System and method for designing and operating a wind turbine power system using statistical analysis

Abstract

A method for operating a wind turbine power system connected to an electrical grid includes collecting data related to one or more parameters of one or more electrical components of the wind turbine power system. The method may also include performing a statistical analysis on the data related to the one or more parameters of the one or more electrical components. Furthermore, the method includes predicting future behavior of the electrical components based on the statistical analysis. Furthermore, the method includes using the predicted future behavior to determine set points for the electrical components. Furthermore, the method includes operating the wind turbine power system at the determined set points for the electrical components to optimize at least one characteristic of the electrical components.

Description

Technical Field

[0001] The present disclosure relates generally to wind turbines, and more particularly to systems and methods for designing and / or operating wind turbine components based on statistical analysis of operational and / or grid data to reduce unnecessary design margins. Background Art

[0002] Wind power is considered to be one of the cleanest and most environmentally friendly energy sources currently available, and wind turbines have gained increasing attention in this regard. A modern wind turbine typically includes a tower, a generator, a gearbox, a nacelle and one or more rotor blades. The rotor blades capture the kinetic energy of the wind using the known airfoil principle. For example, the rotor blades typically have an airfoil cross-sectional profile so that during operation, air flows over the blades to create a pressure difference between the sides. As a result, lift directed from the pressure side toward the suction side acts on the blades. The lift creates a torque on the main rotor shaft, which is engaged to a generator to generate electricity. In addition, multiple wind turbines can be arranged at a predetermined geographical location and electrically connected together to form a wind farm.

[0003] During operation, wind strikes the rotor blades of a wind turbine, and the blades convert the wind energy into a mechanical torque that rotationally drives a low-speed shaft. The low-speed shaft is configured to drive a gearbox, which then increases the low rotational speed of the low-speed shaft to drive a high-speed shaft at an increased rotational speed. Thus, a typical wind turbine also includes various electrical components for converting mechanical energy into electrical power. For example, the high-speed shaft is generally rotatably coupled to a generator so as to rotatably drive the generator rotor. Thus, a rotating magnetic field can be induced by the generator rotor, and a voltage can be induced within the generator stator, which is magnetically coupled to the generator rotor. In certain configurations, the associated electrical power can be transmitted to a turbine transformer, which is typically connected to the power grid via a grid circuit breaker. Thus, the turbine transformer increases the voltage amplitude of the electrical power so that the transformed electrical power can be further transmitted to the power grid.

[0004] In many wind turbines, the generator rotor may also be electrically coupled to a bidirectional power converter, which includes a rotor-side converter connected to a line-side converter via a regulated DC link. More specifically, some wind turbines, such as wind-driven doubly-fed induction generator (DFIG) systems or full power conversion systems, may include a power converter having an AC-DC-AC topology.

[0005] Typical wind turbine components, particularly electrical components, are designed for worst-case scenario conditions (e.g., a combination of the hottest day of the year, highest current, highest voltage, etc.). Such a combination of operating conditions may never occur during the lifecycle of a wind turbine. Consequently, conventional wind turbines are overdesigned and include large operating margins, which add significant cost to the system.

[0006] Therefore, systems and methods for designing and / or operating electrical components of a wind turbine to more accurately reflect actual operating conditions would be welcome in the art. Thus, the present disclosure relates to a system and method for designing and / or operating electrical components of a wind turbine based on the probability of a condition being met, so as to reduce unnecessary design margins. Summary of the Invention

[0007] 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.

[0008] In one aspect, the present disclosure relates to a method for operating a wind turbine power system connected to an electrical grid. The wind turbine power system includes one or more electrical components. The method includes collecting data related to one or more parameters of the one or more electrical components. The method may also include performing statistical analysis on the data related to the one or more parameters of the one or more electrical components. Furthermore, the method includes predicting future behavior of the electrical components based on the statistical analysis. Furthermore, the method includes using the predicted future behavior to determine set points for the electrical components. Furthermore, the method includes operating the wind turbine power system at the determined set points for the electrical components to optimize at least one characteristic of the electrical components.

[0009] For example, in an embodiment, the one or more electrical components may include at least one of a generator, a power converter, a transformer, a switchgear, a cable or wire, a pitch system, or a yaw system.

[0010] In another embodiment, the data may include, for example, forecast or prediction data, simulation data, measured operational data, calculated data, turbine fleet data, and / or historical data. Furthermore, the data may be stored in at least one database. Furthermore, the parameters may include, for example, voltage, current, temperature, time, and / or combinations or functions thereof.

[0011] In further embodiments, the data may be stored in a plurality of databases. Thus, in such embodiments, collecting data related to the parameters of the electrical component may include collecting data related to the parameters of the electrical component from a plurality of databases.

[0012] In some embodiments, the statistical analysis may include individual probabilities, combined probabilities, means, outliers, standard deviations, and / or combinations thereof, as well as any other suitable statistical analysis. For example, in some embodiments, the individual probabilities or combined probabilities may include, for example, the probability that an electrical component operates within a certain range, the probability that one or more events occur in the electrical component, or the probability that one or more conditions occur in the electrical component.

[0013] In certain embodiments, the wind turbine power system may be part of a wind farm that includes a plurality of wind turbine power systems. In such embodiments, the method may further include maximizing the capability of each of the plurality of wind turbine power systems in the wind farm, via a farm-level controller that differentially distributes active power and / or reactive power demands across the plurality of wind turbine power systems, depending on individual probabilities or combined probabilities associated with particular ones of the plurality of wind turbine power systems, to optimize power output of the wind farm.

[0014] In another embodiment, applying the statistical analysis to data related to a parameter of the electrical component may include determining a first probability that the electrical component operates within a first range or that a first event occurs in the electrical component, determining a second probability that the electrical component operates within a second range or that a second event occurs in the electrical component, and multiplying the first and second probabilities of the first and second events together to obtain a combined probability.

[0015] In yet further embodiments, the characteristic of the electrical component may include at least one of power output, reliability, maintenance, capacity, or failure rate. In additional embodiments, the method may further include optimizing the characteristic of the electrical component by allowing increases or decreases in the characteristic of the electrical component based on statistical analysis.

[0016] In an embodiment, the method may include: using the data to estimate a probability that the electrical component operates throughout the PQ capability curve; and using the probability that the electrical component operates throughout the PQ capability curve to determine a setpoint for the electrical component.

[0017] In such embodiments, the method may further include optimizing the power output of the wind turbine power system using the probability of the electrical components operating throughout the PQ capability curve.

[0018] In yet another embodiment, the method may include deriving regional data from the collected data related to parameters of the electrical component, and generating a PQ capability curve for one or more specific regions based on the regional data.

[0019] In still further embodiments, the method may include customizing the expansion of the PQ capability curve by coupling additional power generation units to the wind turbine power system or a hybrid power generation system including the wind turbine power system.

[0020] In certain embodiments, the method may include acquiring data related to derating of the wind turbine power system due solely to increased VAR demand from the grid; and converting the data into revenue reduction for the wind turbine power system due to the VAR-related derating.

[0021] In another aspect, the present disclosure relates to a system for optimizing the design of one or more electrical components of a wind turbine power system. The system includes a controller having at least one processor configured to perform a plurality of operations. For example, the plurality of operations may include collecting data related to one or more parameters of the electrical components, performing a statistical analysis on the data related to the parameters of the electrical components, the statistical analysis including at least a combination probability, predicting future behavior of the electrical components based on the statistical analysis, using the predicted future behavior to determine the size of the electrical components to minimize a design margin of the electrical components, and sizing the wind turbine power system to optimize at least one characteristic of the electrical components. It should also be understood that the method may also include any of the additional steps and / or features described herein.

[0022] Technical Solution 1. A method for operating a wind turbine power system connected to a power grid, the wind turbine power system having one or more electrical components, the method comprising:

[0023] collecting data related to one or more parameters of the one or more electrical components;

[0024] performing statistical analysis on the data related to one or more parameters of the one or more electrical components;

[0025] predicting future behavior of the one or more electrical components based on the statistical analysis;

[0026] using the predicted future behavior to determine a set point for the one or more electrical components; and

[0027] The wind turbine power system is operated at the determined set points with respect to the one or more electrical components to optimize at least one characteristic of the one or more electrical components.

[0028] Technical Solution 2. The method according to Technical Solution 1, wherein the one or more electrical components include at least one of a generator, a power converter, a transformer, a switch device, a cable or wire, a pitch system, or a yaw system.

[0029] Technical Solution 3. A method according to Technical Solution 1, wherein the data includes at least one of predicted or forecast data, simulation data, measured operating data, calculated data, turbine fleet data or historical data, the data is stored in at least one database, and one or more parameters include at least one of voltage, current, temperature, time and / or a combination or function thereof.

[0030] Technical Solution 4. A method according to Technical Solution 1, wherein the data is stored in multiple databases, and wherein collecting the data related to one or more parameters of the one or more electrical components further includes collecting the data related to one or more parameters of the one or more electrical components from the multiple databases.

[0031] Technical Solution 5. The method according to Technical Solution 1, wherein the statistical analysis includes at least one of the following: individual probability, combined probability, mean, outlier, standard deviation or a combination thereof.

[0032] Technical Solution 6. A method according to Technical Solution 5, wherein the individual probability or the combined probability includes at least one of the following: the probability that the one or more electrical components operate within a certain range, the probability that one or more certain events occur in the one or more electrical components, or the probability that one or more certain conditions occur in the one or more electrical components.

[0033] Technical Solution 7. The method according to Technical Solution 5, wherein the wind turbine power system is part of a wind farm including a plurality of wind turbine power systems, the method further comprising:

[0034] The invention also provides for maximizing the capacity of each of the plurality of wind turbine power systems in the wind park, via a park-level controller that differentially distributes active power and / or reactive power demands across the plurality of wind turbine power systems, depending on individual probabilities or combined probabilities with respect to particular ones of the plurality of wind turbine power systems, so as to optimize power output of the wind park.

[0035] Technical Solution 8. The method according to Technical Solution 5, wherein applying the statistical analysis to the data related to one or more parameters of the one or more electrical components further comprises:

[0036] determining a first probability that the one or more electrical components are operating within a first range or that a first event occurs in the one or more electrical components;

[0037] determining a second probability that the one or more electrical components are operating within a second range or that a second event occurs in the one or more electrical components; and

[0038] The first and second probabilities of the first and second events are multiplied together to obtain the combined probability.

[0039] Technical Solution 9. The method according to Technical Solution 1, wherein the at least one characteristic of the one or more electrical components includes at least one of power output, reliability, maintenance, capacity, or failure rate.

[0040] Technical Solution 10. The method according to Technical Solution 9, wherein the method further comprises optimizing at least one characteristic of the one or more electrical components by allowing an increase or decrease in the at least one characteristic of the one or more electrical components based on the statistical analysis.

[0041] Technical Solution 11. The method according to Technical Solution 1, wherein the method further comprises:

[0042] using the data to estimate a probability that the one or more electrical components will operate within a full PQ capability curve;

[0043] A set point for the one or more electrical components is determined using a probability of the one or more electrical components operating throughout the PQ capability curve.

[0044] Technical Solution 12. The method according to Technical Solution 11, wherein the method further comprises optimizing the power output of the wind turbine power system using a probability of the one or more electrical components operating within the entire PQ capability curve.

[0045] Technical Solution 13. The method according to Technical Solution 11, wherein the method further comprises:

[0046] deriving regional data from collected data related to one or more parameters of the one or more electrical components; and

[0047] A PQ capability curve for one or more specific regions is generated based on the regional data.

[0048] Technical Solution 14. The method according to Technical Solution 11, wherein the method further comprises customizing the extension of the PQ capability curve by coupling an additional power generation unit to at least one of the wind turbine power system or a hybrid power generation system including the wind turbine power system.

[0049] Technical Solution 15. The method according to Technical Solution 1, wherein the method further comprises:

[0050] obtaining data related to a derating of the wind turbine power system due solely to an increase in VAR demand from the grid; and

[0051] The data is converted into a revenue reduction for the wind turbine power system due to VAR-related derating.

[0052] Technical Solution 16. A system for optimizing the design of one or more electrical components of a wind turbine power system, the system comprising:

[0053] A controller comprising at least one processor configured to perform a plurality of operations, the plurality of operations comprising:

[0054] collecting data related to one or more parameters of the one or more electrical components;

[0055] performing a statistical analysis on the data related to one or more parameters of the one or more electrical components, the statistical analysis including at least a probability of combination;

[0056] predicting future behavior of the one or more electrical components based on the statistical analysis;

[0057] using the predicted future behavior to size the one or more electrical components so as to minimize a design margin for the one or more electrical components; and

[0058] The wind turbine power system is sized to optimize at least one characteristic of the one or more electrical components.

[0059] Technical Solution 17. The system according to Technical Solution 16, wherein the one or more electrical components include at least one of a generator, a power converter, a transformer, a switch device, a cable or wire, a pitch system, or a yaw system.

[0060] Technical Solution 18. The system according to Technical Solution 16, wherein at least one characteristic of the one or more electrical components includes at least one of power output, reliability, maintenance, capacity, or failure rate.

[0061] Technical Solution 19. A system according to Technical Solution 16, wherein the system further includes at least one database for storing the data, the data including at least one of predicted or forecast data, simulation data, measured operating data, calculated data, turbine fleet data or historical data, and the one or more parameters including at least one of voltage, current, temperature, time and / or a combination or function thereof.

[0062] Technical Solution 20. A system according to Technical Solution 17, wherein the system further comprises an additional power generation unit, wherein the additional power generation unit is connected to the wind turbine power system or a hybrid power generation system including the wind turbine power system for customizing an extension of a PQ capability curve representing the operation of the one or more electrical components.

[0063] These and other features, aspects and advantages of the present invention will become better understood with reference to the following description and appended claims.The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present invention and, together with the description, serve to explain the principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] A complete and enabling disclosure of the invention, including the best mode thereof, to one of ordinary skill in the art is set forth in the specification with reference to the accompanying drawings, in which:

[0065] Figure 1 A perspective view illustrating one embodiment of a wind turbine according to the present disclosure;

[0066] Figure 2 shows a simplified interior view of one embodiment of a nacelle according to the present disclosure;

[0067] Figure 3 Shown to be suitable for Figure 1 A schematic diagram of one embodiment of a wind turbine electrical power system for use with the wind turbine shown in FIG.

[0068] Figure 4 shows a wind farm having a plurality of wind turbines according to the present disclosure;

[0069] Figure 5 A block diagram illustrating one embodiment of a controller according to the present disclosure; and

[0070] Figure 6 A flow chart illustrating one embodiment of a method for operating a wind turbine according to the present disclosure is shown. DETAILED DESCRIPTION

[0071] Reference will now be made in detail to embodiments of the present invention, one or more examples of which are illustrated in the drawings. Each example is provided as an explanation of the present invention and is not a limitation of the present invention. In fact, it will be apparent to those skilled in the art that various modifications and variations may be made in the present invention without departing from the scope or spirit of the present invention. For example, a feature shown or described as part of an embodiment may be used with another embodiment to produce yet further embodiments. Therefore, it is intended that the present invention covers such modifications and variations as fall within the scope of the appended claims and their equivalents.

[0072] Now refer to the figure, Figure 1A perspective view of one embodiment of a wind turbine 10 according to the present disclosure is shown. 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 the hub 20 and extending outwardly from the hub 20. For example, in the illustrated embodiment, the rotor 18 includes three rotor blades 22. However, in alternative embodiments, 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 from the wind to be converted into usable mechanical energy and subsequently into electrical energy. For example, the hub 20 may be rotatably coupled to a generator 24 ( Figure 2 ), to allow the generation of electrical energy.

[0073] 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 external to the wind turbine 10. Furthermore, the controller 26 may be communicatively coupled to any number of components of the wind turbine 10 in order to control the operation of such components and / or implement corrective or control actions. Thus, 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 functions, such as receiving, transmitting, and / or executing wind turbine control signals. Thus, the controller 26 may generally be configured to control various operating modes (e.g., startup or shutdown sequences), de-rating or up-rating of the wind turbine, and / or individual components of the wind turbine 10.

[0074] Now refer to Figure 2 , showing Figure 1 , a simplified interior view of one embodiment of a nacelle 16 of a wind turbine 10 is shown in FIG. As shown, a generator 24 may be disposed within the nacelle 16 and supported atop a base plate 46. Generally, the generator 24 may be coupled to the rotor 18 for generating electrical power from the rotational energy generated by the rotor 18. For example, as shown in the illustrated embodiment, the rotor 18 may include a rotor shaft 34 coupled to the hub 20 for rotation therewith. The rotor shaft 34 may, in turn, be rotatably coupled to a generator shaft 36 of the generator 24 via a gearbox 38. As generally understood, the rotor shaft 34 may provide a low-speed, high-torque input to the gearbox 38 in response to rotation of the rotor blades 22 and the hub 20. The gearbox 38 may then be configured to convert the low-speed, high-torque input into a high-speed, low-torque output to drive the generator shaft 36 and, therefore, the generator 24.

[0075] Wind turbine 10 may also be communicatively coupled to one or more pitch drive mechanisms 32 of wind turbine controller 26, wherein each pitch adjustment mechanism 32 is configured to rotate a pitch bearing 40, and thus, each rotor blade 22, about its respective pitch axis 28. Additionally, as shown, wind turbine 10 may include one or more yaw drive mechanisms 42 configured to change the angle of nacelle 16 relative to the wind (e.g., by engaging a yaw bearing 44 of wind turbine 10 disposed between tower 12 and nacelle 16 of wind turbine 10).

[0076] In addition, the wind turbine 10 may also include one or more sensors 66, 68 for monitoring various wind conditions of the wind turbine 10. For example, the incoming wind direction 52, wind speed, or any other suitable wind condition near the wind turbine 10 may be measured, such as by using a suitable weather sensor 66. Suitable weather sensors may include, for example, a light detection and ranging ("LIDAR") device, a sound detection and ranging ("SODAR") device, an anemometer, a wind vane, a barometer, a radar device (such as a Doppler radar device), or any other sensing device now known in the art or later developed that can provide wind direction information. Still other sensors 68 may be used to measure additional operating parameters of the wind turbine 10, such as voltage, current, vibration, etc., as described herein.

[0077] Now refer to Figure 3 , a schematic diagram illustrating one embodiment of a wind turbine power system 100 according to aspects of the present disclosure. Although generally reference will be made herein to Figure 3 While the present disclosure is described with reference to the system 100 shown in FIG. 1 , using the disclosure provided herein, one of ordinary skill in the art will appreciate that aspects of the present disclosure may also be applicable to other power generation systems, and as mentioned above, the present invention is not limited to wind turbine systems.

[0078] exist Figure 3 In the embodiment and as mentioned, the wind turbine 10 ( Figure 1) can optionally be coupled to a gearbox 38, which in turn is coupled to a generator 102, which is a doubly fed induction generator (DFIG). As shown, the DFIG 102 can be coupled to a stator bus 104. Furthermore, as shown, a power converter 106 can be coupled to the DFIG 102 via a rotor bus 108 and to the stator bus 104 via a line-side bus 110. Thus, the stator bus 104 can provide output multiphase power (e.g., three-phase power) from the stator of the DFIG 102, while the rotor bus 108 can provide output multiphase power (e.g., three-phase power) from the rotor of the DFIG 102. The power converter 106 can also include a rotor-side converter (RSC) 112 and a line-side converter (LSC) 114. The DFIG 102 is coupled to the rotor-side converter 112 via the rotor bus 108. Additionally, the RSC 112 is coupled to the LSC 114 via a DC link 116, with a DC link capacitor 118 spanning the DC link. The LSC 114 is in turn coupled to the line-side bus 110 .

[0079] The RSC 112 and the LSC 114 may be configured to use one or more switching devices (such as insulated gate bipolar transistor (IGBT) switching elements) in a three-phase pulse width modulation (PWM) arrangement for a normal operating mode. Additionally, the power converter 106 may be coupled to a converter controller 120 to control the operation of the rotor-side converter 112 and / or the line-side converter 114 as described herein. It should be noted that the converter controller 120 may be configured as an interface between the power converter 106 and the turbine controller 26 and may include any number of control devices, such as Figure 5 Those described in .

[0080] In a typical configuration, various line contactors and circuit breakers may also be included, including, for example, a grid circuit breaker 122, to isolate various components required for normal operation of the DFIG 102 during connection to or disconnection from a load, such as a grid 124. For example, a system circuit breaker 126 may couple a system bus 128 to a transformer 130, which may be coupled to the grid 124 via the grid circuit breaker 122. In alternative embodiments, fuses may replace some or all of the circuit breakers.

[0081] In operation, alternating current (AC) generated at the DFIG 102 by the rotating rotor 18 is provided to the grid 124 via a dual path defined by the stator bus 104 and the rotor bus 108. On the rotor bus side 108, sinusoidal multiphase (e.g., three-phase) alternating current (AC) is provided to the power converter 106. The rotor-side power converter 112 converts the AC power provided from the rotor bus 108 into direct current (DC) power and provides the DC power to the DC link 116. As generally understood, the switching elements (e.g., IGBTs) used in the bridge circuit of the rotor-side power converter 112 can be modulated to convert the AC power provided from the rotor bus 108 into DC power suitable for the DC link 116.

[0082] Additionally, line-side converter 114 converts the DC power on DC link 116 into AC output power suitable for grid 124. Specifically, switching elements (e.g., IGBTs) in a bridge circuit for line-side power converter 114 may be modulated to convert the DC power on DC link 116 into AC power on line-side bus 110. The AC power from power converter 106 may be combined with power from the stator of DFIG 102 to provide multi-phase power (e.g., three-phase power) having a frequency that is substantially maintained at the frequency of grid 124 (e.g., 50 Hz or 60 Hz).

[0083] In addition, various circuit breakers and switches, such as a grid breaker 122, a system breaker 126, a stator synchronizing switch 132, a converter breaker 134, and a line contactor 136, may be included in wind turbine power system 100 to connect or disconnect corresponding buses, for example, when current is excessive and may damage components of wind turbine power system 100 or for other operational considerations. Additional protective components may also be included in wind turbine power system 100.

[0084] Furthermore, the power converter 106 may receive control signals from, for example, the local control system 176 via the converter controller 120. The control signals may be based, among other things, on sensed conditions or operating characteristics of the wind turbine power system 100. Typically, the control signals provide control over the operation of the power converter 106. For example, feedback in the form of sensed speed of the DFIG 102 may be used to control the conversion of output power from the rotor bus 108 to maintain a suitable and balanced multi-phase (e.g., three-phase) power supply. Other feedback from other sensors may also be used by the controller 120, 26 to control the power converter 106, including, for example, stator and rotor bus voltage and current feedback. Using various forms of feedback information, switching control signals (e.g., gate timing commands for the IGBTs), stator synchronization control signals, and circuit breaker signals may be generated.

[0085] Power converter 106 also compensates or adjusts the frequency of the three-phase power from the rotor for variations in, for example, wind speed at hub 20 and rotor blades 22. Thus, mechanical and electrical rotor frequencies are decoupled, and electrical stator and rotor frequency matching is facilitated substantially independently of mechanical rotor speed.

[0086] Under certain conditions, the bidirectional nature of power converter 106, and in particular LSC 114 and RSC 112, facilitates feeding at least some of the generated electrical power back into the generator rotor. More specifically, electrical power can be transmitted from stator bus 104 to line-side bus 110, and then through line contactor 136 and into power converter 106, in particular LSC 114, which acts as a rectifier and converts the sinusoidal three-phase AC power into DC power. The DC power is transmitted to DC link 116. Capacitor 118 facilitates mitigating DC link voltage amplitude variations by mitigating DC ripple that is sometimes associated with three-phase AC rectification.

[0087] The DC power is then transmitted to RSC 112, which converts the DC power into three-phase sinusoidal AC power by regulating voltage, current, and frequency. This conversion is monitored and controlled via converter controller 120. The converted AC power is transmitted from RSC 112 to the generator rotor via rotor bus 108. In this way, generator reactive power control is facilitated by controlling rotor current and voltage.

[0088] Now refer to Figure 4 , the wind turbine power system 100 described herein may be part of a wind farm 50. As shown, the wind farm 50 may include a plurality of wind turbines 52 (including the wind turbine 10 described above), and an overall farm-level controller 56. For example, as shown in the illustrated embodiment, the wind farm 50 includes twelve wind turbines, including the wind turbine 10. However, in other embodiments, the wind farm 50 may include any other number of wind turbines, such as fewer than twelve wind turbines or more than twelve wind turbines. In one embodiment, the turbine controllers of the plurality of wind turbines 52 are communicatively coupled to the farm-level controller 56, for example, via a wired connection, such as by connecting the turbine controller 26 via a suitable communication link 57 (e.g., a suitable cable). Alternatively, the turbine controllers may be communicatively coupled to the farm-level controller 56 via a wireless connection, such as by using any suitable wireless communication protocol known in the art. In further embodiments, the farm level controller 56 is configured to send and receive control signals to and from the various wind turbines 52 , such as, for example, to distribute active and / or reactive power demands across the wind turbines 52 of the wind farm 50 .

[0089] Still refer to Figure 4, the wind farm 50 may be a hybrid system having one or more additional active or passive power generation units 55, such as, for example, solar power generation devices, energy storage systems (such as batteries), reactive power (VAR) generation units or groups (at either or both turbine or farm levels), or the like. For example, Figure 3 As shown in FIG, wind turbine power system 100 includes reactive power generation unit 138. Thus, it should be understood that such additional power generation devices may be used at either or both the turbine or farm levels.

[0090] Now refer to Figure 5 , a block diagram illustrating one embodiment of suitable components according to exemplary aspects of the present disclosure that may be included within a controller, such as any of converter controller 120, turbine controller 26, and / or farm-level controller 56. As shown, the controller may include one or more processors 58, computers, or other suitable processing units, and associated memory devices 60, which may include suitable computer-readable instructions that, when implemented, configure the controller to perform various functions, such as receiving, sending, and / or executing wind turbine control signals (e.g., performing the methods, steps, calculations, etc. disclosed herein).

[0091] As used herein, the term "processor" refers not only to integrated circuits that are considered in the art to be included in a computer, but also to controllers, microcontrollers, microcomputers, programmable logic controllers (PLCs), application-specific integrated circuits, and other programmable circuits. Additionally, the memory device 60 may generally include memory elements including, but not limited to, computer-readable media (e.g., random access memory (RAM)), computer-readable non-volatile media (e.g., flash memory), floppy disks, compact disk-read only memory (CD-ROM), magneto-optical disks (MODs), digital versatile disks (DVDs), and / or other suitable memory elements.

[0092] Such a memory device 60 may generally be configured to store suitable computer-readable instructions that, when executed by the processor 58, configure the controller to perform various functions as described herein. Additionally, the controller may also include a communication interface 62 to facilitate communication between the controller and various components of the wind turbine 10. The interface may include one or more circuits, terminals, pins, contacts, conductors, or other components for sending and receiving control signals. Furthermore, the controller may include a sensor interface 64 (e.g., one or more analog-to-digital converters) to allow signals transmitted from the sensors 66, 68 to be converted into signals that can be understood and processed by the processor 58.

[0093] Now refer to Figure 6, a flow chart illustrating one embodiment of a method 150 for operating a wind turbine power system connected to an electrical grid is shown. More particularly, as mentioned, the wind turbine power system 100 includes a plurality of electrical components, such as, for example, a generator 102, a power converter 106, a transformer 130, switchgear, cables or wires, a pitch system 32, and a yaw system 42. In general, reference will be made herein to Figure 1-5 The method 150 is described with reference to the wind turbine 10, wind turbine power system 100, and controllers 26, 120. However, it should be understood that the disclosed method 150 may be implemented with a wind turbine having any other suitable configuration. Figure 6 The steps described herein are performed in a particular order for the purposes of illustration and discussion, and the methods discussed herein are not limited to any particular order or arrangement. Using the disclosure provided herein, one skilled in the art will appreciate that the various steps of the methods disclosed herein may be omitted, rearranged, combined, and / or altered in various ways without departing from the scope of the present disclosure.

[0094] As shown at (152), method 150 may include collecting data related to one or more parameters of one or more electrical components. In another embodiment, the data may include, for example, forecast or prediction data, simulation data, measured operational data, calculated data, turbine fleet data, and / or historical data. Additionally, as described herein, the parameters may include, for example, voltage, current, temperature, time, and / or combinations or functions thereof. Furthermore, the data may be stored in a single database or in multiple databases. Thus, in an embodiment, data may be collected from each of the multiple databases.

[0095] As shown at (154), method 150 may include performing a statistical analysis on data related to one or more parameters of one or more electrical components. In some embodiments, for example, the statistical analysis may include individual probabilities, combined probabilities, means, outliers, standard deviations, and / or combinations thereof, as well as any other suitable statistical analysis. For example, in embodiments, the individual probabilities or combined probabilities may include, for example, the probability that the electrical component operates within a certain range, the probability that one or more certain events occur in the electrical component, or the probability that one or more certain conditions occur in the electrical component.

[0096] In certain embodiments, one of the controllers described herein may be configured to determine a first probability that an electrical component operates within a first range, or a first event occurs in the electrical component, and a second probability that the electrical component operates within a second range, or a second event occurs in the electrical component. The controller may then multiply the first and second probabilities of the first and second events together to obtain a combined probability. In another embodiment, method 150 may include using the data described herein to estimate the probability that the electrical component operates within the entire PQ capability curve.

[0097] Still refer to Figure 6 As shown at (156), method 150 may include predicting the future behavior of electrical components based on statistical analysis. For example, future behavior generally refers to various attributes or characteristics of electrical components that affect system optimization. Thus, such behavior may include power output, reliability, maintenance, capacity, failure rate, temperature, voltage, current, or any other suitable parameter. In other words, in one example, the controller can use the probability of various situations occurring and design or operate a cheaper system by operating closer to the limits with smaller design margins.

[0098] As shown at (158), method 150 may include using the predicted future behavior to determine a set point with respect to the electrical component. As shown at (160), method 150 may include operating the wind turbine power system 100 at the determined set point with respect to the electrical component so as to optimize at least one characteristic of the electrical component. For example, in an embodiment, the characteristic of the electrical component may include at least one of power output, reliability, maintenance, capacity, failure rate, or any other suitable parameter. Thus, in additional embodiments, method 150 may also include optimizing the characteristic of the electrical component by allowing an increase or decrease in the characteristic of the electrical component based on a statistical analysis. For example, in an embodiment, the controller may implement a control action such as, for example, pitching one or more rotor blades 22 of the wind turbine 10, yawing the nacelle 16 of the wind turbine 10 (e.g., away from the incoming wind direction 52), de-rating the wind turbine 10, and / or any other suitable control action.

[0099] In certain embodiments, method 150 may include, for example, using the probability of the electrical component operating throughout the PQ capability curve to determine a setpoint for the electrical component. Thus, in such embodiments, method 150 may also include using the probability of the electrical component operating throughout the PQ capability curve to optimize the power output of wind turbine power system 100.

[0100] In yet another embodiment, method 150 may include deriving regional data from collected data related to electrical component parameters and generating P / Q capability curves for one or more specific regions based on the regional data. For example, in regions with a low probability of a combination of conditions (e.g., high temperature, high winds (high power), high VAR demand, high PU voltage variation, etc.), the controller may expand the P / Q capability curves to allow for increased active (P) and / or reactive (Q) power. Furthermore, using historical probabilistic data for the region, this knowledge may be applied to forecast conditions and set regional and / or seasonally specific operating ranges.

[0101] In still further embodiments, the method 150 may include, for example, by adding an additional reactive power generating unit 138 (e.g., a modular VAR box) to the Figure 3 ) is coupled to wind turbine power system 100 to customize the expansion of the PQ capability curve. In such embodiments, reactive power generation unit 138 can be used to customize P, Q operation to customer needs based on a probabilistic analysis of the customer, region, or location. Furthermore, reactive power generation unit 138 can be supplemented with this additional power electronics reactive power to achieve additional P and / or Q turbine capacity. For example, if there is high confidence and low risk for the expanded P, Q curve, a 1.85 MW wind turbine power system can be expanded to 1.95 MW at 0.9 PF by adding reactive power generation unit 138. Furthermore, by defining and selecting the size of reactive power generation unit 138, the methods described herein can be further expanded based on simulations combined with historical grid data.

[0102] In certain embodiments where wind turbine power system 100 is part of wind farm 50, method 150 may further include, via farm-level controller 56, maximizing the capacity of each of a plurality of wind turbine power systems 52 in wind farm 56, depending on the individual probabilities or combined probabilities associated with the particular wind turbine power system 52, so as to optimize the power output of wind farm 52. In such embodiments, such capacity may be maximized by applying knowledge of farm-local capabilities, such as variations in wind across wind farm 50, wake effects across wind farm 50, cables, transformer limitations, etc.

[0103] In yet additional embodiments, method 150 may include: obtaining data related to de-rating of wind turbine power system 100 due solely to increased VAR demand from the grid; and converting the data into a reduction in revenue associated with wind turbine power system 100 due to the VAR-related de-rating. Thus, the controller may use simulation to measure the OEM's financial savings when electrical components are designed with reduced overall capacity based on historical data to optimize capacity only in high-probability regions of the P, Q curve. The financial impact of the increased VAR-related de-rating may then be compared to the financial savings of lower-capacity electrical components using historical grid data.

[0104] Exemplary embodiments of a wind turbine, a controller for a wind turbine, and a method of controlling a wind turbine are described in detail above. The methods, wind turbines, and controllers are not limited to the specific embodiments described herein. Rather, components of the wind turbine and / or controllers and / or steps of the methods may be used independently and separately from other components and / or steps described herein. For example, the controllers and methods may also be used in combination with other wind turbine power systems and methods and are not limited to implementation with only the power system described herein. Rather, the exemplary embodiments may be implemented and used in conjunction with many other wind turbine or power system applications, such as solar power systems.

[0105] 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.

[0106] Aspects and embodiments of the present invention are defined by the following numbered clauses:

[0107] Clause 1. A method for operating a wind turbine power system connected to an electrical grid, the wind turbine power system having one or more electrical components, the method comprising:

[0108] collecting data related to one or more parameters of one or more electrical components;

[0109] performing statistical analysis on data related to one or more parameters of one or more electrical components;

[0110] predicting future behavior of one or more electrical components based on statistical analysis;

[0111] using the predicted future behavior to determine a set point for one or more electrical components; and

[0112] The wind turbine power system is operated at the determined set point with respect to the one or more electrical components to optimize at least one characteristic of the one or more electrical components.

[0113] Clause 2. The method of clause 1, wherein the one or more electrical components include at least one of a generator, a power converter, a transformer, a switchgear, a cable or wire, a pitch system, or a yaw system.

[0114] Clause 3. The method of any of the preceding clauses, wherein the data comprises at least one of predicted or forecast data, simulation data, measured operational data, calculated data, turbine fleet data, or historical data, the data being stored in at least one database, and the one or more parameters comprising at least one of voltage, current, temperature, time, and / or a combination or function thereof.

[0115] Clause 4. The method of any preceding clause, wherein the data is stored in a plurality of databases, wherein collecting data related to one or more parameters of one or more electrical components further comprises collecting data related to one or more parameters of one or more electrical components from the plurality of databases.

[0116] Clause 5. The method of any of the preceding clauses, wherein the statistical analysis comprises at least one of: individual probability, combined probability, mean, outlier, standard deviation, or a combination thereof.

[0117] Clause 6. The method of clause 5, wherein the individual probabilities or the combined probabilities include at least one of the following: a probability that one or more electrical components operate within a certain range, a probability that one or more certain events occur in one or more electrical components, or a probability that one or more certain conditions occur in one or more electrical components.

[0118] Clause 7. The method of clause 5, wherein the wind turbine power system is part of a wind farm comprising a plurality of wind turbine power systems, the method further comprising:

[0119] The capacity of each of the plurality of wind turbine power systems in the wind farm is maximized to optimize the power output of the wind farm, depending on individual probabilities or combined probabilities with respect to particular ones of the plurality of wind turbine power systems, via a farm-level controller that differentially distributes active power and / or reactive power demands across the plurality of wind turbine power systems.

[0120] Clause 8. The method of clause 5, wherein applying the statistical analysis to the data related to one or more parameters of one or more electrical components further comprises:

[0121] determining a first probability that one or more electrical components are operating within a first range or that a first event occurs in one or more electrical components;

[0122] determining a second probability that the one or more electrical components are operating within a second range or that a second event is occurring in the one or more electrical components; and

[0123] The first probability and the second probability of the first event and the second event are multiplied together to obtain a combined probability.

[0124] Clause 9. The method of any of the preceding clauses, wherein the at least one characteristic of the one or more electrical components comprises at least one of power output, reliability, maintenance, capacity, or failure rate.

[0125] Clause 10. The method of clause 9, further comprising optimizing at least one characteristic of the one or more electrical components by allowing an increase or decrease in the at least one characteristic of the one or more electrical components based on the statistical analysis.

[0126] Clause 11. The method of any of the preceding clauses, further comprising:

[0127] Using the data to estimate a probability that one or more electrical components will operate within a full PQ capability curve;

[0128] A set point for the one or more electrical components is determined using a probability that the one or more electrical components operate throughout the PQ capability curve.

[0129] Clause 12. The method of clause 11, further comprising optimizing power output of the wind turbine power system using a probability of one or more electrical components operating throughout the PQ capability curve.

[0130] Clause 13. The method of Clause 11, further comprising:

[0131] deriving regional data from collected data related to one or more parameters of one or more electrical components; and

[0132] A PQ capability curve for one or more specific regions is generated based on the regional data.

[0133] Clause 14. The method of clause 11, further comprising customizing an extension of the PQ capability curve by coupling additional power generating units to at least one of the wind turbine power system or a hybrid power generating system including the wind turbine power system.

[0134] Clause 15. The method of any of the preceding clauses, further comprising:

[0135] obtaining data related to derating of wind turbine power systems due solely to increased VAR demand from the grid; and

[0136] The data is converted into revenue reduction for the wind turbine power system due to VAR related derating.

[0137] Clause 16. A system for optimizing the design of one or more electrical components of a wind turbine power system, the system comprising:

[0138] A controller comprising at least one processor configured to perform a plurality of operations, the plurality of operations comprising:

[0139] collecting data related to one or more parameters of one or more electrical components;

[0140] performing a statistical analysis on the data related to one or more parameters of the one or more electrical components, the statistical analysis including at least a probability of combination;

[0141] predicting future behavior of one or more electrical components based on statistical analysis;

[0142] using the predicted future behavior to size the one or more electrical components so as to minimize a design margin of the one or more electrical components; and

[0143] The wind turbine power system is sized to optimize at least one characteristic of one or more electrical components.

[0144] Clause 17. The system of clause 16, wherein the one or more electrical components include at least one of a generator, a power converter, a transformer, a switchgear, a cable or wire, a pitch system, or a yaw system.

[0145] Clause 18. The system of clauses 16-17, wherein the at least one characteristic of the one or more electrical components comprises at least one of power output, reliability, maintenance, capacity, or failure rate.

[0146] Clause 19. The system of clauses 16-18, further comprising at least one database for storing data, the data comprising at least one of predicted or forecast data, simulation data, measured operational data, calculated data, turbine fleet data, or historical data, wherein one or more parameters comprises at least one of voltage, current, temperature, time, and / or a combination or function thereof.

[0147] Clause 20. The system of clause 17, further comprising an additional power generation unit coupled to the wind turbine power system or a hybrid power generation system including the wind turbine power system for customizing an extension of a PQ capability curve representing operation of the one or more electrical components.

[0148] 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 language of the claims.

Claims

1. A method for operating a wind turbine power system connected to an electrical grid, the wind turbine power system being part of a wind farm comprising a plurality of wind turbine power systems, the wind turbine power system having one or more electrical components, the method comprising: collecting data related to one or more parameters of the one or more electrical components; performing a statistical analysis on the data related to one or more parameters of the one or more electrical components, the statistical analysis comprising at least one of individual probabilities or combined probabilities; predicting future behavior of the one or more electrical components based on the statistical analysis; using the predicted future behavior to determine a set point for the one or more electrical components; operating the wind turbine power system at the determined set point with respect to the one or more electrical components so as to optimize at least one characteristic of the one or more electrical components; as well as The invention also provides for maximizing the capacity of each of the plurality of wind turbine power systems in the wind park, via a park-level controller that differentially distributes active power and / or reactive power demands across the plurality of wind turbine power systems, depending on individual probabilities or combined probabilities with respect to particular ones of the plurality of wind turbine power systems, so as to optimize power output of the wind park.

2. The method according to claim 1, characterized in that The one or more electrical components include at least one of a generator, a power converter, a transformer, a switchgear, a cable or wire, a pitch system, or a yaw system.

3. The method according to claim 1, characterized in that The data includes at least one of predicted or forecast data, simulated data, measured operational data, calculated data, turbine fleet data, or historical data, the data being stored in at least one database, the one or more parameters including at least one of voltage, current, temperature, time, and / or a combination or function thereof.

4. The method according to claim 1, wherein The data is stored in a plurality of databases, wherein collecting the data related to one or more parameters of the one or more electrical components further comprises collecting the data related to one or more parameters of the one or more electrical components from the plurality of databases.

5. The method according to claim 1, characterized in that The individual probabilities or the combined probabilities include at least one of the following: a probability that the one or more electrical components operate within a certain range, a probability that one or more certain events occur in the one or more electrical components, or a probability that one or more certain conditions occur in the one or more electrical components.

6. The method according to claim 1, characterized in that Applying the statistical analysis to the data related to one or more parameters of the one or more electrical components further comprises: determining a first probability that the one or more electrical components are operating within a first range or that a first event occurs in the one or more electrical components; determining a second probability that the one or more electrical components are operating within a second range or that a second event occurs in the one or more electrical components; and The first and second probabilities of the first and second events are multiplied together to obtain the combined probability.

7. The method according to claim 1, characterized in that The at least one characteristic of the one or more electrical components includes at least one of power output, reliability, maintenance, capacity, or failure rate.

8. The method according to claim 7, characterized in that The method also includes optimizing at least one characteristic of the one or more electrical components by allowing an increase or decrease in the at least one characteristic of the one or more electrical components based on the statistical analysis.

9. The method according to claim 1, characterized in that The method further comprises: using the data to estimate a probability that the one or more electrical components will operate within a full PQ capability curve; and A set point for the one or more electrical components is determined using a probability of the one or more electrical components operating throughout the PQ capability curve.

10. The method according to claim 9, characterized in that The method also includes optimizing power output of the wind turbine power system using the probability of the one or more electrical components operating throughout the PQ capability curve.

11. The method according to claim 9, characterized in that The method further comprises: deriving regional data from collected data related to one or more parameters of the one or more electrical components; and A PQ capability curve for one or more specific regions is generated based on the regional data.

12. The method according to claim 9, characterized in that The method also includes customizing an extension of the PQ capability curve by coupling additional power generating units to at least one of the wind turbine power system or a hybrid power generating system including the wind turbine power system.

13. The method according to claim 1, wherein The method further comprises: obtaining data related to a derating of the wind turbine power system due solely to an increase in VAR demand from the grid; and The data is converted into a revenue reduction for the wind turbine power system due to VAR-related derating.

14. A system for optimizing the design of one or more electrical components of a wind turbine power system, the system comprising: A controller comprising at least one processor configured to perform the method according to any one of claims 1 to 13, wherein the statistical analysis comprises at least a combined probability, and wherein the processor is further configured to: using the predicted future behavior to size the one or more electrical components so as to minimize a design margin of the one or more electrical components; as well as The wind turbine power system is sized to optimize at least one characteristic of the one or more electrical components.

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