US10385829B2 - System and method for validating optimization of a wind farm - Google Patents
System and method for validating optimization of a wind farm Download PDFInfo
- Publication number
- US10385829B2 US10385829B2 US15/151,573 US201615151573A US10385829B2 US 10385829 B2 US10385829 B2 US 10385829B2 US 201615151573 A US201615151573 A US 201615151573A US 10385829 B2 US10385829 B2 US 10385829B2
- Authority
- US
- United States
- Prior art keywords
- wind
- farm
- operational
- level
- operational data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active, expires
Links
- 238000000034 method Methods 0.000 title claims abstract description 81
- 238000005457 optimization Methods 0.000 title description 4
- 230000004931 aggregating effect Effects 0.000 claims abstract description 13
- 238000004519 manufacturing process Methods 0.000 claims description 24
- 238000012360 testing method Methods 0.000 claims description 20
- 238000005259 measurement Methods 0.000 claims description 19
- 230000008901 benefit Effects 0.000 claims description 18
- 230000000116 mitigating effect Effects 0.000 claims description 11
- 238000009826 distribution Methods 0.000 claims description 8
- 238000000611 regression analysis Methods 0.000 claims description 7
- 230000001133 acceleration Effects 0.000 claims description 5
- 230000001186 cumulative effect Effects 0.000 description 11
- 238000004891 communication Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 5
- 238000013459 approach Methods 0.000 description 4
- 230000003993 interaction Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000036961 partial effect Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000001739 density measurement Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000013213 extrapolation Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000000135 prohibitive effect Effects 0.000 description 1
- 230000002829 reductive effect Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 238000013341 scale-up Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000013076 uncertainty analysis Methods 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/028—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling wind motor output power
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/04—Automatic control; Regulation
- F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
- F03D7/048—Automatic control; Regulation by means of an electrical or electronic controller controlling wind farms
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D9/00—Adaptations of wind motors for special use; Combinations of wind motors with apparatus driven thereby; Wind motors specially adapted for installation in particular locations
- F03D9/20—Wind motors characterised by the driven apparatus
- F03D9/25—Wind motors characterised by the driven apparatus the apparatus being an electrical generator
- F03D9/255—Wind motors characterised by the driven apparatus the apparatus being an electrical generator connected to electrical distribution networks; Arrangements therefor
- F03D9/257—Wind motors characterised by the driven apparatus the apparatus being an electrical generator connected to electrical distribution networks; Arrangements therefor the wind motor being part of a wind farm
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/10—Purpose of the control system
- F05B2270/20—Purpose of the control system to optimise the performance of a machine
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/30—Control parameters, e.g. input parameters
- F05B2270/335—Output power or torque
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
-
- Y02E10/723—
Definitions
- the present invention relates generally to wind farms, and more particularly, to systems and methods for validating optimization of a wind farm.
- 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.
- 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.
- 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.
- various turbine components such as the generator/power converter, the pitch system, the brakes, and the yaw mechanism to reach the maximum power efficiency.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- the method may further include toggling or switching between the first and second operational modes and collecting operational data during each of the modes.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- the inflow wind direction may be assumed to be the median wind direction of all of the wind turbines in the wind farm.
- 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.
- 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.
- the method may also include toggling between the first operational mode and a second, upgraded operational mode and collecting data during each mode.
- the method may also include generating one or more farm-level power curves for each of the modes based on the analyzed operational data.
- 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 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.
- 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.
- farm-level power curves of the present disclosure account for such interactions intrinsically.
- 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.
- 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.
- 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 .
- the rotor 18 includes three rotor blades 22 .
- 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.
- 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 .
- the controller 26 may be located within any other component of the wind turbine 10 or at a location outside the wind turbine.
- 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.
- the controller 26 may include a computer or other suitable processing unit.
- 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.
- 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 .
- 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.
- 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 .
- 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).
- 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.
- PLC programmable logic controller
- DSPs digital signal processors
- ASICs Application Specific Integrated Circuits
- FPGAs Field Programmable Gate Arrays
- 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.
- computer readable medium e.g., random access memory (RAM)
- computer readable non-volatile medium e.g., a flash memory
- CD-ROM compact disc-read only memory
- CD-R/W compact disk-read/write
- MOD magneto-optical disk
- DVD digital versatile disc
- flash drives optical drives, solid-state storage devices, and/or other suitable memory elements.
- 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 .
- 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 .
- 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.
- 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.
- 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.
- 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 .
- 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.).
- 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.
- 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.
- SODAR Sonic Detection and Ranging
- LIDAR Light Detecting and Ranging
- radiometers pitot tubes, rawinsondes, other optical sensors, and/or any other suitable sensors.
- 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.
- 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.
- the wind farm 200 may include a plurality of wind turbines 202 , including the wind turbine 10 described above, and a farm controller 220 .
- the wind farm 200 includes twelve wind turbines, including wind turbine 10 .
- 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.
- 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).
- 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.
- 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 .
- 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 .
- 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.
- 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.
- wind speeds may vary significantly across a wind farm 200 .
- the wind sensor(s) 216 may allow for the local wind speed at each wind turbine 202 to be monitored.
- the wind turbine 202 may also include one or more additional sensors 218 .
- 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.
- the sensors 218 may include any other sensors that may be utilized to monitor the power output of a wind turbine 202 .
- 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.
- 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.
- 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.
- the method 100 of the present disclosure may be performed manually via a separate computer not associated with the wind farm 200 .
- 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.
- the method 100 includes operating the wind farm 200 in a first operational mode.
- 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.
- the wind farm 200 may be operated in the first operational mode for days, weeks, months, or longer.
- 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.
- 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).
- 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 .
- 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.
- 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 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 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).
- curtailment both internal and external
- standard deviation of wind speed at a reference turbine or wind direction across all turbines
- non-nominal behavior active e.g. iced operation
- 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.
- Equation (1) can be used to determine the cumulative farm power:
- P F cumulative farm power
- P i power from individual turbine
- N total number of wind turbines in the wind farm
- n number of wind turbines that meet inclusion criteria.
- 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) .
- 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 .
- 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.
- a cluster of wind turbines 202 can be defined per wind turbine x.
- the cluster encloses all turbines that impact the performance of turbine x, for example due to wake effects or operational decisions.
- 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.
- 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.
- 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 .
- the method 250 includes operating the wind farm 200 in a baseline or first operational mode for a first time period.
- 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.
- 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.
- 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.
- 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.
- the cumulative farm-level power for the first and the second operational modes may include a time-series cumulative farm-level power.
- 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.
- the farm-level power curves 88 , 90 for the first and/or second operational modes may be generated using data binning or 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.
- 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.
- the controllers 26 , 220 are configured to use an average wind speed for each bin.
- data binning or regression analysis may be implemented for bulk or sector-specific power curves, which will be described in more detail below.
- data binning or regression analysis may also require removal of outliers and/or limiting wind speed range to where sufficient data is available.
- 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 ).
- a wind sector may be any size, including 1 degree up to 360 degrees. More specifically, as shown in FIG.
- 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°-220° 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.
- 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.
- 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.
- 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.
- 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.
- a realization factor may be calculated and applied to the first and/or second operational mode directly.
- 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.
- 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.
- 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.
- 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.
- controller(s) 26 , 220 or separate computer may also determine a realization factor using Equation (4) below:
- 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.
- 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 .
- 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.
- 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.
- the power curve ( FIG. 8 ) and the expected wind rose and Weibull distribution FIG.
- the second operational mode 76 may be used in conjunction to calculate the energy contribution for the first operational mode 74 and the second operational mode 76 .
- 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.
- 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.
Abstract
Description
Where PF=cumulative farm power,
Pi=power from individual turbine,
N=total number of wind turbines in the wind farm, and
n=number of wind turbines that meet inclusion criteria.
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≤R≤1.3.
ΔE=R(E 1 —E 2) Equation (3)
Wherein ΔE is the additional energy production of the second operational mode,
R is the realization factor, such as 0.5≤R≤1.3,
E1 is the energy production of the first operational mode, and
E2 is the energy production of the second operational mode.
Claims (16)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/151,573 US10385829B2 (en) | 2016-05-11 | 2016-05-11 | System and method for validating optimization of a wind farm |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/151,573 US10385829B2 (en) | 2016-05-11 | 2016-05-11 | System and method for validating optimization of a wind farm |
Publications (2)
Publication Number | Publication Date |
---|---|
US20170328348A1 US20170328348A1 (en) | 2017-11-16 |
US10385829B2 true US10385829B2 (en) | 2019-08-20 |
Family
ID=60295148
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/151,573 Active 2037-06-11 US10385829B2 (en) | 2016-05-11 | 2016-05-11 | System and method for validating optimization of a wind farm |
Country Status (1)
Country | Link |
---|---|
US (1) | US10385829B2 (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10982653B2 (en) * | 2016-06-07 | 2021-04-20 | Vestas Wind Systems A/S | Adaptive control of a wind turbine by detecting a change in performance |
US20210231103A1 (en) * | 2018-06-08 | 2021-07-29 | Siemens Gamesa Renewable Energy A/S | Controlling wind turbines in presence of wake interactions |
Families Citing this family (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10296983B2 (en) * | 2014-12-08 | 2019-05-21 | Carolina Carbajal De Nova | How to model risk on your farm |
US10724499B2 (en) * | 2015-12-23 | 2020-07-28 | Vestas Wind Systems A/S | Controlling wind turbines according to reliability estimates |
US10260481B2 (en) | 2016-06-28 | 2019-04-16 | General Electric Company | System and method for assessing farm-level performance of a wind farm |
DE102018112825A1 (en) * | 2018-05-29 | 2019-12-05 | fos4X GmbH | Sensor arrangement for a wind turbine |
CN111401596B (en) * | 2019-01-03 | 2022-07-08 | 新疆金风科技股份有限公司 | Method and device for generating wind speed index |
CN109812389A (en) * | 2019-01-31 | 2019-05-28 | 湖南工程学院 | A kind of wind-driven generator power quality and health status comprehensive monitoring and controlling method |
US10815972B2 (en) | 2019-03-22 | 2020-10-27 | General Electric Company | System and method for assessing and validating wind turbine and wind farm performance |
CN110296055B (en) * | 2019-06-10 | 2020-07-28 | 同济大学 | Wind direction prediction associated seed unit screening method |
EP3967871B1 (en) | 2020-09-14 | 2022-11-02 | Nordex Energy SE & Co. KG | A method of operating a wind turbine |
CN113565702A (en) * | 2021-03-31 | 2021-10-29 | 中国大唐集团新能源科学技术研究院有限公司 | In-service wind power plant multidimensional intelligent evaluation system |
CN113255227A (en) * | 2021-06-03 | 2021-08-13 | 龙源(北京)风电工程技术有限公司 | Wind power plant equivalent modeling method based on improved FCM clustering algorithm |
US11649804B2 (en) | 2021-06-07 | 2023-05-16 | General Electric Renovables Espana, S.L. | Systems and methods for controlling a wind turbine |
Citations (68)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6668656B2 (en) | 1998-12-04 | 2003-12-30 | Weatherford/Lamb, Inc. | Optical sensor having differing diameters |
US6724097B1 (en) | 1999-10-06 | 2004-04-20 | Aloys Wobben | Method for operating a wind farm |
US20050090937A1 (en) * | 2003-10-22 | 2005-04-28 | Gary Moore | Wind turbine system control |
US7025567B2 (en) | 2001-07-31 | 2006-04-11 | Aloys Wobben | Early-warning system for wind power installations |
US20070124025A1 (en) | 2005-11-29 | 2007-05-31 | General Electric Company | Windpark turbine control system and method for wind condition estimation and performance optimization |
EP1817496A1 (en) | 2004-11-22 | 2007-08-15 | REpower Systems AG | Method for optimising the operation of wind farms |
US7299627B2 (en) | 2002-07-15 | 2007-11-27 | Stichting Energieonderzoek Centrum Nederland | Assembly of energy flow collectors, such as windpark, and method of operation |
US7357622B2 (en) | 2003-06-14 | 2008-04-15 | Stichting Energieonderzoek Centrum Nederland | Method and installation for extracting energy from a flowing fluid |
US20090099702A1 (en) | 2007-10-16 | 2009-04-16 | General Electric Company | System and method for optimizing wake interaction between wind turbines |
US7523001B2 (en) * | 2006-09-28 | 2009-04-21 | General Electric Company | Method and apparatus for operating wind turbine generators |
CA2697431A1 (en) | 2007-12-14 | 2009-06-25 | Shinji Arinaga | Wind power generation system and operation control method thereof |
US20090192868A1 (en) | 2008-01-24 | 2009-07-30 | Vrinda Rajiv | Method and System for Analyzing Performance of a Wind Farm |
US20090299780A1 (en) | 2008-05-29 | 2009-12-03 | Abhinanda Sarkar | Method and apparatus for determining and/or providing power output information of wind turbine farms |
US20090299697A1 (en) * | 2008-06-03 | 2009-12-03 | General Electric Company | System and method for trip event data acquisition and wind turbine incorporating same |
US20100115951A1 (en) | 2007-04-27 | 2010-05-13 | Lm Glasfiber A/S | Design of a group of wind power plants |
US7756609B2 (en) | 2007-08-02 | 2010-07-13 | Nordex Energy Gmbh | Wind park with a plurality of wind energy plants and method for the operation of the wind park |
CN101852172A (en) | 2010-03-09 | 2010-10-06 | 山东科技大学 | Method for calculating input wind speed of wind generating sets according to wake effect in wind power station |
US20100274400A1 (en) | 2009-04-22 | 2010-10-28 | Vestas Wind Systems A/S | Wind turbine configuration system |
US20100274401A1 (en) | 2007-12-20 | 2010-10-28 | Vestas Wind Systems A/S | Method for controlling a common output from at least two wind turbines, a central wind turbine control system, a wind park and a cluster of wind parks |
CN101949363A (en) | 2010-09-21 | 2011-01-19 | 山东科技大学 | Method for grouping wind generating sets by taking input wind speed and random fluctuation of wind direction of wind farm into consideration |
US7895016B2 (en) * | 2009-08-31 | 2011-02-22 | General Electric Company | System and method for wind turbine health management |
CN102012956A (en) | 2010-11-30 | 2011-04-13 | 山东科技大学 | Wind farm equivalent method based on wind farm input wind speed and wind direction chance fluctuation |
US7941304B2 (en) | 2009-04-30 | 2011-05-10 | General Electric Company | Method for enhancement of a wind plant layout with multiple wind turbines |
EP2326835A2 (en) | 2008-08-23 | 2011-06-01 | DeWind Co. | Method for controlling a wind farm |
US20110175353A1 (en) | 2010-01-20 | 2011-07-21 | Per Egedal | Wind farm power control based on matrix reflecting a power load distribution between individual wind turbines |
US20110176926A1 (en) | 2008-09-19 | 2011-07-21 | Cortenergy Bv | Wind turbine with low induction tips |
US20110193344A1 (en) | 2010-12-29 | 2011-08-11 | Vestas Wind Systems A/S | Control Network for Wind Turbine Park |
US8035241B2 (en) | 2010-07-09 | 2011-10-11 | General Electric Company | Wind turbine, control system, and method for optimizing wind turbine power production |
US8046191B2 (en) * | 2004-09-30 | 2011-10-25 | General Electric Company | Method for monitoring performance of a heat transfer device |
US8050899B2 (en) | 2008-05-30 | 2011-11-01 | General Electric Company | Method for wind turbine placement in a wind power plant |
CN102235313A (en) | 2011-06-30 | 2011-11-09 | 内蒙古电力勘测设计院 | Regular arrangement optimization method of fans in flat terrain |
CN102270256A (en) | 2011-06-30 | 2011-12-07 | 内蒙古电力勘测设计院 | Wind field planning method for flat region |
CN102289539A (en) | 2011-06-30 | 2011-12-21 | 内蒙古电力勘测设计院 | Method for optimizing fan layout for improving utilization rate of wind energy |
US20120025526A1 (en) * | 2010-07-30 | 2012-02-02 | General Electric Company | System and method for monitoring wind turbine gearbox health and performance |
US20120053983A1 (en) | 2011-08-03 | 2012-03-01 | Sameer Vittal | Risk management system for use with service agreements |
US8185331B2 (en) * | 2011-09-02 | 2012-05-22 | Onsemble LLC | Systems, methods and apparatus for indexing and predicting wind power output from virtual wind farms |
US20120133138A1 (en) | 2011-12-22 | 2012-05-31 | Vestas Wind Systems A/S | Plant power optimization |
US20120139244A1 (en) * | 2011-10-11 | 2012-06-07 | Laurent Bonnet | Method and system for control of wind turbines |
US20120185414A1 (en) * | 2010-12-15 | 2012-07-19 | Vaisala, Inc. | Systems and methods for wind forecasting and grid management |
US8249753B2 (en) | 2007-05-15 | 2012-08-21 | Siemens Aktiengesellschaft | Method for operating a wind farm comprising a plurality of wind turbines |
US8295987B2 (en) | 2010-03-31 | 2012-10-23 | General Electric Company | Systems and methods for performance monitoring and identifying upgrades for wind turbines |
CN102913399A (en) | 2012-11-06 | 2013-02-06 | 中国科学院工程热物理研究所 | Plasma flow control method for reducing the wake loss of a wind turbine |
WO2013026538A2 (en) | 2011-08-19 | 2013-02-28 | Repower Systems Se | Determining the energy yield loss of a wind turbine |
WO2013037374A1 (en) | 2011-09-13 | 2013-03-21 | Vestas Wind Systems A/S | A method for improving large array wind park power performance through active wake manipulation reducing shadow effects |
CN103020462A (en) | 2012-12-21 | 2013-04-03 | 华北电力大学 | Wind power plant probability output power calculation method considering complex wake effect model |
US20130103202A1 (en) | 2010-06-21 | 2013-04-25 | Robert Bowyer | Control of wind turbines in a wind park |
US20130144449A1 (en) | 2011-12-06 | 2013-06-06 | Søren Dalsgaard | Warning a wind turbine generator in a wind park of an extreme wind event |
US20130156577A1 (en) | 2011-12-15 | 2013-06-20 | Thomas Esbensen | Method of controlling a wind turbine |
US20130166082A1 (en) | 2011-12-23 | 2013-06-27 | General Electric Company | Methods and Systems for Optimizing Farm-level Metrics in a Wind Farm |
US20130184838A1 (en) | 2012-01-06 | 2013-07-18 | Michigan Aerospace Corporation | Resource optimization using environmental and condition-based monitoring |
US20130255363A1 (en) | 2012-03-29 | 2013-10-03 | Alstom Wind, S.L.U. | Detecting a Wake Situation in a Wind Farm |
WO2013152776A1 (en) | 2012-04-13 | 2013-10-17 | Kk-Electronic A/S | A configuration system for a wind turbine control system |
KR20130124028A (en) | 2012-05-04 | 2013-11-13 | 삼성중공업 주식회사 | System and method for output control of wind farm |
US20130300115A1 (en) | 2012-05-08 | 2013-11-14 | Johnson Controls Technology Company | Systems and methods for optimizing power generation in a wind farm turbine array |
US8606418B1 (en) | 2011-03-18 | 2013-12-10 | Rockwell Collins, Inc. | Wind prediction for wind farms through the use of weather radar |
US20140028496A1 (en) | 2012-07-27 | 2014-01-30 | Texas Tech University System | Apparatus and method for using radar to evaluate wind flow fields for wind energy applications |
US20140037447A1 (en) | 2012-08-06 | 2014-02-06 | Sid Ahmed ATTIA | Wind turbine yaw control |
US20140186176A1 (en) * | 2012-12-27 | 2014-07-03 | Jimmi Andersen | Method of detecting a degree of yaw error of a wind turbine |
US20140234103A1 (en) | 2013-02-19 | 2014-08-21 | John M. Obrecht | Method and system for improving wind farm power production efficiency |
US20140336833A1 (en) | 2012-01-25 | 2014-11-13 | Antonis Marinopoulos | Wind Park With Real Time Wind Speed Measurements |
US20140371936A1 (en) * | 2011-11-28 | 2014-12-18 | Expanergy, Llc | System and methods to aggregate instant and forecasted excess renewable energy |
US20150152846A1 (en) * | 2013-11-29 | 2015-06-04 | Alstom Renewable Technologies | Methods of operating a wind turbine, wind turbines and wind parks |
US20150160373A1 (en) * | 2013-12-07 | 2015-06-11 | Cardinal Wind, Inc. | Computer-implemented data analysis methods and systems for wind energy assessments |
US20150267686A1 (en) * | 2012-08-14 | 2015-09-24 | Vestas Wind Systems A/S | Partial-load de-rating for wind turbine control |
US20160084224A1 (en) | 2014-09-23 | 2016-03-24 | General Electric Company | System and method for optimizing wind farm performance |
US20160265513A1 (en) * | 2015-03-11 | 2016-09-15 | General Electric Company | Systems and methods for validating wind farm performance improvements |
US20170005515A1 (en) * | 2015-07-04 | 2017-01-05 | Dean Sanders | Renewable energy integrated storage and generation systems, apparatus, and methods with cloud distributed energy management services |
US9551322B2 (en) | 2014-04-29 | 2017-01-24 | General Electric Company | Systems and methods for optimizing operation of a wind farm |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2002048389A2 (en) * | 2000-12-15 | 2002-06-20 | The Genetics Company Inc. | Focussing of compound libraries using atomic electrotopological values |
CN102336069A (en) * | 2010-07-16 | 2012-02-01 | 鸿富锦精密工业(深圳)有限公司 | Printer with picture clipping function and picture clipping method |
JP5838311B2 (en) * | 2011-03-29 | 2016-01-06 | パナソニックIpマネジメント株式会社 | Endoscope device |
JP2015019467A (en) * | 2013-07-09 | 2015-01-29 | 船井電機株式会社 | Information terminal device |
-
2016
- 2016-05-11 US US15/151,573 patent/US10385829B2/en active Active
Patent Citations (72)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6776045B2 (en) | 1998-12-04 | 2004-08-17 | Cidra Corporation | Bragg grating pressure sensor for industrial sensing applications |
US6820489B2 (en) | 1998-12-04 | 2004-11-23 | Weatherford/Lamb, Inc. | Optical differential pressure sensor |
US6668656B2 (en) | 1998-12-04 | 2003-12-30 | Weatherford/Lamb, Inc. | Optical sensor having differing diameters |
US6724097B1 (en) | 1999-10-06 | 2004-04-20 | Aloys Wobben | Method for operating a wind farm |
US7025567B2 (en) | 2001-07-31 | 2006-04-11 | Aloys Wobben | Early-warning system for wind power installations |
US7299627B2 (en) | 2002-07-15 | 2007-11-27 | Stichting Energieonderzoek Centrum Nederland | Assembly of energy flow collectors, such as windpark, and method of operation |
US7357622B2 (en) | 2003-06-14 | 2008-04-15 | Stichting Energieonderzoek Centrum Nederland | Method and installation for extracting energy from a flowing fluid |
US20050090937A1 (en) * | 2003-10-22 | 2005-04-28 | Gary Moore | Wind turbine system control |
US8046191B2 (en) * | 2004-09-30 | 2011-10-25 | General Electric Company | Method for monitoring performance of a heat transfer device |
EP1817496A1 (en) | 2004-11-22 | 2007-08-15 | REpower Systems AG | Method for optimising the operation of wind farms |
US20070124025A1 (en) | 2005-11-29 | 2007-05-31 | General Electric Company | Windpark turbine control system and method for wind condition estimation and performance optimization |
US7523001B2 (en) * | 2006-09-28 | 2009-04-21 | General Electric Company | Method and apparatus for operating wind turbine generators |
US20100115951A1 (en) | 2007-04-27 | 2010-05-13 | Lm Glasfiber A/S | Design of a group of wind power plants |
US8249753B2 (en) | 2007-05-15 | 2012-08-21 | Siemens Aktiengesellschaft | Method for operating a wind farm comprising a plurality of wind turbines |
US7756609B2 (en) | 2007-08-02 | 2010-07-13 | Nordex Energy Gmbh | Wind park with a plurality of wind energy plants and method for the operation of the wind park |
US20090099702A1 (en) | 2007-10-16 | 2009-04-16 | General Electric Company | System and method for optimizing wake interaction between wind turbines |
CA2697431A1 (en) | 2007-12-14 | 2009-06-25 | Shinji Arinaga | Wind power generation system and operation control method thereof |
US20100274401A1 (en) | 2007-12-20 | 2010-10-28 | Vestas Wind Systems A/S | Method for controlling a common output from at least two wind turbines, a central wind turbine control system, a wind park and a cluster of wind parks |
US20090192868A1 (en) | 2008-01-24 | 2009-07-30 | Vrinda Rajiv | Method and System for Analyzing Performance of a Wind Farm |
US20090299780A1 (en) | 2008-05-29 | 2009-12-03 | Abhinanda Sarkar | Method and apparatus for determining and/or providing power output information of wind turbine farms |
US8050899B2 (en) | 2008-05-30 | 2011-11-01 | General Electric Company | Method for wind turbine placement in a wind power plant |
US20090299697A1 (en) * | 2008-06-03 | 2009-12-03 | General Electric Company | System and method for trip event data acquisition and wind turbine incorporating same |
EP2326835A2 (en) | 2008-08-23 | 2011-06-01 | DeWind Co. | Method for controlling a wind farm |
US20110176926A1 (en) | 2008-09-19 | 2011-07-21 | Cortenergy Bv | Wind turbine with low induction tips |
US20100274400A1 (en) | 2009-04-22 | 2010-10-28 | Vestas Wind Systems A/S | Wind turbine configuration system |
US7941304B2 (en) | 2009-04-30 | 2011-05-10 | General Electric Company | Method for enhancement of a wind plant layout with multiple wind turbines |
US7895016B2 (en) * | 2009-08-31 | 2011-02-22 | General Electric Company | System and method for wind turbine health management |
US20110175353A1 (en) | 2010-01-20 | 2011-07-21 | Per Egedal | Wind farm power control based on matrix reflecting a power load distribution between individual wind turbines |
CN101852172A (en) | 2010-03-09 | 2010-10-06 | 山东科技大学 | Method for calculating input wind speed of wind generating sets according to wake effect in wind power station |
US8295987B2 (en) | 2010-03-31 | 2012-10-23 | General Electric Company | Systems and methods for performance monitoring and identifying upgrades for wind turbines |
US20130103202A1 (en) | 2010-06-21 | 2013-04-25 | Robert Bowyer | Control of wind turbines in a wind park |
US8035241B2 (en) | 2010-07-09 | 2011-10-11 | General Electric Company | Wind turbine, control system, and method for optimizing wind turbine power production |
US20120025526A1 (en) * | 2010-07-30 | 2012-02-02 | General Electric Company | System and method for monitoring wind turbine gearbox health and performance |
CN101949363A (en) | 2010-09-21 | 2011-01-19 | 山东科技大学 | Method for grouping wind generating sets by taking input wind speed and random fluctuation of wind direction of wind farm into consideration |
CN102012956A (en) | 2010-11-30 | 2011-04-13 | 山东科技大学 | Wind farm equivalent method based on wind farm input wind speed and wind direction chance fluctuation |
US20120185414A1 (en) * | 2010-12-15 | 2012-07-19 | Vaisala, Inc. | Systems and methods for wind forecasting and grid management |
US20110193344A1 (en) | 2010-12-29 | 2011-08-11 | Vestas Wind Systems A/S | Control Network for Wind Turbine Park |
US8606418B1 (en) | 2011-03-18 | 2013-12-10 | Rockwell Collins, Inc. | Wind prediction for wind farms through the use of weather radar |
CN102289539A (en) | 2011-06-30 | 2011-12-21 | 内蒙古电力勘测设计院 | Method for optimizing fan layout for improving utilization rate of wind energy |
CN102270256A (en) | 2011-06-30 | 2011-12-07 | 内蒙古电力勘测设计院 | Wind field planning method for flat region |
CN102235313A (en) | 2011-06-30 | 2011-11-09 | 内蒙古电力勘测设计院 | Regular arrangement optimization method of fans in flat terrain |
US20120053983A1 (en) | 2011-08-03 | 2012-03-01 | Sameer Vittal | Risk management system for use with service agreements |
WO2013026538A2 (en) | 2011-08-19 | 2013-02-28 | Repower Systems Se | Determining the energy yield loss of a wind turbine |
US8185331B2 (en) * | 2011-09-02 | 2012-05-22 | Onsemble LLC | Systems, methods and apparatus for indexing and predicting wind power output from virtual wind farms |
WO2013037374A1 (en) | 2011-09-13 | 2013-03-21 | Vestas Wind Systems A/S | A method for improving large array wind park power performance through active wake manipulation reducing shadow effects |
US20120139244A1 (en) * | 2011-10-11 | 2012-06-07 | Laurent Bonnet | Method and system for control of wind turbines |
US20140371936A1 (en) * | 2011-11-28 | 2014-12-18 | Expanergy, Llc | System and methods to aggregate instant and forecasted excess renewable energy |
US20130144449A1 (en) | 2011-12-06 | 2013-06-06 | Søren Dalsgaard | Warning a wind turbine generator in a wind park of an extreme wind event |
US20130156577A1 (en) | 2011-12-15 | 2013-06-20 | Thomas Esbensen | Method of controlling a wind turbine |
US20120133138A1 (en) | 2011-12-22 | 2012-05-31 | Vestas Wind Systems A/S | Plant power optimization |
US20130166082A1 (en) | 2011-12-23 | 2013-06-27 | General Electric Company | Methods and Systems for Optimizing Farm-level Metrics in a Wind Farm |
US20130184838A1 (en) | 2012-01-06 | 2013-07-18 | Michigan Aerospace Corporation | Resource optimization using environmental and condition-based monitoring |
US20140336833A1 (en) | 2012-01-25 | 2014-11-13 | Antonis Marinopoulos | Wind Park With Real Time Wind Speed Measurements |
US9086337B2 (en) | 2012-03-29 | 2015-07-21 | Alstom Renovables Espana, S.L. | Detecting a wake situation in a wind farm |
US20130255363A1 (en) | 2012-03-29 | 2013-10-03 | Alstom Wind, S.L.U. | Detecting a Wake Situation in a Wind Farm |
WO2013152776A1 (en) | 2012-04-13 | 2013-10-17 | Kk-Electronic A/S | A configuration system for a wind turbine control system |
KR20130124028A (en) | 2012-05-04 | 2013-11-13 | 삼성중공업 주식회사 | System and method for output control of wind farm |
US20130300115A1 (en) | 2012-05-08 | 2013-11-14 | Johnson Controls Technology Company | Systems and methods for optimizing power generation in a wind farm turbine array |
US20140028496A1 (en) | 2012-07-27 | 2014-01-30 | Texas Tech University System | Apparatus and method for using radar to evaluate wind flow fields for wind energy applications |
US20140028495A1 (en) | 2012-07-27 | 2014-01-30 | Texas Tech University System | System and Method for Evaluating Wind Flow Fields Using Remote Sensing Devices |
US20140037447A1 (en) | 2012-08-06 | 2014-02-06 | Sid Ahmed ATTIA | Wind turbine yaw control |
US20150267686A1 (en) * | 2012-08-14 | 2015-09-24 | Vestas Wind Systems A/S | Partial-load de-rating for wind turbine control |
CN102913399A (en) | 2012-11-06 | 2013-02-06 | 中国科学院工程热物理研究所 | Plasma flow control method for reducing the wake loss of a wind turbine |
CN103020462A (en) | 2012-12-21 | 2013-04-03 | 华北电力大学 | Wind power plant probability output power calculation method considering complex wake effect model |
US20140186176A1 (en) * | 2012-12-27 | 2014-07-03 | Jimmi Andersen | Method of detecting a degree of yaw error of a wind turbine |
US20140234103A1 (en) | 2013-02-19 | 2014-08-21 | John M. Obrecht | Method and system for improving wind farm power production efficiency |
US20150152846A1 (en) * | 2013-11-29 | 2015-06-04 | Alstom Renewable Technologies | Methods of operating a wind turbine, wind turbines and wind parks |
US20150160373A1 (en) * | 2013-12-07 | 2015-06-11 | Cardinal Wind, Inc. | Computer-implemented data analysis methods and systems for wind energy assessments |
US9551322B2 (en) | 2014-04-29 | 2017-01-24 | General Electric Company | Systems and methods for optimizing operation of a wind farm |
US20160084224A1 (en) | 2014-09-23 | 2016-03-24 | General Electric Company | System and method for optimizing wind farm performance |
US20160265513A1 (en) * | 2015-03-11 | 2016-09-15 | General Electric Company | Systems and methods for validating wind farm performance improvements |
US20170005515A1 (en) * | 2015-07-04 | 2017-01-05 | Dean Sanders | Renewable energy integrated storage and generation systems, apparatus, and methods with cloud distributed energy management services |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10982653B2 (en) * | 2016-06-07 | 2021-04-20 | Vestas Wind Systems A/S | Adaptive control of a wind turbine by detecting a change in performance |
US20210231103A1 (en) * | 2018-06-08 | 2021-07-29 | Siemens Gamesa Renewable Energy A/S | Controlling wind turbines in presence of wake interactions |
Also Published As
Publication number | Publication date |
---|---|
US20170328348A1 (en) | 2017-11-16 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US10385829B2 (en) | System and method for validating optimization of a wind farm | |
EP3263889B1 (en) | System and method for assessing farm-level performance of a wind farm | |
US10371124B2 (en) | System and method for determining wind farm wake loss | |
AU2017269206B2 (en) | System and method for forecasting power output of a wind farm | |
US9797377B2 (en) | System and method for controlling a wind farm | |
US10815972B2 (en) | System and method for assessing and validating wind turbine and wind farm performance | |
US9822762B2 (en) | System and method for operating a wind turbine | |
US9644612B2 (en) | Systems and methods for validating wind farm performance measurements | |
US10247171B2 (en) | System and method for coordinating wake and noise control systems of a wind farm | |
US10393093B2 (en) | System and method for assessing the performance impact of wind turbine upgrades | |
US10487804B2 (en) | Systems and methods for validating wind farm performance improvements | |
EP3249218B1 (en) | System and method for micrositing a wind farm for loads optimization | |
EP3406897B1 (en) | System and method for determining wind farm wake loss | |
US20190226456A1 (en) | System and Method for Evaluating Loads of a Potential Wind Farm Site for Multiple Wind Scenarios | |
EP3642480B1 (en) | System and method for coordinating wake and noise control systems of a wind farm |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: GENERAL ELECTRIC COMPANY, NEW YORK Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:WILSON, MEGAN;KERN, STEFAN;CHANDRASHEKAR, SIDDHANTH;AND OTHERS;SIGNING DATES FROM 20160502 TO 20160510;REEL/FRAME:038543/0812 |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: RESPONSE AFTER FINAL ACTION FORWARDED TO EXAMINER |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: NOTICE OF ALLOWANCE MAILED -- APPLICATION RECEIVED IN OFFICE OF PUBLICATIONS |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: PUBLICATIONS -- ISSUE FEE PAYMENT VERIFIED |
|
STCF | Information on status: patent grant |
Free format text: PATENTED CASE |
|
MAFP | Maintenance fee payment |
Free format text: PAYMENT OF MAINTENANCE FEE, 4TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1551); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY Year of fee payment: 4 |
|
AS | Assignment |
Owner name: GE INFRASTRUCTURE TECHNOLOGY LLC, SOUTH CAROLINA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:GENERAL ELECTRIC COMPANY;REEL/FRAME:065727/0001 Effective date: 20231110 |