Method of operating a wind turbine

Abstract

A method for operating a wind turbine (100) using a controller (10) is provided. The controller (10) is configured to activate or deactivate each of two or more different control features of the wind turbine (100), each control feature changing an operating characteristic of the wind turbine (100) and having an impact on at least one of a lifespan and a power generation of the wind turbine (100). The method includes determining a type of optimization parameter and an optimization target for the optimization parameter, wherein the optimization parameter is related to at least one of the lifespan or the power generation of the wind turbine (100). The method further performs one or more optimization steps, wherein each optimization step is performed for a different combination of activation states of the two or more control features. Based on the one or more optimization steps, an optimal combination of activation states of the two or more control features is determined for which the estimated optimization parameter achieves the optimization target.

Description

Technical Field

[0001] The present invention relates to a method of operating a wind turbine using a controller, wherein the controller can activate or deactivate different control features of the wind turbine. The invention also relates to a corresponding controller and a computer program. Background Art

[0002] The use of wind energy is surging. Wind turbines are installed in diverse locations around the world and are therefore exposed to diverse environmental conditions. Wind turbines must withstand considerable wind forces acting on their rotors, nacelles, and towers. Over their lifetime, wind turbine structural components are exposed to numerous load cycles, which can ultimately lead to component failure. Structural failure of such wind turbine components is typically assessed through physical inspection of these components. Furthermore, fatigue assessment of wind turbines based on aeroelastic models is known.

[0003] Modern wind turbines are also equipped with a variety of control features that can improve wind turbine operation. One example of such a control feature is the High Wind Ride-Through (HWRT) control feature, according to which the rotor speed and power output of the wind turbine are reduced as wind speed increases in order to prevent the wind turbine from shutting down. This is typically performed when the wind speed exceeds a certain limit for a certain period of time. Another exemplary control function is the Power Boost (PB) function, which can increase the power generated by a wind turbine by temporarily increasing the wind turbine's power limit under certain conditions. Another control feature is, for example, an Adaptive Control System (ACS), which performs load management in real time based on measured data and, in particular, reduces the wind turbine's power output if increased turbulence is measured at the wind turbine.

[0004] It is desirable to operate such wind turbines in a manner that effectively achieves the operator's desired operational characteristics. For example, an operator may wish to operate a wind turbine to maximize its lifespan. A different operator may wish to operate a wind turbine to maximize power generation. Operators are often faced with the problem that varying environmental conditions, different types of wind turbines and their varying characteristics, and the varying available control schemes do not allow the operator to easily establish an operating scheme for the wind turbine that results in the desired outcome.

[0005] For example, as known from document US 2013 / 0161949 A1, it is known to take into account fatigue loads for operating wind turbines. The operating ranges of the different wind turbines are adjusted so that each wind turbine operates within a range where fatigue loads do not reach a maximum. The average lifespan of the wind turbines can thus be increased.

[0006] Document WO 2019 / 214785 A1 describes a method in which a model is used to obtain simulated fatigue values ​​for a wind turbine. The simulated fatigue values ​​are compared with expected fatigue values ​​to identify opportunities to modify the wind turbine's control strategy. However, it does not describe how the control strategy needs to be modified to achieve the desired results. Summary of the Invention

[0007] There is a need to improve the operation of wind turbines, particularly to operate the turbines in such a way that an operational goal, such as maximum life or maximum energy production, is achieved through the operation.

[0008] This need is met by the features of the independent claim.The dependent claims describe embodiments of the invention.

[0009] In one embodiment of the present invention, a method for operating a wind turbine using a controller is provided. The controller is configured to activate or deactivate each of two or more control features of the wind turbine. Each control feature changes the operating characteristics of the wind turbine and has an impact on the life and power generation of the wind turbine. According to the method, a type of optimization parameter and an optimization target for the optimization parameter are determined. The optimization parameter is related to at least one of the life or power generation of the wind turbine. One or more optimization steps (e.g., 1, 2, 3 or more) are performed, wherein each optimization step is performed for a different combination of activation states of two or more control features. Each optimization step includes selecting a combination of activation states of the two or more control features; estimating an optimization parameter based on an estimate of the remaining life and / or power generation of the wind turbine (e.g., per year or over the remaining life), wherein the estimate takes into account the impact of the control features activated in the optimization step; and determining whether to perform a further optimization step based on the estimated optimization parameter. The method further includes, based on the one or more optimization steps, determining a combination of activation states of the two or more control features for which the estimated optimization parameters best meet (i.e., achieve) an optimization goal, e.g., are above a target threshold or are closest to the optimization goal. This determined combination of activation states is considered an optimal combination of activation states.

[0010] This method allows for an efficient control strategy to be derived for the selected optimization parameters. In particular, such a control strategy can be automatically derived by the controller for the optimal combination of activation states. The operator can simply select the type of optimization parameter (and the optimization target, if not already implied by the selected optimization parameters), and the controller can efficiently and automatically select a control strategy and execute the corresponding control of the wind turbine in response. It should be clear that the optimization parameters can also be based on a combination of lifetime and energy production. Thus, this method estimates the optimization parameters while taking into account the impact of activated / deactivated control features (i.e., combinations of activation states), resulting in an accurate estimation of the wind turbine's performance and failure risk. Consequently, the impact of all possible control features can be evaluated simultaneously and automatically.

[0011] Thus far, optimization of lifetime and energy production has been discussed. A third optimization objective, which can be used instead of or in addition to lifetime and / or energy production, is the satisfaction of electricity demand from the grid to which the wind turbine is connected. As described, the controller can be configured to activate or deactivate each of two or more control features of the wind turbine. Each control feature modifies the operating characteristics of the wind turbine and has an impact on the lifetime and energy production of the wind turbine, as well as the extent to which expected future electricity demand will be met. For example, if electricity demand is expected to be high on certain days of the week, the optimization process can increase production on those days and reduce production on other days to ensure that electricity demand is met, for example, to avoid overproduction. Each optimization step can include selecting a combination of activation states for the two or more control features; estimating optimization parameters based on an estimate of the remaining lifetime and / or energy production of the wind turbine and / or an estimate of future electricity demand; and estimating wind turbine operation accordingly to meet that demand. This estimation takes into account the impact of the control features activated in the optimization step. Based on the estimated optimization parameters, a determination is made as to whether further optimization steps should be performed. The method further includes, based on the one or more optimization steps, determining a combination of activation states of the two or more control features for which the estimated optimization parameters best meet (i.e., achieve) an optimization goal, e.g., are above a target threshold or are closest to the optimization goal. This determined combination of activation states is considered an optimal combination of activation states.

[0012] Electricity demand fulfillment—that is, whether wind turbines can meet the grid operator's demands—can ensure that the grid will operate in a stable manner. This, in turn, ensures that wind turbines do not enter unstable operating modes, such as low voltage ride-through. As a side effect, wind turbine revenue can be optimized, as grid operators typically offer different energy prices based on expected demand and supply on the grid.

[0013] For example, if the optimization parameter is related to the remaining life of the wind turbine (or the optimization parameter is the remaining life of the wind turbine), estimating the optimization parameter includes at least estimating the remaining life (or is performed by at least estimating the remaining life), and if the optimization parameter is related to the power generation of the wind turbine or the satisfaction of power demand from the power grid to which the wind turbine is connected (or the optimization parameter is the power generation of the wind turbine or the satisfaction of power demand from the power grid to which the wind turbine is connected), estimating the optimization parameter includes at least estimating the power generation of the wind turbine (or is performed by at least estimating the power generation of the wind turbine). It can of course include estimating both the remaining life and the power generation of the wind turbine.

[0014] If further optimization steps are to be performed, the method may select a different combination of activation states for the available control features and re-estimate the optimization parameters using this combination. If the estimated optimization parameters meet the objectives, such as exceeding a threshold for lifetime / energy generation increase, or if a stopping condition of a search or optimization algorithm implemented by two or more optimization steps is reached, such as after the optimization parameters have been estimated for every possible combination of activation states, it may be determined that no further optimization steps are to be performed, thereby determining the combination of optimization parameters that results in the best satisfaction of the objectives.

[0015] It should be clear that the controller may implement a control unit that performs control of the wind turbine and a strategy optimization unit / optimizer that determines the corresponding optimal combination of control strategies, in particular activation states. These units may be implemented in the same physical unit (e.g., a wind turbine controller) or in physically separate units.

[0016] In one embodiment, determining the type of optimization parameter includes receiving user input for selecting the type of optimization parameter from at least two types of possible optimization parameters. The type of optimization parameter can, for example, be selected from at least two types of possible optimization parameters, including energy production (annual or residual) and remaining lifetime. Other types of optimization parameters are of course also contemplated, such as electricity demand fulfillment of the power demand of the grid to which the wind turbine is connected, or "useful energy production," which relates to a combination of lifetime and energy production. When using useful energy production as the optimization parameter, external parameters such as power demand on the grid can also be considered. For example, when power demand is high (e.g., above a predetermined threshold), energy production of the wind turbine can be maximized, while when power demand is low (e.g., below the threshold), lifetime can be maximized. It should be appreciated that the method can be repeatedly executed during operation of the wind turbine, allowing the control strategy to be adjusted during operation, for example, when external parameters change, such as when power demand increases or decreases. It should be appreciated that, also in this case, the type of optimization parameter (useful energy production) only needs to be selected once, and the method thereafter automatically derives the optimal control strategy, further considering the external parameters.

[0017] For these exemplary optimization parameters, the optimization objective is typically to maximize or achieve the optimized parameter above a threshold, while for other optimization parameters, it may be to minimize or achieve the optimized parameter below a threshold. As an example, one type of optimization parameter may be residual life, and the optimization objective may be maximizing the residual life or increasing the residual life / life above a threshold. As another example, the optimization parameter may be fatigue load, and the optimization objective may be minimizing it. In response, the controller selects the combination of control features that results in the minimum estimated fatigue load. The controller automatically determines and selects the feature combination that achieves the optimization objective.

[0018] The optimization objective can be associated with an optimization parameter of a selectable type (e.g., the parameter energy production can be associated with the objective "maximize"), it can be determined by the controller based on the selected type of optimization parameter (e.g., based on thresholds of predetermined expected changes in lifetime and / or energy production associated with available control features), or it can be determined by user input.

[0019] In each optimization step, the combination of activation states of two or more control features may be selected based on a predetermined effect of the two or more control features on the lifespan and / or energy production of the wind turbine. Such predefined effects may be associated with the available control features and may be stored or accessible by the controller, for example, in a memory or database within the controller. Such predefined effects may be based on empirical data for the respective control features and may not correspond to the actual effect that control feature has in combination with other control features. The actual effect may instead be determined during the estimation of optimization parameters, taking into account the activation states of other control features. For example, when maximizing lifespan, the selected combination may include activation of all features that have a positive effect on lifespan. If such a combination does not achieve the desired result, the combination of activation states may be modified in the next step, for example by subsequently deactivating features that have only a limited effect on lifespan.

[0020] Determining whether to perform further optimization steps can include evaluating one or more boundary conditions (or stopping criteria), particularly for operating parameters other than the optimization parameters. Evaluating the boundary conditions preferably includes determining whether the change in the lifetime and / or energy production of the wind turbine resulting from activating the control features according to the corresponding combination of activation states exceeds a corresponding threshold. For example, a threshold may be exceeded if the reduction in lifetime is too high or if the reduction in energy production is too high. A boundary condition can also be evaluated by determining whether the total remaining lifetime exceeds a predetermined time period threshold (e.g., if the total remaining lifetime decreases below the threshold, the boundary condition may not be met). Such a fixed time period threshold for the remaining lifetime can be, for example, a minimum absolute value of the remaining lifetime. If such a threshold for the boundary condition is exceeded, further optimization steps with different feature combinations are performed. This has the advantage of ensuring that the resulting combination of activation states not only achieves the optimization goal but also has an acceptable impact on the remaining operating parameters. The thresholds for the boundary conditions can be predefined (e.g., a maximum annual energy production reduction of 10%) or can be provided by user input. If one or more boundary conditions are satisfied when the optimization goal is achieved, the resulting combination of activation states is considered the optimal combination. A combination of activation states that does not satisfy one or more boundary conditions may not be considered an optimal combination, but rather such a combination may be excluded. If no combination is found that satisfies the optimization objective and boundary conditions, the optimization objective may be adjusted and / or the boundary conditions may be relaxed.

[0021] Performing one or more optimization steps can include executing a search algorithm or optimization algorithm to find a combination of activation states that results in optimization parameters that best meet the optimization objective, wherein the combination of activation states found by the algorithm is selected as the optimal combination of activation states. This search algorithm can, for example, subsequently activate other control features that influence the optimization parameters and stop the search if a stopping criterion is met, such as if the estimated optimization parameters exceed a threshold set by the optimization objective. As described above, boundary conditions can be evaluated, and if they are not met, the search continues until a result that meets these conditions is found. Using this algorithm, the optimal combination of activation states can be found quickly and efficiently, particularly because not all possible combinations need to be evaluated.

[0022] If the optimal combination of activation states that satisfies the boundary conditions is not found, the operator may be asked to adjust the optimization target or the boundary conditions.

[0023] In one embodiment, an optimization step can be performed for each possible combination of activation states of two or more control features. The combination of activation states for which each optimization step results in optimized parameters that best meet (or satisfy) the optimization objective and that satisfy any boundary conditions can then be selected as the optimal combination of activation states. Using this approach, it is possible to ensure that the effects of all possible combinations of control features are considered in the evaluation, and to obtain a control strategy that results in optimized parameters that best meet the optimization objective.

[0024] During operation of the wind turbine, the optimization step and determination of the optimal combination of activation states can be repeated. The operation of the wind turbine can then be adjusted based on the respectively determined optimal combination of activation states of two or more control features. In this way, it can be ensured that as the conditions used for evaluation change, which can be determined via feedback from the wind turbine's condition monitoring system, the operating strategy can be updated to operate the wind turbine so that it achieves the best possible optimized parameters.

[0025] The method can be performed on a wind turbine-by-wind turbine basis, i.e., for each wind turbine in a wind farm. Thus, each wind turbine in the wind farm can be operated to achieve the best possible optimization parameters, taking into account the optimization objectives. It should be appreciated that different types of optimization parameters can be set for different wind turbines, i.e., one wind turbine can be operated to maximize its lifespan, while another wind turbine can be operated to maximize its energy production.

[0026] In one embodiment, the two or more control features include at least two or all control features selected from the group consisting of or comprising: a high wind ride-through (HWRT) control feature that performs a load-based reduction in wind turbine output power under predetermined wind conditions (e.g., at a specific wind speed within a specific time period); an adaptive control system (ACS) control feature that reduces wind turbine output power if turbulence is determined at the wind turbine to be above a threshold, wherein the turbulence may be determined, for example, based on a measured wind speed and a load model; a power boost (PB ) control feature, which increases the power output of the wind turbine by increasing the power limit of the wind turbine under predetermined wind conditions (for example, it may increase the rotational speed of the rotor in proportion to the increase in output power); a Power Curve Update Kit (PCUK) control feature, which modifies the control functions of the controller, in particular the pitch angle control, based on hardware modifications installed on the wind turbine, in particular hardware modifications installed on the rotor blades; and a Peak Shaving feature, which changes the operating curve used by the controller to operate the wind turbine, which operating curve determines the wind turbine settings, in particular the rotor speed and pitch, based on the wind speed.

[0027] It should be understood that embodiments are not limited to the control features described above, but rather that additional or different control features available for respective wind turbines may be used with embodiments of the present invention. Furthermore, as new control features become available, these control features may be used to estimate an optimal combination of control features, as described herein.

[0028] These different control features are used in different wind turbines and have an impact on the remaining life of the wind turbine (e.g., in some cases by reducing loads on structural components of the wind turbine) and power generation (e.g., in some cases by increasing or decreasing power generation). For example, a wind turbine may include at least HWRT and ACS control features, or HWRT and PB control features, or at least HWRT, ACS, and PB control features. Activation of a control feature may correspond to a specific operating mode of the wind turbine, i.e., a control feature may change how the wind turbine operates in a specific situation, which may depend on, for example, current wind conditions. Different activated control features may therefore correspond to different operating modes of the wind turbine.

[0029] Estimating the optimization parameters may include at least estimating the remaining lifetime, wherein estimating the remaining lifetime includes estimating a failure rate for the wind turbine based on statistical data of the wind turbine and / or a fleet of wind turbines (particularly wind turbines corresponding to a model of an actual wind turbine) and performing a fatigue assessment of the wind turbine based on measurements taken by sensors of the wind turbine and / or on-site measurements (i.e., measurements taken at the wind turbine's site, such as those related to weather conditions, wind speed, etc.). When determining the remaining lifetime, combining the statistical failure rate with the fatigue assessment allows for an improved and more accurate determination that takes into account both the individual wind turbine and knowledge gained from the entire population of corresponding wind turbine models (via the statistical failure rate). The statistical data may, for example, include data from fleets of wind turbines of the same model as the wind turbine under consideration or data from the same wind turbine components to be assessed. In this way, information about the general behavior and failures of wind turbine models or components can be obtained and taken into account in the remaining lifetime estimation. By combining statistics with fatigue assessment, for example by weighting the lifetime estimates obtained from both methods, the remaining lifetime is derived.

[0030] Performing a fatigue assessment may include providing an aeroelastic model of the wind turbine and estimating fatigue loads on components of the wind turbine based on the aeroelastic model and wind turbine data received from measurements. Additionally or alternatively, a control model may be employed in the fatigue assessment, which may model the operating curve of the wind turbine. This approach allows for an efficient assessment of the current state of fatigue structural damage in wind turbine components. For example, a BHawC model of the wind turbine may be employed.

[0031] In other embodiments, such a model may not be employed and, for example, weather conditions and turbine data, in particular measurements made by corresponding sensors on the wind turbine, may be used for the basic fatigue assessment.

[0032] Performing the fatigue assessment may include adjusting the aeroelasticity and / or control model of the wind turbine based on the control features activated according to the combination of activation states associated with the respective optimization steps. Thus, when the activated control feature(s) change how the wind turbine operates under different environmental conditions, such as high winds, etc., accounting for the effects of these changes within the framework of the wind turbine model may provide an efficient and accurate estimate of the remaining life and / or energy production of the wind turbine for the respective combination of activation states.

[0033] In a first optimization step, a combination of activation states may be selected based on a predetermined impact of two or more control features on the remaining lifetime and / or energy production of the wind turbine. The optimization parameters are preferably estimated by model-based lifetime and / or energy production estimation, taking into account the control features activated according to the selected combination of activation states, wherein subsequent optimization steps select different combinations of activation states based on the predetermined impact. A different combination of activation states corresponds to at least one of: (a) activating an additional control feature, (b) deactivating one of the control features activated in a previous optimization step, or (c) replacing an activated control feature with a different activated control feature. For example, when maximizing lifetime, two control features associated with the highest predetermined lifetime increase may be selected in the first optimization step, and if this combination does not result in the estimated optimization parameters meeting the target, one of the control features may be replaced by the control feature associated with the next highest lifetime increase, or such a control feature may be activated in addition.

[0034] It should be clear that some control features may interact or may overlap to some extent. The aeroelastic / control model of the wind turbine used in the estimation takes this interaction into account.

[0035] Estimating the remaining life of a wind turbine may include estimating the remaining life of certain predetermined structural or mechanical components of the wind turbine. The lowest estimated remaining life of such structural or mechanical components may then be used to determine the remaining life of the wind turbine. Specifically, load-bearing components of the wind turbine may be considered when estimating the remaining life. At least the rotor blades, hub, tower, and nacelle may be considered. Rotor bearings, blade bearings, blade pitch drives, yaw drives, generators, gearboxes, and other components of the wind turbine may also be considered.

[0036] In some embodiments, the estimated remaining life for a wind turbine may be a remaining useful life (RUL), which is the time until the structural reserves of the wind turbine are consumed while maintaining a target safety level when operating the wind turbine.

[0037] The method may further include activating control features of the wind turbine according to the determined optimal combination of activation states, and operating the wind turbine using the activated control features. Thus, the wind turbine may be operated in a manner that achieves an optimization goal, such as maximizing remaining life, maximizing energy production, or achieving another optimization goal.

[0038] The "combination of activation states" describes the activation states of the various control features considered by the method, and therefore in the corresponding optimization steps. For example, if three control features A, B, and C are available, a combination of activation states might be one where control features A and B are "on," while control feature C is "off"; a different combination might be one where control features A and C are on, while control feature B is off. Thus, the activation states can be changed for each optimization step to evaluate different combinations of optimization parameters. Thus, the combination of activation states indicates which available control features are turned on and which are turned off.

[0039] According to another embodiment of the present invention, a controller for controlling the operation of a wind turbine is provided. The controller is configured to activate or deactivate each of two or more different control features of the wind turbine, each of which changes the operating characteristics of the wind turbine and affects the lifespan and power generation of the wind turbine. The controller includes a data processor and a memory coupled to the data processor. The memory stores control instructions that, when executed by the data processor, perform any of the methods described above or further below. The controller can be specifically configured to perform any of the above-described method steps and can accordingly have an interface for interacting with any of the components described herein. For example, it can be configured to receive data from sensors disclosed herein, or can transmit corresponding control commands for operating the wind turbine based on the optimal combination of activation states of the control features determined. With such a controller, advantages similar to those outlined further above with respect to the method can be achieved.

[0040] In one embodiment, the controller is implemented by a wind turbine controller of the wind turbine. The wind turbine itself can therefore determine the corresponding control strategy as described above. In other embodiments, the controller can be a wind farm controller coupled to the wind turbine, or can be implemented by a combination of a wind turbine controller and a wind farm controller. Portions of the method can then be performed by each of these controllers. For example, the wind turbine controller can collect data from the turbines and transmit it to the wind farm controller. A user can select the type of optimization parameters at the wind farm controller. The wind farm controller can determine the optimal combination of activation states and transmit it to the wind turbine controller. The wind turbine controller can then activate the corresponding control feature based on the combination of the determined and received activation states.

[0041] According to another embodiment of the invention, a wind turbine or wind farm comprising such a controller is provided.

[0042] Another embodiment of the present invention provides a computer program for controlling the operation of a wind turbine. The computer program includes control instructions that, when executed by a data processor of a controller controlling the wind turbine, cause the data processor to perform any of the methods disclosed herein. Similarly, similar advantages to those outlined further above can be achieved with such a computer program. A data carrier including such control instructions is also provided.

[0043] It should be understood that the above features and those to be explained below may be used not only in the respective combinations shown, but also in other combinations or alone, without departing from the scope of the present invention. In particular, the features of the different aspects and embodiments of the present invention may be combined with each other unless otherwise indicated. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The foregoing and other features and advantages of the present invention will become more apparent from the following detailed description read in conjunction with the accompanying drawings, in which like reference numerals denote like elements.

[0045] Figure 1 is a schematic diagram illustrating a wind turbine including a controller according to an embodiment of the present invention.

[0046] Figure 2 is a schematic diagram illustrating functional components of a controller according to an embodiment of the present invention.

[0047] Figure 3 is a schematic diagram illustrating functional components of a controller according to an embodiment of the present invention.

[0048] Figure 4 is a schematic diagram illustrating a controller and controlled components according to an embodiment of the present invention.

[0049] Figure 5 is a flow chart illustrating a method of operating a wind turbine according to an embodiment of the present invention.

[0050] Figure 6 is a flow chart illustrating a method of determining an optimal set of activation states for wind turbine control features according to one embodiment of the present invention. DETAILED DESCRIPTION

[0051] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the following description of the embodiments is provided for illustrative purposes only and is not provided in a restrictive sense. It should be noted that the accompanying drawings are only regarded as schematic representations, and the elements in the accompanying drawings are not necessarily proportional to each other. Instead, the representations of the various elements are selected so that their functions and general purposes become apparent to those skilled in the art. As used herein, the singular forms "a", "an" and "the" are intended to also include plural forms, unless the context clearly indicates otherwise. Unless otherwise stated, the terms "comprise", "have", "include" and "contain" should be interpreted as open terms (i.e., meaning "including, but not limited to,").

[0052] Figure 1 A wind turbine 100 is schematically shown comprising a rotor 101 having rotor blades 102. A nacelle 103 of the wind turbine 100 is mounted to a wind turbine tower 104 and can be rotated by a yaw drive (not shown). The wind turbine 100 further comprises a gearbox 105, a generator 106 and a converter 107. In operation, wind energy is converted by the blades 102 of the rotor into rotational mechanical energy, wherein the rotation of the rotor 101 turns the generator 106 which converts the mechanical energy into electrical energy. With the aid of the converter 107, the electrical energy can be converted to a desired AC frequency, thereby allowing the wind turbine 100 to operate at variable speed. It should be clear that the wind turbine 100 may have different Figure 1 10. The wind turbine 100 may be configured in a manner similar to that shown. For example, it may be a direct drive turbine that does not include the gearbox 105. It may also employ a full converter solution, or it may employ a doubly fed induction generator (DFIG) with the converter 107 coupled to the rotor of such a generator. The wind turbine 100 may also include a transformer (not shown), which may be located, for example, at the base of the tower 104.

[0053] Furthermore, a controller 10 is provided, which may be fully or partially implemented as a wind turbine controller, such as Figure 1 100 . This wind turbine controller controls components of wind turbine 100 . It can, for example, control the yaw angle of nacelle 103 and the pitch angle of blades 102 . It can also control electrical components, such as converter 107 and / or generator 106 . Controller 100 can further be configured to shut down wind turbine 100 under certain conditions and to start wind turbine 100 .

[0054] The controller 10 can implement one or a combination of the following control features. The controller 10 can, for example, implement a high wind ride-through (HWRT) control feature. If the average wind speed exceeds 25 m / s over a ten-minute time interval, a conventional wind turbine can be programmed to shut down. When the HWRT control feature is activated, the wind turbine does not shut down under such predetermined conditions, but above a certain wind speed, it adopts a load-based reduction in output power. For example, if the wind speed exceeds 23 m / s for a certain period of time, the rotational speed of the rotor and the power output of the wind turbine are gradually reduced. For example, the pitch angle of the rotor blades 102 can be modified so that less wind energy is converted. Thus, the HWRT performs a load-based reduction in the output power of the wind turbine under predetermined wind conditions.

[0055] Another control feature implemented by controller 10 may be an adaptive control system (ACS) control feature. If relatively high turbulence is present in the air impacting a wind turbine, the wind power system may experience overload and excessive material fatigue. Conventional control systems can shut down the wind turbine to prevent such an overload condition. By implementing the ACS control feature, turbulence is detected at the wind turbine. If turbulence above a certain threshold is detected, the controller reduces the wind turbine's output power. This reduces fatigue loads on the wind turbine. If the turbulence in the airflow decreases again, the output power can be increased back to its previous value. Controller 10 may, for example, implement an ACS controller that uses a load model to determine the occurrence of turbulence that could potentially cause a wind turbine overload. Therefore, if turbulence above this threshold is detected at a wind turbine, the ACS control feature effectively prevents the wind turbine from shutting down in this situation by reducing the wind turbine's output power. The ACS control feature can therefore be based on the measured wind speed and the corresponding load model. The ACS functionality is particularly useful in wind farms, where densely packed wind turbines may increase turbulence in the airflow.

[0056] Another control feature that controller 10 can implement is the power boost (PB) control feature. The power boost control feature increases the wind turbine's power generation by increasing the power limit under predetermined conditions. For example, a wind turbine may be operating at its output power limit, but this operation may occur below the load limit of the wind turbine's structural loads. In this case, the PB feature increases the rotor speed in proportion to the increase in output power. By doing so, net power generation can be increased by up to 5%.

[0057] Another control feature that can be implemented by controller 10 is a turbine load control (TLC) control feature. Controller 10 can implement a turbine load control system that continuously monitors the structural loads on the wind turbine. If the measured loads exceed normal operating values, the controller can automatically adjust the operation of the wind turbine to bring the loads back within the design envelope. For example, it can reduce the rotor's rotational speed by correspondingly pitching the rotor blades.

[0058] Another control feature that can be implemented by the controller 10 is the Power Curve Upgrade Kit (PCUK) control feature. After deployment, hardware upgrades can be installed on the wind turbine, such as aerodynamic modifications to the blades, such as flaps installed to the trailing edge of the blade root to increase lift, or flaps with serrated edges installed to the trailing edge of the blade tip to increase lift by extending the blade chord. The PCUK control feature modifies the wind turbine control to account for these hardware modifications and, for example, enhances power generation by adjusting pitch control.

[0059] It should be appreciated that wind turbine 100 and controller 10 may implement further control features that can be used to adapt the operation of wind turbine 100 to prevailing conditions. Such control features can be used to increase the power generation of the wind turbine, reduce fatigue loads on wind turbine components, implement safety measures, and the like. Within the meaning of the present disclosure, a control feature is a control feature that affects the lifespan and / or power generation of wind turbine 100 and is a dedicated feature that can be activated or deactivated by controller 10. Controller 10 can accordingly control wind turbine 100 to operate with one or more of the aforementioned control features activated or deactivated. Thus, wind turbine 100 typically operates with a specific combination of these control features activated. It should be appreciated that wind turbine 100 may not implement all of the aforementioned control features, but may only implement some of them. Controller 10 and wind turbine 100 may implement any combination of two or more of the aforementioned control features. Influencing the lifetime or energy production of the wind turbine means that activation of the respective control feature changes the remaining lifetime of the wind turbine and / or changes the energy production (annual energy production or energy production during the remaining lifetime).

[0060] Traditionally, the overall impact of activating or deactivating such control features on the wind turbine's performance and risk of failure has not been considered. While the wind turbine's structural characteristics can be assessed through physical inspection, determining whether a specific control feature is activated or deactivated, the wind turbine's overall actual risk of failure, particularly the impact of combinations of control features, has not been considered. Furthermore, in traditional systems, this process is a manual one.

[0061] Controller 10 is configured to determine the optimal combination of activation states of available control features that achieves the optimization objective. Specifically, a user need only specify the quantity they wish to optimize (the type of optimization parameter), and controller 10 automatically determines the control parameters, and in particular, the activation states of the control features, to achieve the desired optimization. If the user selects, for example, the remaining lifetime of the wind turbine as the type of optimization parameter, whose maximization is the associated optimization objective, controller 10 automatically determines the combination of activation states of the control features that maximizes the lifetime of the wind turbine. Similarly, if the type of optimization parameter is energy production, and the optimization objective is maximization of energy production, controller 10 determines the combination of activation states that provides the maximum energy output from wind turbine 10 and controls wind turbine 10 accordingly, in particular by activating / deactivating the corresponding control features according to the determined combination.

[0062] Figure 2 is a functional diagram of controller 10, illustrating the different functions implemented by controller 10 and the different data used by controller 10. Controller 10 embodies a wind turbine management unit 15, which determines the optimal combination of activation states for control features and, therefore, implements an optimal control strategy 80. To determine the remaining life of a wind turbine, controller 10 may utilize fatigue assessments provided by fatigue assessment unit 20 and statistical data 24 regarding failure rates of the respective wind turbine. The failure rates included in statistical data 24 may be derived, for example, from corresponding statistical data for the entire wind turbine fleet from the same model, or for the same components of the respective wind turbine. The corresponding failure rates may be derived using a statistical model. Statistical data 24 may be provided in a memory of controller 10, or controller 10 may obtain statistical data 24 via a data connection (e.g., a network connection to a server) or from a data carrier that an operator may, for example, couple to controller 10. By utilizing statistical data 24, information regarding the general behavior and failures of the respective wind turbine model or components can be processed and used in the remaining life estimation.

[0063] The fatigue assessment (unit 20) employs a model 21, which is in particular an aeroelastic and / or control model. Preferably, at least an aeroelastic model is employed. The wind turbine model 21 may be, for example, a BHawC model. Furthermore, wind turbine data 22 is acquired and used for the fatigue assessment. The wind turbine data 22 may include data from various data sources on the wind turbine or from relevant sensors, such as wind sensors, accelerometers, air density sensors, temperature sensors, and other data related to operation, and in particular data related to loads on the wind turbine 100; it may also include data internal to the wind turbine, such as rotor speed, torque, etc. The wind turbine data 21 includes data from turbine sensors collected by the controller 10 or known to the controller 10. Thus, information about the operation of a particular wind turbine 100 can be acquired and processed.

[0064] Model 21, together with wind turbine data 22, is used to assess the current state of fatigue structural damage in wind turbine components. Specifically, it can assess fatigue loads and the remaining life of various structural components of a wind turbine. For example, for a component made of a particular material type, a certain number of load cycles may be performed during the lifetime of the structural component. The loads on these components are calculated using structural dynamics model 21 of the wind turbine, using the environmental conditions and loads measured in data 22 as input. The estimated remaining life of the structural component can form the basis for determining the remaining life of wind turbine 100. Unit 20 performs the corresponding fatigue assessment.

[0065] Wind turbine management unit 15 estimates the remaining life of wind turbine 100 based on the fatigue assessment performed by unit 20 and statistical data 24. For example, management unit 15 may combine the remaining life estimate based on fatigue assessment 20 and the remaining life estimate based on statistical data 24 for the corresponding type and model of wind turbine by weighting the different values ​​to obtain a total remaining life estimate. The fatigue assessment 20 may be given a higher weight because it reflects the actual state of the wind turbine. As an example, fatigue assessment 20 may contribute 60% to the total remaining life estimate, and statistical data 24 may contribute 40% to the total remaining life estimate, but other ways of combining these estimates are also conceivable. The weights may be selected based on the model used and may be preset by the wind turbine manufacturer. They may also be adjustable based on experience with the corresponding wind turbine model, for example, by the manufacturer or operator.

[0066] Based on the known control curve of the wind turbine and the average wind conditions for one year, the wind turbine management unit 15 further estimates the energy production of the wind turbine in one year (Annual Energy Production, AEP), or the remaining life of the wind turbine.

[0067] A user interface 60 is further coupled to the controller 10. Via the user interface 60, an operator can select a type of optimization parameter according to which the operation of the wind turbine should be optimized. Such optimization parameter can be associated with an optimization target, or the optimization target can be input by the operator via the user interface 60. In addition, the controller 10 can receive additional information in the form of external parameters 70 that are relevant to the operation of the wind turbine and can form a basis for operating the wind turbine. By way of example, such external parameters can include data related to energy demand, such as data from the power grid to which the wind turbine is connected, indicating when power demand is high or low. Exemplary optimization parameters and targets include maximizing power generation (e.g., annual power generation or power generation over the remaining lifetime); maximizing remaining lifetime; minimizing fatigue loads; maximizing power demand satisfaction; and maximizing useful power generation, for example by prioritizing power production when power demand on the grid is high and prioritizing life when power demand is low (this can be determined by corresponding power demand thresholds).

[0068] The wind turbine management unit 15 then performs an optimization method, an example of which will be described below with respect to Figure 6 To further illustrate, the controller 10 is used to determine which control features provided by the controller 10 should be activated in order to best meet the optimization objective, i.e., it determines a combination of activation states of the available control features. Based on the resulting optimal combination of activation states 80, the controller 10 then controls the wind turbine 100. Specifically, based on the determined combination, the controller 10 activates the corresponding control features during operation of the wind turbine 100. The wind turbine includes a condition monitoring system 110, which may include sensors that monitor the condition of corresponding wind turbine components to determine the remaining life. Information from the condition monitoring system 110 can be provided as feedback and used in the turbine data 22, and can be used, in particular, to confirm the expected impact of the corresponding control strategy 80 on the life of the wind turbine. If the expected change in remaining life for operation with the optimal combination of activation states is not achieved, the control strategy 80 can be modified by the unit 15 to take into account the life impact determined by the condition monitoring system 110. It should be clear that effects on wind turbine component fatigue are typically only observable after extended periods of wind turbine operation (months or years), while short-term events are unlikely to affect fatigue assessments.

[0069] Figure 3 It is shown in more detail how the controller 10 determines the optimal control strategy 80 in the form of an optimal combination of activation states of the control features. Figure 5A flowchart illustrating a corresponding method is shown. Via the user interface 60 , the controller 10 receives a user selection of an optimization parameter type associated with a corresponding optimization objective (step 501 ), such as maximizing lifetime, maximizing energy production, or maximizing useful energy delivery (step 502 ). Other optimization objectives are a predetermined change in remaining lifetime or energy production, e.g., a 5%, 10%, or other increase. The controller 10 then obtains statistical data regarding the failure rate of the corresponding wind turbine model (step 503 ), where the statistical data 24 may be stored in a memory of the controller 10 or available via a data link. The controller 10 also obtains turbine data 22 in step 504 and, based on this data, performs a fatigue assessment using the aeroelastic and control model 21 (step 505 ; fatigue assessment unit 20 ). The remaining lifetime estimation unit 25 then estimates the remaining lifetime of the wind turbine based on the statistical data 24 and the fatigue assessment 20 (step 506 ). As described above, the fatigue assessment and the statistical data can be combined, for example, by weighting the determined lifetime estimates.

[0070] Energy estimation unit 30 also estimates the baseline annual energy production (AEP) or remaining lifetime of the wind turbine (step 507). Controller 10 also has available data 41 indicating the impact of available control features on lifetime, as well as data 42 indicating the impact of available control features on energy production. This data may, for example, indicate that control feature A has a predetermined impact on lifetime of +3 years and a predetermined impact on annual energy production (AEP) of -3%. Another control feature, B, may, for example, have a predetermined impact on lifetime of -6 years and a predetermined impact on AEP of +4%. These are predetermined impact values ​​that may not reflect the actual impact of the control features on a single wind turbine, particularly when different control features are combined.

[0071] The strategy optimization unit 50 now performs one or more optimization steps ( Figure 5 The optimization method of step 508 in FIG. 5 determines the best combination of activation states that satisfies the optimization goal for the optimization parameters selected by the user, ie it determines which available control features should be activated in order to achieve the optimization goal.

[0072] An example of this optimization approach is Figure 6is shown in the flowchart of . In step 601, based on data 41 and 42 and the optimization objective, a combination of activation states for available control features is selected based on their predetermined impact on lifespan and performance (energy production). For example, if the user selects lifetime maximization as the optimization parameter and objective, the optimization unit 50 will select activation states for control features that increase wind turbine lifespan, such as Feature A described above, with Feature B set to inactive in the combination of activation states. In steps 602 and 603, the "actual" remaining lifespan and energy production of the wind turbine are estimated as described above, taking into account the control features activated according to the selection in step 601, i.e., using activated Feature A. This can be performed by units 25 and 30 based on model 21 (which is adjusted according to the activated features) and data 22 and 24. As an example, an activated control feature may reduce the loads on structural components in fatigue assessment 20, but may result in a reduction in energy production when estimating wind turbine performance by unit 30. Using the adapted estimation, the optimization parameter, in this example, the total remaining lifespan, is estimated in step 604. Preferably, at least the remaining lifetime and annual energy production or remaining energy production of the wind turbine are estimated in step 604. Likewise, it should be clear that other optimization parameters may be selected, such as maximization of the total power delivery taking into account the remaining lifetime and annual energy production over which power can be delivered.

[0073] In step 605, one or more boundary conditions are evaluated. For example, the estimated remaining lifetime and / or the estimated energy production are compared to corresponding thresholds, e.g., to determine whether a change in the corresponding quantity exceeds the corresponding threshold. As an example, the thresholds may be set such that activation of the control feature should not result in a reduction in AEP of more than 5%. If maximizing AEP is the optimization goal, a suitable threshold may be that activation of the control feature should not result in a reduction in remaining lifetime of more than 5 years. Such thresholds may be preset thresholds, or may be defined by the operator using user interface 60, or by the wind turbine manufacturer.

[0074] In step 606, it is determined whether the optimization goal has been achieved and whether the boundary conditions have been met, i.e., whether the value of the remaining lifetime or the power generation is within the corresponding threshold value. As an example, in addition to the above-mentioned control feature A, another control feature that should generally increase the remaining lifetime of the wind turbine may be available. However, due to the interaction between the two control features, the desired increase in lifetime may not be achieved, and the control feature may partially negate itself. Therefore, for such a combination, the optimization goal may not be achieved. The optimization goal can, for example, be defined as a predefined increase in lifetime or a predefined increase in power generation. Similarly, the combination of activation states determined may result in a change in the remaining lifetime or power generation exceeding the corresponding threshold value, i.e., the remaining lifetime is reduced too much or the power generation is reduced too much (the boundary conditions are not met).

[0075] Therefore, if the optimization goal is not achieved or if the boundary conditions are not met, a different combination of activation states is selected in step 601, wherein this selection can again be based on the available data 41, 42. Otherwise, in step 607, the corresponding combination of activation states determined in step 601 is taken as the optimal combination of activation states. This optimal combination of activation states (optimal strategy 80) is generated by the strategy optimization unit 50 and used by the controller 10 to control the wind turbine 100.

[0076] For example, another control feature, B, has an effect on lifetime of -6 years and an effect on energy production of +4% AEP, and the optimization objective is to maximize energy production. In step 601, the strategy optimization unit 50 may select feature B as active. However, the evaluation in steps 602 to 604 and the comparison in step 605 may determine that the reduction in lifetime is too high, and the method may return to step 601 in step 606 to select a different feature combination, for example, by adding a control feature that improves wind turbine lifetime but has only a slight negative impact on energy production. It should be clear that the interactions between these control features are not known in advance, but are actually estimated by utilizing the model 21 in steps 602 to 604 to determine the optimal control strategy for achieving the optimization objective.

[0077] It should be clear that in Figure 5 The above-described optimization method performed in step 508 is merely an example, and other optimization methods may also be used. As one example, a search algorithm may be used that searches all possible combinations of activation states, or a subset thereof, to find the combination that best satisfies both the optimization objective and the boundary conditions. If the search algorithm encounters a stopping condition, such as reaching a combination that satisfies the optimization objective, the optimization objective is reached in step 606. Another example is that the optimization method performs optimization steps 601 to 605 for all possible combinations of activation states and then selects the activation state combination for which the boundary conditions are met and the optimization parameters best meet the optimization objective. Thus, after all possible combinations of activation states have been tested, the optimization objective is reached in step 606.

[0078] exist Figure 5 In the method of , the estimation steps 506 and 507 and the corresponding preceding steps may be performed as part of the optimization method 508 and may not be performed separately and in advance. Likewise, the units 25 and 30 may form part of the policy optimization unit 50.

[0079] Figure 4The diagram illustrates an exemplary embodiment of a controller 10. Controller 10 includes a processor 11 and memory 12. Processor 11 can be any type of processor, such as a microprocessor, an application-specific integrated circuit, a digital signal processor, or the like. Memory 12 can include volatile and non-volatile memory, particularly RAM, ROM, flash memory, a hard drive, or the like. Controller 10 includes input and output interfaces for receiving data and transmitting control data and control commands to components of the wind turbine. This communication can occur wirelessly or via corresponding lines, such as a control bus. Controller 10 can include a user interface 60 (e.g., including a display and input devices) through which user input from a wind turbine operator can be received. Controller 10 also receives statistical data 24, external parameters 70, and data from a condition monitoring system 110 and other sensors of the wind turbine. Based on a determined control strategy, i.e., the optimal combination of activation states of control features, controller 10 provides control signals to control mechanical components 91 and electrical components 92 of the wind turbine. By way of example, controller 10 can adjust the pitch angle of the wind turbine blades, control the yaw angle, control the braking system, and the like. Electrically, the controller 10 may control the converter 107 of the wind turbine and / or generator 106 to control the torque applied to the rotor 101 and thereby control the rotational speed and mechanical loads.

[0080] It should be understood that the various features and embodiments described herein can be combined and implemented by controller 10. Controller 10 can be implemented in whole or in part by a wind turbine controller or a wind farm controller. When implemented as a wind turbine controller, which is the preferred embodiment, the controller can determine the optimal control strategy for each wind turbine within the wind turbine based on optimization objectives and control the wind turbine accordingly. Alternatively, when implemented as a wind farm controller, the controller can individually evaluate the operating parameters of each of the various wind turbines in the wind farm and then determine the optimal control strategy for each wind turbine in the wind farm. It can then provide the corresponding control parameters, such as torque setpoints, speed setpoints, etc., to the individual wind turbine controllers, or it can instruct the individual wind turbine controllers to activate or deactivate corresponding control features based on the control strategy 80 determined for the respective wind turbine. The operator can then input the optimization parameters / goals for the wind farm, so that all wind turbines can be controlled according to these objectives. It should be clear that in other embodiments, the controller 10 is partially implemented by such a wind turbine controller and such a wind farm controller, and that functionality can be distributed between the controllers, for example by receiving user input at the wind farm controller and by determining the optimal control strategy at the individual wind turbine controllers. Other implementations are of course conceivable.

[0081] Thus, the embodiments disclosed above allow for optimization of wind turbine operation, which is performed automatically and requires only user input of optimization objectives. For example, an operator can specify a maximum life strategy, and the controller automatically selects a combination of control features that maximizes the life of the wind turbine, while also taking into account corresponding boundary conditions for other operating parameters (which differ from the optimization parameters). Similarly, an operator can select a maximum performance target, and the controller can determine a combination of control features that maximizes the wind turbine's energy production. For example, the wind turbine's annual energy production or energy production over its remaining lifetime can be maximized. Furthermore, by incorporating fatigue assessments and statistical data into the lifetime estimation, a very accurate estimate of the remaining lifetime is achieved.

[0082] Although specific embodiments are disclosed herein, various changes and modifications may be made without departing from the scope of the present invention. The present embodiments are to be considered in all respects as illustrative and not restrictive, and all changes coming within the meaning and equivalency range of the appended claims are to be embraced therein.

Claims

1. A method of operating a wind turbine (100) using a controller (10), wherein the controller (10) is configured to activate or deactivate each of two or more different control features of the wind turbine (100), each control feature changing an operating characteristic of the wind turbine (100) and having an effect on at least one of a lifespan and power generation of the wind turbine (100), wherein the method comprises: determining a type of optimization parameter and an optimization target for the optimization parameter, wherein the optimization parameter is related to at least one of: (a) a lifespan of the wind turbine (100), (b) power generation of the wind turbine (100), and (c) power demand satisfaction of a power demand from a power grid to which the wind turbine (100) is connected; performing one or more optimization steps, wherein each optimization step is performed for a different combination of activation states of the two or more control features, wherein each optimization step comprises: selecting a combination of activation states of the two or more control features; estimating the optimization parameters, said estimation comprising at least an estimation of the remaining lifetime if the optimization parameters relate to the lifetime of the wind turbine, and at least an estimation of the energy production of the wind turbine (100) if the optimization parameters relate to the energy production of the wind turbine or the fulfillment of power demand, wherein said estimation takes into account the influence of the control features activated in the optimization step; determining whether to perform further optimization steps based on the estimated optimization parameters; Wherein the method further comprises: Based on the one or more optimization steps, a combination of activation states of two or more control features for which the estimated optimization parameters satisfy the optimization target is determined as an optimal combination of activation states, the optimal combination of activation states is automatically determined by a controller, and the control features of the wind turbine (100) are activated according to the determined optimal combination of activation states, and the wind turbine (100) is operated using the activated control features.

2. The method according to claim 1, wherein The type of optimization parameter is selected from at least two types of possible optimization parameters, including at least two of power generation, remaining lifetime, and useful power generation, wherein useful power generation is associated with optimization objectives of maximizing power generation when power demand from the grid is above a threshold and maximizing remaining lifetime when power demand from the grid is below a threshold. 3 . The method of claim 2 , wherein determining the type of optimization parameter comprises receiving user input for selecting the type of optimization parameter from at least two types of possible optimization parameters.

4. The method according to claim 1 or 2, wherein: The combination of activation states of the two or more control features is selected based on a predetermined impact of the two or more control features on the remaining lifetime and / or power generation of the wind turbine.

5. The method according to claim 1, wherein Determining whether to perform further optimization steps includes evaluating one or more boundary conditions.

6. The method of claim 5 , wherein evaluating the boundary conditions comprises determining whether a change in the remaining lifetime and / or power generation of the wind turbine resulting from activating the control features in combination according to the corresponding activation states exceeds a corresponding threshold value, and / or determining whether the total remaining lifetime exceeds a predetermined time period threshold value.

7. The method of claim 1 , wherein performing the one or more optimization steps comprises executing a search algorithm or an optimization algorithm to find a combination of activation states that results in optimization parameters that best satisfy an optimization objective, wherein the combination of activation states found by the algorithm is selected as the optimal combination of activation states.

8. The method according to claim 1, wherein The two or more control features include at least two or all control features selected from the group consisting of: a high wind ride-through (HWRT) control feature that performs a load-based reduction in output power of the wind turbine (100) under predetermined wind conditions; an adaptive control system (ACS) control feature that reduces the output power of the wind turbine if turbulence is determined to be above a threshold at the wind turbine (100); a power boost (PB) control feature that increases the power output of the wind turbine (100) by increasing the power limit of the wind turbine under predetermined wind conditions; a power curve upgrade kit (PCUK) control feature that modifies the control functionality of the controller (10), in particular the pitch angle control, based on hardware modifications installed on the wind turbine (100), in particular on the rotor blades (102); and A peak shaving feature that changes the operating curve used by a controller (10) to operate the wind turbine (100), the operating curve determining wind turbine settings, particularly rotational speed and pitch, as a function of wind speed.

9. The method of claim 1 , wherein estimating the optimization parameters comprises estimating at least a remaining lifetime, wherein estimating the remaining lifetime comprises estimating a failure rate of the wind turbine based on statistical data (24) of the wind turbine (100) and / or a group of wind turbines, and performing a fatigue assessment (20) of the wind turbine (100) based on measurements made by sensors (110) of the wind turbine (100) and / or on-site measurements.

10. The method of claim 9, wherein performing the fatigue assessment (20) comprises providing an aeroelastic model (21) of the wind turbine (100) and assessing fatigue loads of components (101, 102, 103, 104) of the wind turbine (100) based on the aeroelastic model and wind turbine data received from the measurements.

11. The method according to claim 10, wherein performing the fatigue assessment (20) comprises adapting an aeroelastic model (21) of the wind turbine (100) based on control features activated according to a combination of activation states associated with respective optimization steps.

12. The method according to claim 1, wherein In a first optimization step, selection of an activation state combination is based on a predetermined impact of two or more control features on the remaining lifetime and / or energy production of the wind turbine (100), wherein estimation of optimization parameters performs a model-based estimation of the remaining lifetime and / or energy production taking into account the control features activated according to the selected activation state combination, wherein subsequent optimization steps select a different combination of activation states based on the predetermined impact, wherein the different combination corresponds to at least one of activating an additional control feature, deactivating one of the control features activated in a previous optimization step, or replacing an activated control feature with a different activated control feature.

13. The method of claim 1 , wherein estimating the remaining lifetime of the wind turbine (100) comprises estimating the remaining lifetime of predetermined structural or mechanical components (101, 102, 103, 104) of the wind turbine, wherein the lowest remaining lifetime estimated for the structural or mechanical components (101, 102, 103, 104) determines the remaining lifetime of the wind turbine (100).

14. A controller for controlling the operation of a wind turbine, wherein the controller (10) is configured to activate or deactivate each of two or more different control features of the wind turbine (100), each control feature changing an operating characteristic of the wind turbine and having an effect on the lifespan and / or power generation of the wind turbine (100), The controller (10) comprises a data processor (11) and a memory (12) coupled to the data processor (11), the memory (12) storing control instructions which, when executed by the data processor (11), perform the method of claim 1.

15. The controller according to claim 14, wherein: The controller (10) is implemented by a wind turbine controller of the wind turbine (100), a wind farm controller coupled to the wind turbine (100), or a combination of such a wind turbine controller and a wind farm controller.

16. A computer-readable medium for controlling the operation of a wind turbine (100), wherein control instructions stored on the computer-readable medium, when executed by a data processor (11) of a controller (10) controlling the wind turbine (100), cause the data processor (11) to perform the method of claim 1.

17. A wind turbine (100) comprising a rotor (101) having rotor blades (102), a nacelle (103) rotatably mounted to a wind turbine tower (104), a generator (106) for generating electrical energy, and a controller according to claim 14 or 15.

18. A method for generating electrical energy, comprising the steps of: Operating the wind turbine (100) according to claim 17 according to the method of the preceding claim 1; Converting wind energy into rotational mechanical energy via rotor blades (102); rotating the generator (106) by rotating the rotor (101); and Converts rotational mechanical energy into electrical energy.

Images