More specifically, the second operational mode is typically
characterized by one or more of the wind turbines 202 being
provided with an upgrade. As such, in certain embodiments, the farm
controller 220 may operate the wind farm 200 by toggling between
the first and second operational modes or may simply operate the
wind farm 200 in a subsequent manner, i.e. by first operating in
the first operational mode and then operating in the second
operational mode.
Further, in particular embodiments, the upgrade(s) as described
herein may include any one of or a combination of the following:
revised pitch or yaw angles or tip speed ratio, rotor blade chord
extensions, software upgrades, controls upgrades, hardware
upgrades, wake controls, aerodynamic upgrades, blade tip
extensions, vortex generators, winglets, or any other suitable
upgrades.
More specifically, as shown at 256, the method 250 may include
analyzing the operational data collected during the first
operational mode and/or the second operational mode. It should be
understood that the operational data may be analyzed according to
any of the methods as described herein, for example, in reference
to FIG. 4. As shown at 258, the method 250 may also include
generating one or more farm-level power curves for the first and
second operational modes based on the analyzed operational data.
For example, as shown in FIG. 8, the method 250 may further include
determining a cumulative farm-level power for the first operational
mode 88 and optionally the second operational mode 90 based on the
analyzed operational data, e.g. using power scaling,
sub-clustering, or back-filling, and generating a farm-level power
curve for each mode based on the cumulative farm-level power. In
such an embodiment, the cumulative farm-level power for the first
and the second operational modes may include a time-series
cumulative farm-level power. Thus, as mentioned, the step of
determining the time-series cumulative farm-level power for the
first and second operational modes may include summing power
generated by each wind turbine 202 in the wind farm 200 for the
first and second operational modes at each time period.
In particular embodiments, the farm-level power curves 88, 90 for
the first and/or second operational modes may be generated using
data binning or regression analysis. For example, for regression
analysis, the controllers 26, 220 or a separate computer may be
configured to utilize a multi-parameter (e.g. four parameters)
logistic cumulative distribution fit through data collected during
the different operational modes. For data binning, the controllers
26, 220 may be configured to bin the operational data, e.g. in 0.5
meter/second (m/s) intervals or any other suitable internal. In
addition, in one embodiment, the controllers 26, 220 are configured
to use an average wind speed for each bin. It should be understood
that either data binning or regression analysis may be implemented
for bulk or sector-specific power curves, which will be described
in more detail below. In addition, data binning or regression
analysis may also require removal of outliers and/or limiting wind
speed range to where sufficient data is available.
For example, in certain embodiments, the step of generating one or
more farm-level power curves 88, 90 for the first and/or second
operational modes based on the analyzed operational data may
include binning the operational data from the first and/or second
operational modes by wind direction into a plurality of wind
sectors (FIG. 10), excluding wind sectors with insufficient
operational data (FIG. 10), and generating a sector-specific
farm-level power curve for non-excluded wind sectors (FIG. 11). As
used herein, a wind sector may be any size, including 1 degree up
to 360 degrees. More specifically, as shown in FIG. 9, a wind rose
and Weibull distribution 80 is illustrated particularly depicting
the Weibull shape 82, the Weibull scale 84, and the frequency 86 of
occurrence of a particular wind direction. Further, as shown in the
illustrated example of FIG. 11, measurement-based results would
only be shown for sectors 165.degree.-220.degree. due to limited
data availability elsewhere. Moreover, as shown in FIG. 10, the
minimum available data requirement or threshold 78 may be set such
that any sector and/or wind speed bin not meeting the threshold 78
is excluded.
More specifically, farm-level power curves typically vary with
respect to wind direction due to differences in wake loss as a
function of turbine layout in the wind farm 200 as viewed from the
incoming wind. As such, FIG. 11 illustrates a representative
sector-wise farm-level power curve for the first operational mode.
Further, FIG. 11 illustrates power curves for all thirty-six (36)
sectors.
In yet another embodiment, the method 250 may include determining a
predicted farm-level power curve for the first and/or second
operational modes based on one or more estimated wind conditions
prior to operating the wind farm in the different modes. As such,
pre-test predictions are typically simulation-based only. As the
wind farm 200 is operated in the different operational modes based
on actual wind conditions, the controller(s) 26, 202 or a separate
computer is configured to compare the predicted farm-level power
curves with actual wind conditions collected during the first
and/or second operational modes and create an equivalent farm-level
power curve based on the comparison.
It is often desirable to adjust the initial simulation-based
performance expectation as measurement data becomes available,
thus, measurement data may substituted in place of pre-test
predictions. Where such data is not available, measurement-based
scaling may be substituted for all remaining pre-test predictions
that are not directly replaced by measurement equivalents. Further,
one or more assumptions can be made that if additional wind speeds
and sectors had been observed, they would have exhibited an
equivalent test-specific realization factor. This enables the
remaining pre-test predictions that have not already been replaced
by measurement to be scaled by the test-specific realization
factor. More specifically, in certain embodiments, pre-test
prediction data can be scaled using Equation (2) below: S=P*R
Equation (2) Where S is the scaled value, P is the predicted value
for each bin, and R is the realization factor, such as
0.5.ltoreq.R.ltoreq.1.3.
Thus, the realization factor is a ratio that represents how much
benefit was observed between the first and/or second operational
modes relative to the expectation or prediction. Similarly, a
realization factor may be calculated and applied to the first
and/or second operational mode directly. Further, in certain
embodiments, the realization factor can be based on historic
observation, as well as site/test-specific values. Realization
factors may be calculated and/or applied in a number of ways,
including but not limited to a single site-specific value, a single
value derived from observation at one or more other wind farms, a
site-specific wind speed bin-specific value derived from valid
sectors at the test site, and/or a wind speed bin-specific
observation at one or more other wind farms. Realization factors
may also consider a number of other criteria as well, such as being
representative of performance across the entire wind speed range
over which the wind turbine operates, or be restricted to only
consider and apply to a smaller range of wind speeds, and/or vary
as a function of wind speed across the full or partial wind speed
range over which the wind turbine operates.
Thus, Equation (3) represents one embodiment of using the
realization factor to estimate a difference in energy production
between operational modes using a measurement-based scalar:
.DELTA.E=R(E.sub.1--E.sub.2) Equation (3) Wherein .DELTA.E is the
additional energy production of the second operational mode, R is
the realization factor, such as 0.5.ltoreq.R.ltoreq.1.3, E.sub.1 is
the energy production of the first operational mode, and E.sub.2 is
the energy production of the second operational mode.
In additional embodiments, the controller(s) 26, 220 (as well as
any other suitable processing means) is also configured to validate
trends of the predictive model using the pre-test predictions. More
specifically, in such embodiments, the measurement data is used
as-is with no extrapolation. Thus, the controller(s) 26, 220 or
separate computer is configured to generate a test equivalent
prediction based on simulated sector-wise power curves and observed
wind speeds and/or directions. The measured test and the test
equivalent can then be used to generate a farm-level power curve
for the first and/or second operational modes of the wind farm 200.
A wind rose and Weibull distribution may be applied to each curve
to estimate a representative energy contribution. Further, the
controller(s) 26, 220 or separate computer may determine a gain for
the first and/or second operational modes that can be assessed for
both the measured test and the test equivalent. In certain
embodiments, the controller(s) 26, 220 or separate computer may
also determine a realization factor using Equation (4) below:
.times..times..times..times..times..times..times..times.
##EQU00002##
In additional embodiments, as mentioned, the controller(s) 26, 220
(as well as any other suitable processing means) is also configured
to evaluate uncertainty of the operational data. For example,
bootstrapping may be used to generate a plurality of power curve
fits using data replicates to be used for uncertainty analysis.
Further, these power curves used in conjunction with a wind
resource assumption provides a plurality of energy values that can
be viewed as an energy histogram (FIG. 12). If there is minimal or
no overlap between the different operational modes, the result is
statistically significant, i.e. a benefit of the second operational
mode is realized. It should be understood that additional
uncertainty methods may also be used in addition to
bootstrapping.
After generating the power curves, the method 250 may also include
determining a farm-level energy production for the first
operational mode and the second operational mode based, at least in
part, on the farm-level power curves for each mode as shown at 260
of FIG. 7. Thus, as shown at 262, the method 250 also includes
evaluating the farm-level energy production for the first
operational mode and the second operational mode to assess a
benefit of the upgrade. More specifically, in certain embodiments,
the method 100 may include evaluating the farm-level energy
production for the first and second operational modes based on at
least one of the sector-specific farm-level power curves and an
expected wind rose and Weibull distribution. Thus, as shown in FIG.
12, the power curve (FIG. 8) and the expected wind rose and Weibull
distribution (FIG. 9) may be used in conjunction to calculate the
energy contribution for the first operational mode 74 and the
second operational mode 76. For example, as shown in the
illustrated embodiment, the second operational mode 76 produces
more energy than the first operational mode 74, which is the
desired outcome when one or more upgrades have been provided to the
wind farm 200.
Exemplary embodiments of a wind farm, a controller for a wind farm,
and a method for controlling a wind farm are described above in
detail. The method, wind farm, and controller are not limited to
the specific embodiments described herein, but rather, components
of the wind turbines and/or the controller and/or steps of the
method may be utilized independently and separately from other
components and/or steps described herein. For example, the
controller and method may also be used in combination with other
power systems and methods, and are not limited to practice with
only the wind turbine controller as described herein. Rather, the
exemplary embodiment can be implemented and utilized in connection
with many other wind turbine or power system applications.
Although specific features of various embodiments of the invention
may be shown in some drawings and not in others, this is for
convenience only. In accordance with the principles of the
invention, any feature of a drawing may be referenced and/or
claimed in combination with any feature of any other drawing.
This written description uses examples to disclose the invention,
including the best mode, and also to enable any person skilled in
the art to practice the invention, including making and using any
devices or systems and performing any incorporated methods. The
patentable scope of the invention is defined by the claims, and may
include other examples that occur to those skilled in the art. Such
other examples are intended to be within the scope of the claims if
they include structural elements that do not differ from the
literal language of the claims, or if they include equivalent
structural elements with insubstantial differences from the literal
languages of the claims.
* * * * *