System and method for optimizing operation of a wind farm

Abstract

A method for optimizing operation of a wind farm includes receiving sensor information from one or more measurement systems of respective wind turbines of the wind farm indicative of operation of the respective wind turbines. The method also includes determining, via turbine estimators, estimates of one or more local wind parameters and a turbine state of each of a plurality of wind turbines based on the sensor information. Further, the method includes determining, via a farm estimator, estimates of one or more farm wind parameters based on, at least, the estimates and the turbine states. The method further includes estimating, via the farm estimator, a wind farm resource based on the estimates and determining one or more setpoints for each of the wind turbines based on the wind farm resource. Thus, the method includes controlling, via the plurality of turbine controllers, the plurality of wind turbines based on the setpoint(s).

Description

SYSTEM AND METHOD FOR OPTIMIZING OPERATION OF A WIND FARMFIELD

[0001] The present disclosure relate generally to wind turbines, and more particularly to systems and methods for optimizing operation of a wind farm using, e.g., hierarchical estimation.BACKGROUND

[0002] 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 modem wind turbine typically includes a tower, a generator, a gearbox, a nacelle, and one or more rotor blades. The rotor blades capture kinetic energy from wind using known foil principles and transmit the kinetic energy7through rotational energy to turn a shaft coupling the rotor blades to a gearbox, or if a gearbox is not used, directly to the generator. The generator then converts the mechanical energy to electrical energy that may be deployed to a utility grid.

[0003] Such wind turbines are typically located in a wind farm spread across a specific geographical region such that the wind passing over the region causes the blades associated with the wind turbines to rotate. Traditionally, wind farms are controlled in a decentralized fashion to generate power such that each turbine is operated to maximize local power output and to minimize impacts of local fatigue and extreme loads. However, in practice, such independent optimization of the wind turbines ignores farm performance goals, thereby leading to sub-optimal performance at the wind farm. For example, independent optimization of the wind turbines may not account for aerodynamic interactions such as wake effects between neighboring turbines within the wind farm that may affect a farm power output.

[0004] Typically, wake effects include a reduction in wind speed and increased wind turbulence at a downstream wind turbine due to a conventional operation of an upstream wind turbine. The reduced wind speed causes a proportional reduction in a power output of the downstream wind turbine. Moreover, the increased turbulence increases the fatigue loads placed on the downstream wind turbine. Several studies have reported a loss of more than 10% in the annual energy production (AEP) of thewind farm owing to the wake effects between neighboring independently optimized wind turbines within the wind farm.

[0005] Accordingly, some currently available approaches attempt to optimize power generation at the wind farm by mitigating an impact of the wake effects through a coordinated control of the wind turbines in the wind farm. Typically, mitigating the wake effects involves accurately modeling the wake effects experienced at different wind turbines in the wind farm. For example, empirical or semi -empirical thrust-based, and / or high fidelity physics-based models may be used to model the wake effects between the aerodynamically interacting wind turbines in the wind farm.

[0006] Conventionally, the empirical or semi-empirical models (engineering wake models) are generated based on field-experiment data and / or historical wind information. Accordingly, these models may be used to design the layouts of wind farms so as to optimize one or more performance goals before installation of the wind turbines. Alternatively, these models may be used to optimize performance of the wind farm subsequent to the installation.

[0007] One optimization approach, for example, employs the engineering wake models to determine control settings for the wind turbines. Particularly, the engineering wake models determine the control settings so as to operate upstream turbines at lower efficiencies, which in turn, allows for greater energy recovery at the downstream turbines. The optimization approach uses the engineering wake models for adjusting a yaw- alignment of the upstream turbines relative to an incoming wind direction to steer the resulting wake effects aw ay from the downstream turbines.

[0008] Furthermore, traditional farm wake estimation approaches are implemented independent from the turbine controllers. Existing approaches rely on turbine sensor information such as generator power, or wind speed from anemometers to provide additional information at a farm controller. In part, this is because the farm controller that manages wakes are intentionally designed to avoid interaction with turbine controllers because the developers of farm control solutions are typically third parties that may not have access to the turbine controller data. Because of this fact, traditional approaches cannot include detailed local wind information at each turbine location (e.g., accurate wind speed, direction vertical, horizontal shear, veer) or localturbine / flow interaction information (such thrust, lift drag distribution along blade surfaces, tower drag distribution, etc.). As a result, conventional calculations of wind farm flow characteristics are relatively inaccurate. Consequently, any control solution that relies on conventional wind farm flow features will have relatively lower performance benefits.

[0009] For example, wake optimization solutions that yaw the wind turbines to reduce wake losses in traditional approaches produce lower power gains because the characterization of wind parameters have additional inaccuracy (noise) and therefore, the computed yaw angles will be sub-optimal. In addition, farm control solutions for tracking a power signal incur additional power oscillations, degraded tracking capabilities, and degraded power reliability because conventional wind resources are not accurately known in traditional approaches. Accordingly, there is a need for improved systems and methods for optimizing operating a wind farm using, e.g., hierarchical estimation.BRIEF DESCRIPTION

[0010] Aspects and advantages of the present disclosure 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 present disclosure.

[0011] In an aspect, the present disclosure is directed to a method for optimizing operation of a wind farm having a plurality of wind turbines. The wind farm includes a computational farm module having a farm controller and a farm estimator. The computational farm module is communicatively coupled to a plurality of computational turbine modules associated with the plurality of wind turbines. The computational turbine modules each include a turbine controller and a turbine estimator. Each of the turbine estimators is configured to implement one or more models of wind turbine dynamics and local aerodynamics. The method includes receiving sensor information from one or more measurement systems of respective wind turbines of the plurality of wind turbines indicative of operation of the respective wind turbines, determining, via the turbine estimators, estimates of one or more local wind parameters and a turbine state of each of the plurality of wind turbines based on the sensor information, determining, via the farm estimator,estimates of one or more farm wind parameters based on, at least, the estimates of the one or more local wind parameters and the turbine state of each of the plurality of wind turbines; estimating, via the farm estimator, a wind farm resource based on the estimates of the one or more farm wind parameters; determining, via the farm controller, one or more setpoints for each of the plurality7of wind turbines based on the wind farm resource; and controlling, via the plurality of turbine controllers, the plurality of wind turbines based on the one or more setpoints.

[0012] In another aspect, the present disclosure is directed to a wind farm. The wind farm includes a computational farm module having a farm controller and a farm estimator. The wind farm also includes a plurality of wind turbines and a plurality of computational turbine modules associated with the plurality of wind turbines. The computational turbine modules each include a turbine controller and a turbine estimator. Each of the turbine estimators is configured to implement one or more models of wind turbine dynamics and local aerodynamics. The computational farm module is communicatively coupled to the plurality of computational turbine modules. Further, the farm controller and the plurality of computational turbine modules are configured to communicate together to perform a plurality of operations. The plurality of operations include receiving sensor information from one or more measurement systems of respective wind turbines of the plurality of wind turbines indicative of operation of the respective wind turbines, determining, via the turbine estimators, estimates of one or more local wind parameters and a turbine state of each of the plurality of wind turbines based on the sensor information, determining, via the farm estimator, estimates of one or more farm wind parameters based on, at least, the estimates of the one or more local wind parameters and the turbine state of each of the plurality of wind turbines, estimating, via the farm estimator, a wind farm resource based on the estimates of the one or more farm wind parameters, determining, via the farm controller, one or more setpoints for each of the plurality' of wind turbines based on the wind farm resource, and controlling, via the plurality of turbine controllers, the plurality of wind turbines based on the one or more setpoints.

[0013] These and other features, aspects and advantages of the present disclosure will be further supported and described with reference to the following description and appended claims. The accompanying drawings, which are incorporated in andconstitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.BRIEF DESCRIPTION OF THE DRAWINGS

[0014] A full and enabling disclosure of the present disclosure, including the best mode thereof, directed to one of ordinary skill in the art, is set forth in the specification, which makes reference to the appended figures, in which:

[0015] FIG. 1 illustrates a schematic diagram of an embodiment of a w ind farm according to the present disclosure;

[0016] FIG. 2 illustrates a schematic diagram of a system for optimizing operation of a wind farm having a plurality of wind turbines according to the present disclosure;

[0017] FIG. 3 illustrates a flow diagram of a method for optimizing operation of a wind farm having a plurality of wind turbines according to the present disclosure;

[0018] FIG. 4 illustrates a schematic diagram of an embodiment of a flow of information between turbine estimators of a plurality of w ind turbines in a wind farm and a farm estimator according to the present disclosure.DETAILED DESCRIPTION

[0019] Reference now will be made in detail to embodiments of the present disclosure, one or more examples of w hich are illustrated in the drawings. Each example is provided by way of explanation of the present disclosure, not limitation of the present disclosure. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made in the present disclosure without departing from the scope or spirit of the present disclosure. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present disclosure covers such modifications and variations as come within the scope of the appended claims and their equivalents.

[0020] The following description presents exemplary embodiments of online systems and methods for optimizing operation of a wind farm. Particularly, embodiments illustrated herein disclose an online system for estimating a wind farmresource and controlling the wind farm based on the wind farm resource. In an embodiment, the local turbine controllers include computational modules associated with each wind turbine in the wind farm. The computational modules include a wind turbine estimator and a wind turbine controller. Each wind turbine estimator has a model of a w ind turbine dynamics together with a model of the surrounding aerodynamics. Each wind turbine estimator receives sensor information from standard measurement systems, which can include sensors for generator speed, tower top acceleration, rotor imbalance proximity sensors, blade moments, pitch angle for each blade, electrical power, and / or any other suitable measurements. Further, in an embodiment, one or more anemometers for wind speed and wind vane for wind direction may also be received. Moreover, the wind turbine estimators are configured to calculate estimates of wind parameters like wind speed, vertical and horizontal wind direction, vertical and horizontal misalignment, induced velocities, wake strength, wake direction, etc.

[0021] Furthermore, the farm controller includes a model of the farm wind. This model(s) can be represented in different ways and in general are given in terms of wind velocities at a discrete grid of reference points in the wind farm layouts. Thus, in an embodiment, a goal of the farm controller is to provide an accurate representation of the wind farm resource. This can include, for example, configuration at the turbine location, including information of the w ake geometry and intensity, wind direction, and / or shear. As such, the wind farm resource can be used, for example, to optimize wind farm operation in real time by increasing power output and / or better management of turbine loads.

[0022] Referring now to the drawings. FIG. 1 illustrates an embodiment of a wind farm 100 according to aspects of the present disclosure. In an embodiment, the wind farm 100 includes a plurality7of wind turbines 102 arranged in a desired geometrical layout. In an embodiment, the wind farm 100 may be an onshore wind farm. In another embodiment, the wind farm 100 may be an offshore wind farm with floating wind turbines. Further, the geometrical layout of the wind turbines 102 may be arranged randomly, in a single row, or in an array of rows and columns using one or more layout optimization algorithms. Generally, the optimization algorithms may be designed to maximize positive effects of expected wind velocity7and direction on theperformance goals such as annual energy production (AEP), while minimizing negative effects such as an increase in fatigues loads associated with each of the individual wind turbines 102.

[0023] In an embodiment, each of the wind turbines 102 includes one or more energy conversion modules such as rotor blades 104, a step-up gearbox (not shown), and a power generator (not shown) that converts wind energy into usable electrical energy. Additionally, the wind turbines 102 also include blade pitch mechanisms (not shown) to regulate turbine power output and rotor speed, yaw mechanisms (not shown), and one or more monitoring devices (e.g. sensors) that work cohesively with other components of the wind turbines 102 to swivel and align the rotor blades 104 in line and / or or relative to the prevailing wind direction. Moreover, the wind turbines 102 may also include cooling units (not shown) to prevent components of the wind turbines 102 from overheating, braking systems (not shown) to stop the rotor blades 104 from rotating when desired, and nacelles (not shown) for protecting the different components of the wind turbines 102 from environmental factors.

[0024] Typically, the rotor blades 104 of the wind turbines 102 are aligned in a substantially similar direction, for example, the direction of incoming wind during operation of the wind turbine 102. Such a blade alignment, however, positions certain downstream wind turbines 102 behind certain upstream wind turbines 102 in the wind farm 100, thus resulting in wake effects that adversely impact operations of the downstream wind turbines 102. For example, the wind blowing across the rotor blades 104 of upstream wind turbines 102 causes rotation of the corresponding blades 104. The rotating blades 104 convert at least some of the kinetic energy of the incoming wind into mechanical energy, thus reducing the wind speed experienced at the downstream wind turbine 102. while further increasing the turbulence.

[0025] Since power output of wind turbines 102 is proportional to incoming wind velocity, a reduction in wind speed at the downstream wind turbine 102 owing to the wake effects reduces a corresponding power output. Additionally, the turbulence caused by the wake effects may damage turbine components due to cyclical fatigue loading. For example, fatigue loading may initiate small cracks on surfaces of the turbine components that may increase in size and propagate, thus potentially leading to failure of the dow nstream wind turbine 102.

[0026] Accordingly, each of the wind turbines 102 includes one or more turbine controllers 106 that regulate the operation of the corresponding wind turbines 102. In an embodiment, the turbine controllers 106 regulate the operation of the wind turbines 102 based on ambient conditions, user-inputs, and / or commands received from an associated farm controller 108. Accordingly, the turbine controllers 106 may include application-specific processors, programmable logic controller (PLC), digital signal processors (DSPs), microcomputers, microcontrollers, Application Specific Integrated Circuits (ASICs) and / or Field Programmable Gate Arrays (FPGAs).

[0027] Furthermore, the turbine controllers 106 may be communicatively coupled to the farm controller 108 and / or a plurality of sensors 110 via a wired and / or wireless communications network 112. The communications network 112, for example, 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.

[0028] Moreover, the sensors 110 may provide direct or indirect measurement of wind parameters such as wind speed, wind direction, ambient temperature, pressure, density, turbulence, wind shear, power output of the wind turbines 102, and / or any other suitable parameters. In certain embodiments, the sensors 110 may be posit oned within and / or outside the wind farm 100 to measure parameters such as supervisory’ control and data acquisition (SCAD A) information including wind experienced and / or expected at the different wind turbines 102. In an embodiment, for example, the sensors 110 may be disposed on or proximal to the wind turbines 102 to measure SCADA information corresponding to ambient conditions.

[0029] Referring now to FIG. 2. a system 200 for optimizing operation of a wind farm having a plurality of wind turbines, such as wind farm 100, is illustrated according to the present disclosure. More specifically, as shown, the system 200 includes a computational farm module 202 having a farm controller 204 (similar to farm controller 108) and a farm estimator 206. Further, as shown, the computational farm module 202 is communicatively coupled to a plurality of computational turbine modules 208 associated with the plurality of wind turbines 102. Moreover, as show n, each of the computational turbine modules 208 includes a turbine controller 210(similar to turbine controller 106) and a turbine estimator 212. In addition, as shown, each of the turbine estimators 212 is configured to implement one or more turbine models 214 of wind turbine dynamics and local aerodynamics and may also include estimation equations 216 programmed therein. In an embodiment, for example, the turbine estimator(s) 212 may utilize a non-linear Kalman filter (such as an extended Kalman filter, an unscented Kalman filter, an ensemble Kalman filter, etc.), a moving horizon estimation, or any other suitable estimation means. As used herein, a Kalman filter, also known as linear quadratic estimation (LQE), generally refers to an algorithm that uses a series of measurements observed over time, which typically contains noise, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone.

[0030] Similarly, as shown, the farm estimator 206 may also be configured to implement a farm model 218 of farm-level flow dynamics and may include estimation equations 220. As such, in an embodiment, the farm model 218 includes one or more wind velocities at a discrete grid of reference points in a layout of the wind farm 100. Furthermore, in an embodiment, the farm model 218 may be any suitable model, such as but not limited to an analytic model, a set of equations of discretized partial differential equations, a data-driven models, or combinations thereof, or any other suitable model.

[0031] Thus, as shown in FIG. 3, a flow diagram of an embodiment of a method 300 for optimizing operation of a wind farm having a plurality of w ind turbines is illustrated according to the present disclosure. Such a method 300, for example, may be implemented by the system 200 of FIG. 2. Further, in an embodiment, the method 300 may be described in a general context of non-transitory computer executable instructions on a computing system or a processor. Generally, computer executable instructions may include routines, programs, objects, components, data structures, procedures, modules, functions, and the like that perform particular functions or implement particular abstract data types.

[0032] Additionally, embodiments of the method 300 may also be practiced in a distributed computing environment where optimization functions are performed by remote processing devices that are linked through a wired and / or wireless communication network. In the distributed computing environment, the computerexecutable instructions may be located in both local and remote computer storage media, including memory storage devices.

[0033] Further, in FIG. 3, the method 300 is illustrated as a collection of blocks in a logical flow chart, which represents operations that may be implemented in hardware, software, or combinations thereof. The order in which the method is described is not intended to be construed as a limitation, and any number of the described blocks may be combined in any order to implement the exemplary method disclosed herein, or an equivalent alternative method. Additionally, certain blocks may be deleted from the exemplary method or augmented by additional blocks with added functionality without departing from the spirit and scope of the subject matter described herein.

[0034] As shown at (302), the method 300 includes receiving, via each of the turbine estimators 212, sensor information 222 from one or more measurement systems of respective wind turbines 102 (e.g., Turbine #1, Turbine #N, and so on) of the plurality’ of wind turbines 102 indicative of operation of the respective wind turbines 102. For example, in an embodiment, the sensor information may include generator speed, rotor speed, tower top acceleration, rotor imbalance proximity^ sensor data, blade moments, blade twist, pitch angle, torque output, power output, or combinations thereof, as well as any other sensor information. In an embodiment, the use of the blade twist effect at the turbine level can improve accuracy of determining the aerodynamic behavior of the rotor blades.

[0035] As shown at (304), the method 300 includes determining, via the one or more models turbine 214, estimates 224 of one or more local wind parameters and a turbine state of each of the plurality of wind turbines 102 based on the sensor information 222. For example, in an embodiment, the estimates 224 of the local wind parameter(s) may include wind speed, w ind direction, vertical and horizontal misalignment, induced velocities, wake strength, wind shear, wind direction, wind veer, wind turbulence, one or more wake parameters, or combinations thereof, as w ell as any other wind parameters. In other embodiment, another input to the turbine model(s) 214 may include one or more wind measurements from one or more anemometers or wind vanes of the plurality of wind turbines 102.

[0036] Still referring to FIG. 3, as shown at (306), the method 300 includesdetermining, via the farm estimator 206, estimates 226 of one or more farm wind parameters based on. at least, the estimates 224 of the one or more local wind parameters and the turbine state of each of the plurality of wind turbines 102 (e.g., using the estimation equations 220). For example, in an embodiment, and similar to the estimates 224 of the local wind parameter(s), the estimates 226 of the farm wind parameter(s) may include wind speed, wind direction, vertical and horizontal misalignment, induced velocities, wake strength, wind shear, wind direction, wind veer, wind turbulence, one or more wake parameters, or combinations thereof, as well as any other wind parameters. Moreover, in an embodiment, the farm estimator 206 may receive forecast information to provide more accurate predictions more accurate (such as a future time history of the farm power). Providing an estimate of the power and its associated uncertainty for every minute for the future can be useful in the participation of ancillary services markets, as it can increase the reliability of power production of the wind farm 100.

[0037] As shown at (308), the method 300 includes estimating, via the farm estimator 206, a wind farm resource 228 based on the estimates 226 of the farm wind parameter(s). In another embodiment, the farm estimator 206 may also utilize additional farm sensor information 234 and / or an additional hierarchical level to capture inter-farm effects in the calculation of the estimates 226 of the farm wind parameter(s). For example, in an embodiment, the additional farm sensor information may include met mast measurements, remote sensor information such as Lidar, Radar, or Sodar sensor data, wind and weather forecast data, and / or any other additional farm-level information. Furthermore, in an embodiment, the additional hierarchical level may generally include information and / or a model relating to an atmospheric boundary layer. In such embodiments, this model is configured to capture features such as a height of the boundary layer, friction with the sea surface or with ground, stability properties characterized by the vertical temperature profile, momentum transport through turbulence structures, etc.

[0038] Referring back to FIG. 3. as shown at (310), the method 300 includes determining, via the farm controller 204, one or more setpoints 230, 232 for each of the plurality of wind turbines 102 based on the wind farm resource 228. As shown at (312), the method 300 includes controlling, via the plurality of turbine controllers210, the plurality of wind turbines 102 based on the one or more setpoints 230, 232. Furthermore, in an embodiment, the method 300 may include exchanging information between the farm controller 204 and the plurality of turbine controllers 210 to converge common parameters. For example, in an embodiment, the method 300 may include exchanging information about turbine and farm level to converge on alike parameters like vertical shear, horizontal shear, veer, local speed, local direction, induced velocities. Thus, in an embodiment, the method 300 can use a decentralized estimation approach that exchanges information between the turbine layer and the farm layer to accelerate the overall convergence of the estimation.

[0039] Accordingly, in an embodiment, systems and methods of the present disclosure are configured to utilize one or more aeroelastic models (e.g., that includes turbine structure details and local wind flow behavior) at the turbine level, for every turbine. In an embodiment, the aeroelastic model(s) includes turbine operation states such as rotor speed, flexible deformation of the tower and rotor blades, and local aerodynamic models (e.g., Blade Element Momentum (BEM) with dynamic inflow and blade root and tip corrections for 3D losses. Further, in an embodiment, the aeroelastic model(s) are configured to model the aeroelasticity of each wind turbine 102 using, e.g., first principle models similar to those available in aeroelastic simulation environments for wind turbines (e.g.. Flex5, Bladed, Simpack, Hawcs2, etc.). In further embodiments, systems and methods of the present disclosure may also use other modeling frameworks such as data-driven models with machine learning algorithms. In an embodiment, wherein the wind farm 100 is a floating offshore wind turbine, the aeroelastic model(s) may include at least six new degrees of freedom of the platform and the dynamics of one or more mooring lines.Moreover, in an embodiment, the farm flow model may include wind interaction with waves for offshore wind farms. In another embodiment, wherein the wind farm 100 is an onshore wind turbine, the aeroelastic model(s) may be configured to capture the effect of uneven terrain and / or may include vertical wind velocity components to the distributed flow properties.

[0040] Furthermore, in an embodiment, systems and methods of the present disclosure are configured to utilize farm flow models that represent the behavior of wakes (e.g., wake intensity, deflection, expansion, blockage, vorticity, turbulencemixing, acceleration in between wakes, etc.) as a function of the undisturbed wind properties (e.g., wind direction, wind speed, wind turbulence, wind shear, wind veer, etc.), the turbine layout, the structure-flow interactions (such as thrust, lift, drag in the rotor blades and / or tower, etc ), the yaw angle of each turbine and the control settings for each turbine. The farm flow7models can be represented by different modeling frameworks, including but not limited to analytic models (where the flow features are captured by analytic formulas to describe e.g.. the speed deficit beyond every turbine, or the blockage effect in the induction zone), set of equations of discretized partial differential equations (this is the case of ordinary differential equations resulting from temporal and spatial discretization of the Navier Stokes (NS) equations), the Reynold Averaged NS (RANS) equations and / or any further simplification of the first principle equations, data-driven models (typically implemented through the use of machine learning (ML) algorithms such as neural networks), and / or any combinations thereof.

[0041] In an embodiment, any distributed representation of the flow7(3D spatially distributed wind speeds) such as the numeric solution of discretized models can include all the flow features implicitly. In this case, the flow characteristics can be calculated by post-processing the numeric solution.

[0042] The outputs of the turbine estimator 212 (such as a Kalman filter (KF), an ensemble Kalman filter (enKF), an extended KF (EKF), and unscented KF (UKF), a moving horizon estimation (MHE)) from every wind turbine 102 can be used as a virtual sensor input to the farm level estimation algorithm. These outputs can include turbine operation parameters (e.g., power, speed, pitch, yaw-, etc.), local wind magnitudes (e.g., wind speed, wind direction, wind shear, wind turbulence, wind veer, etc.) and magnitudes representing the flow-structure interaction (e.g., thrust, distributed tower drag, distributed lift, drag and aerodynamic moments at the rotor blades).

[0043] The farm flow7estimation algorithm (which may also include any one or more of a EKF / UKF / EnKF / MHE, or any model-based optimization based approach to minimize the estimation error) at the farm level uses local sensors (e.g.. power, speed, anemometers, vanes, etc.), virtual wind sensors (e g., estimated wind speed, direction, shear, wind veer, etc.), virtual force sensors (e.g., thrust, lift, drag, aerodynamic moments) to estimate properties of the farm flow / wakes. As used herein, virtualsensors are calculations performed at the turbine controller 210 with a local estimation algorithm. In another embodiment, if remote wind measurements are available, the farm flow estimation algorithm may be configured to compute properties directly from the measurements and avoid the use of the farm flow model for the estimation. Despite of this apparent simplification, in another embodiment, any optimizationbased control making use of the farm flow parameters can also utilize the farm flow to be able to link one or more control actions (e.g.. yaw) with the change in farm flow properties that are needed to improve the target operating performance magnitude.

[0044] In further embodiments, systems and methods of the present disclosure may also use remote sensing (such as LIDAR, radar, etc.) as an additional sensor input. Moreover, in an embodiment, systems and methods of the present disclosure may also use analytical derivatives in the farm model(s) 218 and / or the turbine model(s) 214.

[0045] In another embodiment, in addition to the decentralized approach described herein, systems and methods of the present disclosure may include a centralized approach where all the turbine models and the farm flow models are integrated into a single model with coupling dynamics. In such embodiments, it may be useful to simplify coupling terms to neglect coupling between wind turbines 102 that are far apart in the wind farm 100 and not aligned with the wind direction. This simplification is configured to reveal sparsity structure in the model matrices, which can be used to save computation time.

[0046] In additional embodiments, systems and methods of the present disclosure provide multiple advantages such as wake management for maximum power production, power tracking at the farm level as part of the participation in ancillary services, reduction of power variability with respect to the committed allocations in the energy market (e.g., day-ahead, hour ahead), reduction of turbine component fatigue life expenditure, and / or reduction of maintenance costs.

[0047] Referring now to FIG. 4, a schematic diagram of an embodiment of the flow of information between the turbine estimators 212 of the wind turbines 102 in a wind farm 100 and the farm estimator 206 according to the present disclosure is illustrated. Thus, in an embodiment, as shown, there are multiple parameters at the farm level and the turbine level that may represent alike magnitudes. For example,the local wind flow speeds upstream of a wind turbine can be obtained by averaging the local wind from the farm estimation, while the local upstream speed for the same turbine can also be estimated by the turbine estimator 212. Since both estimated magnitudes should be consistent, the farm magnitude can be used as a virtual measurement for the turbine level estimation, as illustrated by consistency loop 236 of FIG. 4.

[0048] Other magnitudes that may also be implemented in this manner may include, for example, wind direction, vertical and horizontal wind shear, wind turbulence, etc. as computed from the turbine model and the farm model, swirl in induction zone consistency with the tangential induced velocities, and / or axial induction from the farm model 218 with axial induced velocities in the turbine model 214. Such models may include 2D blade airfoil aerodynamics, 3D correction factors for the blade root and tip, wake angle, dynamic inflow model to capture the time evolution of the induced axial and tangential velocities at the rotor plane, the distributed forces and moment between the flow and the structure, etc., considering the real-time blade and tower kinematics.

[0049] It may be noted that the foregoing examples, demonstrations, and process steps that may be performed by certain components of the present systems, for example by the turbine controllers 106 and / or farm controller 108 of FIG. 1 may be implemented by suitable code on a processor-based system. To that end, the processor-based system, for example, may include a general-purpose or a specialpurpose computer. It may also be noted that different implementations of the present disclosure may perform some or all of the steps described herein in different orders or substantially concurrently.

[0050] Additionally, the functions may be implemented in a variety of programming languages, including but not limited to Ruby, Hypertext Preprocessor (PHP), Perl, Delphi, Python, C, C++, or Java. Such code may be stored or adapted for storage on one or more tangible, machine-readable media, such as on data repository chips, local or remote hard disks, optical disks (that is, CDs or DVDs), solid-state drives, or other media, which may be accessed by the processor-based system to execute the stored code.

[0051] Further aspects are provided by the subject matter of the following clauses:

[0052] A method for optimizing operation of a wind farm having a plurality' of wind turbines, the wind farm comprising a computational farm module comprising a farm controller and a farm estimator, the computational farm module communicatively coupled to a plurality of computational turbine modules associated with the plurality of wind turbines, the plurality' of computational turbine modules each comprising a turbine controller and a turbine estimator, each of the turbine estimators configured to implement one or more models of wind turbine dynamics and local aerodynamics, the method comprising: receiving sensor information from one or more measurement systems of respective w ind turbines of the plurality of w ind turbines indicative of operation of the respective w ind turbines; determining, via the turbine estimators, estimates of one or more local wind parameters and a turbine state of each of the plurality of wind turbines based on the sensor information; determining, via the farm estimator, estimates of one or more farm w ind parameters based on, at least, the estimates of the one or more local wind parameters and the turbine state of each of the plurality of wind turbines; estimating, via the farm estimator, a wind farm resource based on the estimates of the one or more farm wind parameters; determining, via the farm controller, one or more setpoints for each of the plurality of wind turbines based on the wind farm resource; and controlling, via the plurality of turbine controllers, the plurality of wind turbines based on the one or more setpoints.

[0053] The method of any preceding clause, wherein the sensor information comprises at least one of generator speed, rotor speed, tower top acceleration, rotor imbalance proximity sensor data, blade moments, blade twist, pitch angle, torque output, or power output.

[0054] The method of any preceding clause, wherein at least one of the estimates of the one or more local wind parameters or the estimates of one or more farm wind parameters comprise any one or more of wind speed, wind direction, vertical and horizontal misalignment, induced velocities, wake strength, wind shear, wind direction, wind veer, wind turbulence, or one or more wake parameters.

[0055] The method of any preceding clause, further comprising determining, via the one or more models, the estimates of one or more local wind parameters and the turbine state of each of the plurality of wind turbines based on the sensor informationand one or more wind measurements from one or more anemometers or wind vanes of the plurality of wind turbines.

[0056] The method of any preceding clause, wherein the farm estimator comprises an implementation of a farm model of farm flow dynamics, the farm model comprising one or more wind velocities at a discrete grid of reference points in a layout of the wind farm.

[0057] The method of any preceding clause, wherein the farm model comprises at least one of an analytic model, a set of equations of discretized partial differential equations, a data-driven models, or combinations thereof.

[0058] The method of any preceding clause, wherein each of the turbine estimators utilizes at least one of a non-linear Kalman filter, or a moving horizon estimation.

[0059] The method of any preceding clause, further comprising estimating, via farm estimator, the wind farm resource based on the estimates of the one or more farm wind parameters and additional farm sensor information, the additional farm sensor information comprising at least one of met mast measurements, remote sensor information comprising Tidar, Radar, or Sodar sensor data, or wind and weather forecast data.

[0060] The method of any preceding clause, further comprising estimating, via farm estimator, the wind farm resource based on the estimates of the one or more farm wind parameters and an additional hierarchical level to capture inter-farm effects, the additional hierarchical level comprising information relating to an atmospheric boundary layer.

[0061] The method of any preceding clause, further comprising exchanging information between the farm controller and the plurality of turbine controllers to improve convergence of magnitudes estimated by the farm estimator and the turbine estimators, the magnitudes comprising at least one of wind speed, wind shear, wind direction, or wind veer.

[0062] A wind farm, comprising: a computational farm module comprising a farm controller and a farm estimator; a plurality of wind turbines; and a plurality of computational turbine modules associated with the plurality of wind turbines, the plurality of computational turbine modules each comprising a turbine controller and aturbine estimator, each of the turbine estimators configured to implement one or more models of wind turbine dynamics and local aerodynamics, the computational farm module communicatively coupled to the plurality of computational turbine modules, wherein the farm controller and the plurality of computational turbine modules are configured to communicate together to perform a plurality of operations, the plurality of operations comprising: receiving sensor information from one or more measurement systems of respective wind turbines of the plurality of wind turbines indicative of operation of the respective wind turbines; determining, via the turbine estimators, estimates of one or more local wind parameters and a turbine state of each of the plurality of wind turbines based on the sensor information; determining, via the farm estimator, estimates of one or more farm wind parameters based on. at least, the estimates of the one or more local wind parameters and the turbine state of each of the plurality of wind turbines; estimating, via the farm estimator, a w ind farm resource based on the estimates of the one or more farm wind parameters; determining, via the farm controller, one or more setpoints for each of the plurality of wind turbines based on the wind farm resource; and controlling, via the plurality of turbine controllers, the plurality of wind turbines based on the one or more setpoints.

[0063] The wind farm of any preceding clause, wherein the sensor information comprises at least one of generator speed, rotor speed, tow er top acceleration, rotor imbalance proximity sensor data, blade moments, blade twist, pitch angle, torque output, or power output.

[0064] The wind farm of any preceding clause, wherein at least one of the estimates of the one or more local wind parameters or the estimates of one or more farm wind parameters comprise any one or more of wind speed, wind direction, vertical and horizontal misalignment, induced velocities, wake strength, wind shear, wind direction, wind veer, wind turbulence, or one or more w ake parameters.

[0065] The wind farm of any preceding clause, wherein the plurality of operations further comprise determining, via the one or more models, the estimates of one or more local wind parameters and the turbine state of each of the plurality of wind turbines based on the sensor information and one or more wind measurements from one or more anemometers or wind vanes of the plurality' of wind turbines.

[0066] The wind farm of any preceding clause, wherein the farm estimator comprises an implementation of a farm model of farm flow dynamics, the farm model comprising one or more wind velocities at a discrete grid of reference points in a layout of the wind farm.

[0067] The wind farm of any preceding clause, wherein the farm model comprises at least one of an analytic model, a set of equations of discretized partial differential equations, a data-driven models, or combinations thereof.

[0068] The wind farm of any preceding clause, wherein each of the turbine estimators utilizes at least one of a non-linear Kalman filter, or a moving horizon estimation.

[0069] The wind farm of any preceding clause, wherein the plurality of operations further comprise estimating, via farm estimator, the wind farm resource based on the estimates of the one or more farm wind parameters and additional farm sensor information, the additional farm sensor information comprising at least one of met mast measurements, remote sensor information comprising Lidar, Radar, or Sodar sensor data, or wind and weather forecast data.

[0070] The wind farm of any preceding clause, wherein the plurality of operations further comprise estimating, via farm estimator, the wind farm resource based on the estimates of the one or more farm wind parameters and an additional hierarchical level to capture inter-farm effects, the additional hierarchical level comprising information relating to an atmospheric boundary layer.

[0071] The wind farm of any preceding clause, wherein the plurality of operations further comprise exchanging information betw een the farm controller and the plurality of turbine controllers to improve convergence of magnitudes estimated by the farm estimator and the turbine estimators, the magnitudes comprising at least one of wind speed, wind shear, wind direction, or wind veer.

[0072] Although specific features of embodiments of the present disclosure may be shown in and / or described with respect to some drawings and not in others, this is for convenience only. It is to be understood that the described features, structures, and / or characteristics may be combined and / or used interchangeably in any suitable manner in the various embodiments, for example, to construct additional assemblies and methods for use in wind farm optimization.

[0073] While only certain features of the present disclosure have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the present disclosure.

Claims

WHAT IS CLAIMED IS:

1. A method for optimizing operation of a wind farm having a plurality’ of wind turbines, the wind farm comprising a computational farm module comprising a farm controller and a farm estimator, the computational farm module communicatively coupled to a plurality’ of computational turbine modules associated with the plurality of wind turbines, the plurality of computational turbine modules each comprising a turbine controller and a turbine estimator, each of the turbine estimators configured to implement one or more models of wind turbine dynamics and local aerodynamics, the method comprising: receiving sensor information from one or more measurement systems of respective wind turbines of the plurality of wind turbines indicative of operation of the respective wind turbines; determining, via the turbine estimators, estimates of one or more local wind parameters and a turbine state of each of the plurality7of w ind turbines based on the sensor information; determining, via the farm estimator, estimates of one or more farm wind parameters based on, at least, the estimates of the one or more local wind parameters and the turbine state of each of the plurality’ of wind turbines; estimating, via the farm estimator, a wind farm resource based on the estimates of the one or more farm wind parameters; determining, via the farm controller, one or more setpoints for each of the plurality of w ind turbines based on the wind farm resource; and controlling, via the plurality7of turbine controllers, the plurality7of w ind turbines based on the one or more setpoints.

2. The method of claim 1. wherein the sensor information comprises at least one of generator speed, rotor speed, tower top acceleration, rotor imbalance proximity7sensor data, blade moments, blade twist, pitch angle, torque output, or power output.

3. The method of claim 1. wherein at least one of the estimates of the one or more local wind parameters or the estimates of one or more farm wind parameters comprise any one or more of wind speed, w ind direction, vertical and horizontal misalignment, induced velocities, wake strength, wind shear, wind direction, windveer, wind turbulence, or one or more wake parameters.

4. The method of claim 1. further comprising determining, via the one or more models, the estimates of one or more local wind parameters and the turbine state of each of the plurality of wind turbines based on the sensor information and one or more wind measurements from one or more anemometers or wind vanes of the plurality of wind turbines.

5. The method of claim 1. wherein the farm estimator comprises an implementation of a farm model of farm flow dynamics, the farm model comprising one or more wind velocities at a discrete grid of reference points in a layout of the wind farm.

6. The method of claim 5. wherein the farm model comprises at least one of an analytic model, a set of equations of discretized partial differential equations, a data-driven models, or combinations thereof.

7. The method of claim 1, wherein each of the turbine estimators utilizes at least one of a non-linear Kalman filter, or a moving horizon estimation.

8. The method of claim 1. further comprising estimating, via farm estimator, the wind farm resource based on the estimates of the one or more farm wind parameters and additional farm sensor information, the additional farm sensor information comprising at least one of met mast measurements, remote sensor information comprising Lidar, Radar, or Sodar sensor data, or wind and weather forecast data.

9. The method of claim 1, further comprising estimating, via farm estimator, the wind farm resource based on the estimates of the one or more farm wind parameters and an additional hierarchical level to capture inter-farm effects, the additional hierarchical level comprising information relating to an atmospheric boundary layer.

10. The method of claim 1, further comprising exchanging information between the farm controller and the plurality of turbine controllers to improve convergence of magnitudes estimated by the farm estimator and the turbine estimators, the magnitudes comprising at least one of wind speed, wind shear, wind direction, or wind veer.

11. A wind farm, comprising:a computational farm module comprising a farm controller and a farm estimator; a lurality of wind turbines; and a plurality of computational turbine modules associated w ith the plurality of wind turbines, the plurality' of computational turbine modules each comprising a turbine controller and a turbine estimator, each of the turbine estimators configured to implement one or more models of wind turbine dynamics and local aerodynamics, the computational farm module communicatively coupled to the plurality of computational turbine modules, wherein the farm controller and the plurality of computational turbine modules are configured to communicate together to perform a plurality of operations, the plurality of operations comprising: receiving sensor information from one or more measurement systems of respective wind turbines of the plurality of wind turbines indicative of operation of the respective wind turbines; determining, via the turbine estimators, estimates of one or more local wind parameters and a turbine state of each of the plurality of w ind turbines based on the sensor information; determining, via the farm estimator, estimates of one or more farm wind parameters based on, at least, the estimates of the one or more local wind parameters and the turbine state of each of the plurality of w ind turbines; estimating, via the farm estimator, a wind farm resource based on the estimates of the one or more farm wind parameters; determining, via the farm controller, one or more setpoints for each of the plurality of wind turbines based on the w ind farm resource; and controlling, via the plurality of turbine controllers, the plurality of wind turbines based on the one or more setpoints.

12. The wind farm of claim 11. wherein the sensor information comprises at least one of generator speed, rotor speed, tower top acceleration, rotor imbalance proximity sensor data, blade moments, blade twist, pitch angle, torque output, or powder output.

13. The wind farm of claim 11, wherein at least one of the estimates of theone or more local wind parameters or the estimates of one or more farm wind parameters comprise any one or more of wind speed, wind direction, vertical and horizontal misalignment, induced velocities, wake strength, wind shear, wind direction, wind veer, wind turbulence, or one or more w ake parameters.

14. The wind farm of claim 11, w herein the plurality of operations further comprise determining, via the one or more models, the estimates of one or more local wind parameters and the turbine state of each of the plurality of wind turbines based on the sensor information and one or more wind measurements from one or more anemometers or wind vanes of the plurality of wind turbines.

15. The wind farm of claim 11, wherein the farm estimator comprises an implementation of a farm model of farm flow dynamics, the farm model comprising one or more wind velocities at a discrete grid of reference points in a layout of the wind farm.

16. The wind farm of claim 15, wherein the farm model comprises at least one of an analytic model, a set of equations of discretized partial differential equations, a data-driven models, or combinations thereof.

17. The wind farm of claim 11, wherein each of the turbine estimators utilizes at least one of a non-linear Kalman filter, or a moving horizon estimation.

18. The wind farm of claim 11. wherein the plurality of operations further comprise estimating, via farm estimator, the wind farm resource based on the estimates of the one or more farm wind parameters and additional farm sensor information, the additional farm sensor information comprising at least one of met mast measurements, remote sensor information comprising Lidar, Radar, or Sodar sensor data, or wind and weather forecast data.

19. The wind farm of claim 11. wherein the plurality of operations further comprise estimating, via farm estimator, the wind farm resource based on the estimates of the one or more farm wind parameters and an additional hierarchical level to capture inter-farm effects, the additional hierarchical level comprising information relating to an atmospheric boundary layer.

20. The wind farm of claim 11, wherein the plurality’ of operations further comprise exchanging information between the farm controller and the plurality of turbine controllers to improve convergence of magnitudes estimated by the farmestimator and the turbine estimators, the magnitudes comprising at least one of wind speed, wind shear, wind direction, or wind veer.

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