Method of operating wind turbine

Abstract

A method of operating a wind turbine (100) is provided in which the wind turbine (100) is operable in a plurality of different operating modes (41-46) differentiating from at least one of life-time consumption of the wind turbine and energy production of the wind turbine. A sequence of operation modes (305; 306) is determined for a future time period (301). 741-745), and wherein an optimization parameter (op) is estimated on the basis of the at least one estimated extrinsic parameter (ep). The method further comprises obtaining a current value of the at least one external parameter (ep) and determining an actual mode of operation of the wind turbine at a current point in time, where the determination of the actual mode of operation comprises determining an actual mode of operation for a plurality of sequences (305; 741-745) and estimating an adjusted optimization parameter for a current value of the at least one external parameter (ep), and selecting an actual operating mode based on the estimated adjusted optimization parameter. The wind turbine is operated in the determined actual mode of operation.

Description

Technical Field

[0001] The invention relates to a method of operating a wind turbine, wherein the wind turbine is operable in a plurality of different operating modes. The invention also relates to a control system and a wind turbine comprising such a control system, and also to a computer program. Background Art

[0002] The use of wind energy is surging. Wind turbines are installed at different locations around the world and are therefore exposed to different environmental conditions. Wind turbines must withstand considerable wind forces acting on the rotor, nacelle and tower of the wind turbine. During its life, the structural components of the wind turbine are subjected to multiple load cycles, which may eventually lead to component failure. Wind turbines can usually be operated in different operating modes, wherein some modes drive the wind turbine more aggressively (usually resulting in higher loads and higher energy production), while other modes drive the wind turbine less aggressively (usually resulting in lower loads and lower energy production).

[0003] Wind turbines may also be exposed to varying environmental conditions, such as wind speed, which may in some cases lead to reduced energy production and in other cases to high loads on the wind turbine and, therefore, high lifetime consumption. It is therefore difficult for the operator to determine the best way to operate the wind turbine. Some optimization methods are known that optimize wind turbine operation to achieve optimization objectives, such as maximizing energy production, minimizing lifetime consumption, maximizing the consistency between energy production and demand, and maximizing revenue. An example of such optimization is described in document WO 2021 / 214152A1. Although this method achieves good optimization for many situations, there may be certain situations where the determined wind turbine operation scheme can still be improved to achieve better results, such as higher energy production or returns, a longer lifetime, or better utilization of the wind turbine during its design lifetime. Summary of the invention

[0004] Therefore, there is a need to improve the operation of wind turbines, and in particular to operate wind turbines in a manner that better achieves operational goals, such as increasing energy production or revenue.

[0005] This need is met by the features of the independent claim. The dependent claims describe embodiments of the invention.

[0006] In one embodiment of the invention, a method of operating a wind turbine is provided, wherein the wind turbine is operable in a plurality of different operating modes that differ in at least one of lifetime consumption of the wind turbine and energy production of the wind turbine. The method comprises determining a sequence (one or more) of operating modes for a future time period in an optimization process. Determining the sequence comprises estimating optimization parameters for the future time period and for each of a plurality of different sequences (e.g., candidate sequences) comprising different combinations of operating modes based at least on an estimated energy production associated with operation of the wind turbine in the operating mode of the respective sequence. Such an optimization process may also be referred to as long-term optimization, as it may estimate optimization parameters for a longer period of time, for example until the end of an estimated lifetime of the wind turbine.

[0007] The method may further comprise: obtaining a current value of at least one parameter, wherein the at least one parameter is an operating parameter and / or an external parameter of the wind turbine, and determining an actual operating mode of the wind turbine at a current point in time, wherein the determination of the actual operating mode is based at least on the determined sequence of operating modes and the obtained current value of the at least one parameter. Such selection of the actual operating mode may be referred to as short-term optimization, as it may determine the actual operating mode for a shorter period of time. Further, the wind turbine is operated in the determined actual operating mode.

[0008] The actual operating mode may be allowed to differ from the operating mode prescribed by the operating mode sequence determined for the current point in time, in particular, to differ from the determined sequence (optimal sequence) in which the estimated optimization parameters satisfy the optimization objective.

[0009] Several benefits can be achieved by this method. The determined sequence of operating modes can be or can include a sequence in which the optimization parameter is at its optimum (e.g., at a maximum or minimum value), depending on the type of the optimization parameter. An example is a sequence of operating modes that maximizes energy production or revenue. However, since such a determined sequence may be based on one or more estimated operating parameters and / or estimated external parameters, such a sequence may not always provide the best results in actual operation. In particular, the actual current value of the parameter may be very different from the actual estimate, so that changing the operation to a mode different from the mode specified by the "optimal sequence" may actually improve performance and better achieve the optimization goal. In addition, by determining the actual operating mode based not only on the current value of the parameter but also taking into account the determined (optimal) sequence, the impact of the change in mode on the optimal sequence can be taken into account. This allows the actual operating mode to be selected so that the optimization goal of the entire sequence is better achieved. As an example, an aggressive operation may be selected at present, but an update to the current external parameter (e.g., electricity price) is subsequently received, which is lower than the estimated external parameter, which will produce optimal operation if the curtailment operating mode of the turbine is used, thereby saving life for subsequent use. As another example, if the curtailed operating mode is currently selected due to a low value of an estimated external parameter, but the current value is significantly higher than the estimated value, then changing to an aggressive operating mode of the turbine may now result in more power and revenue being generated, at the expense of the lifetime of the turbine. Thus, by determining the actual operating mode of the wind turbine based on both the current value of the parameter and the (one or more) determined (optimal) operating mode sequence, several benefits may be achieved. Furthermore, the initial optimization process, i.e. the long-term optimization, is not repeated in order to determine the actual operating mode in the short-term optimization, as explained in more detail further below. Thus, the control may be made more efficient, since the actual operating mode may be determined repeatedly (e.g., within a short period of time) without repeating the optimization process, i.e., without performing the long-term optimization until the estimated end of lifetime.

[0010] Determining the sequence of operating modes may include determining one or more optimal sequences in which the optimization parameters satisfy the optimization goal. The determined sequence of operating modes may be a sequence of modes in which the wind turbine will operate during the future time period. For example, such an optimal sequence may be used as a prescribed sequence for operating the wind turbine during the future time period, or one or more of such optimal sequences may be used to derive an actual operating mode.

[0011] Optionally, the estimation of the optimization parameter may also be based on an estimate (e.g., an estimated value) of an operating parameter and / or an external parameter for a future time period. The corresponding parameter may be estimated for a time interval for a future time period. The estimated operating parameter may be an estimated lifetime consumption. The estimated external parameter may be an estimated wind condition and / or an estimated electricity price.

[0012] For example, the estimation of the optimization parameters may also be based on the estimated lifetime consumption. Thus, the end of the life of the wind turbine may be predicted, which may allow a more accurate estimation of the optimization parameters.

[0013] The future (first) time period may include a sequence of time intervals, wherein the sequence of operation modes may be defined by the operation mode for each time interval. The length of the time interval may be, for example, at least one month (e.g., at least 28 days), which may be, for example, 1 month, 2 months, 3 months, 4 months or more months, a quarter, half a year or a whole year. The future (first) time period may start at the current time point.

[0014] The future (first) time period may extend to the end of the lifetime of the wind turbine. The end of lifetime may be a predefined end of lifetime (end of design lifetime, e.g. 20 or 25 years), and / or may be a point in time at which the lifetime of the wind turbine has expired (e.g. as estimated from the corresponding sequence of operating modes). Combinations are also conceivable (e.g. the end of the future time period may correspond to the design lifetime, but may end earlier if the lifetime of the wind turbine expires before reaching the design lifetime).

[0015] The sequence of operating modes may be determined for a first time period in the future, wherein the actual operating mode of the wind turbine may be determined for a second time period in the future, which is shorter than the first time period and at least partially overlaps with the first time period. The first and / or the second time period may include the current point in time. The length of the second time period may correspond to or may be shorter than the length of the time interval of the first time period. A current value of the parameter may be obtained for the current point in time and / or for the second time period, in particular for a second time period shorter than the time interval (which may be an actual (measured, received or obtained) value or a near future estimate).

[0016] In particular, the actual operation mode may be repeatedly determined during a time interval of a future (first) time period (for which only one operation mode is defined for each sequence). The current value of the obtained parameter may be updated accordingly. For example, the determination of the actual operation mode may be performed each time an updated current value of the corresponding parameter is obtained. As an example, the actual operation mode may be regularly determined using a period of 30 days or less, 10 days or less, 2 days or less, 1 day or less, 12 hours or less, 6 hours or less, 1 hour or less or 10 minutes or less. If the parameter is an operating parameter, in particular a consumption life period (e.g., cumulative life period consumption), the period may be between 1 hour and 30 days, such as between 0.5 and 15 days, such as about 1 day. If the parameter is an external parameter, such as an electricity price, the period may be between 10 minutes and 10 days, such as between 1 hour and 5 days. The time interval of the future (first) time period may be at least one month, such as between one month and two years. Thus, optimization of operations may be achieved in both the long term and the short term, without neglecting the impact of short term optimization on long term performance.

[0017] In some exemplary embodiments, determining the sequence of operating modes may include: selecting a sequence from a plurality of different sequences in which the optimization parameters satisfy the optimization objective (e.g., maximization or minimization of the optimization parameters). Such a sequence may be considered to constitute an optimal sequence (for the corresponding optimization parameters). As indicated above, a plurality of such optimal sequences may be determined, e.g., for different sequence groups.

[0018] For example, the optimization parameter may be at least one of energy production, satisfaction of electrical energy demand, or revenue. For example, revenue may be estimated by utilizing an external parameter in the form of an electricity price. The electricity price generally depends on the electrical power demand, in particular the extent to which the demand can be satisfied by the available electrical power (if the available electrical power is less than the demand, the electricity price will generally rise, and vice versa). Therefore, feeding electrical energy into the grid to satisfy the demand may be achieved by feeding electrical energy into the grid when the electricity price is high. Feeding electrical energy into the grid when demand is high (and not feeding energy into the grid when demand is low) stabilizes the power supply of the grid and stabilizes the grid itself; for example, frequency and / or voltage fluctuations caused by a mismatch between the available power and the power demand may be reduced. Therefore, feeding energy into the grid in order to maximize revenue may also reduce grid fluctuations, stabilize the grid, and ensure a stable power supply of the grid. Maximizing revenue may particularly correspond to maximizing power feed under high power demand.

[0019] The optimization parameter may specifically correspond to revenue. The optimization parameter may be a net present value (NPV), which may correspond to the cumulative revenue from the production of electrical energy of the wind turbine determined from a current point in time (e.g. until the estimated end of the life of the wind turbine). The NPV may be determined by summing the revenue estimated for time intervals for future time periods. The revenue may be a discounted revenue, which may be discounted depending on how far in the future the time interval is, e.g. to take into account the risk of not realizing the revenue (e.g. for time intervals further in the future the security of the predicted revenue is lower and the risk of wind turbine failure etc. may increase). By such an optimization parameter the reliability of the optimization may be increased.

[0020] The optimization objective may be, for example, maximization of an optimization parameter in the form of energy production, satisfaction of power demand (e.g. how well the estimated power output corresponds to the power demand estimated based on external parameters) or revenue, or may be minimization of an optimization parameter in the form of consumption life.

[0021] The multiple different sequences may be candidate sequences. For each possible combination of operating modes for a future time period, a candidate sequence may be provided. Optimization parameters may be estimated for each candidate sequence. Thus, all possible combinations of operating modes may be considered when determining one or more optimal sequences.

[0022] For different candidate sequences, the future time period may have different lengths, for example, the lifespan of the wind turbines of different candidate sequences may be estimated to end at different future time points, ie, the end of the lifespan of the wind turbines may be different.

[0023] Optimization parameters of the sequence of operating modes may be estimated by estimating wind conditions (particularly wind speed) for a future time period, estimating a lifetime consumption of the wind turbine when operating with the sequence of operating modes under the estimated wind conditions, and estimating an energy production of the wind turbine for operation according to the sequence of operating modes under the estimated wind conditions in the future time period. The optimization parameters may be determined based on the estimated energy production for the future time period and optionally under consideration of an estimate of external parameters. Estimating the optimization parameters of the sequence may further comprise estimating an end of life of the wind turbine based on the estimated lifetime consumption of the respective sequence, which may correspond to an end of the future time period of the respective sequence. After reaching the end of life of the wind turbine, the wind turbine will likely no longer generate energy, which may therefore affect the total energy production achieved with the respective sequence of operating modes and thus the optimization parameters.

[0024] The determination of the optimization parameters may be performed for each of a plurality of time intervals in the future time period. For example, the determination of the optimization parameters may include: estimating the energy production of the wind turbine for each of a plurality of time intervals in the future time period based on the operation mode of the sequence corresponding to the respective time interval, estimating the external parameters for each time interval, calculating the optimization parameters for each time interval based on the energy production and the external parameters estimated for the respective time interval, and summing the optimization parameters determined for the time intervals (until the end of life is reached) to obtain the optimization parameters for the sequence.

[0025] Optionally, the optimization parameters may be discounted for the risk or uncertainty of achieving the optimization parameters in at least some time intervals. Preferably, the discount is higher for time intervals further into the future. Thus, higher uncertainties associated with achieving estimated optimization parameters in the distant future may be taken into account.

[0026] The external parameter may be at least one of wind speed, electrical power demand and electricity price. The electricity price may also be referred to as electrical power price or electrical energy price and may be measured in Euro / MWh or equivalent units.

[0027] The plurality of different operating modes may include one, two or more degrade modes and one, two or more upgrade modes. The plurality of different operating modes may include at least two, three, four or five of the following modes: a stop mode, in which energy production of the wind turbine is stopped; a life enhancement (LE) operating mode, which reduces the electrical power output of the wind turbine for predetermined operating conditions (e.g., if turbulence above a threshold is determined at the wind turbine, wherein the turbulence may be determined, for example, based on a measured wind speed and load model); a baseline mode, in which the wind turbine is operated conventionally (in particular, no upgrade or degrade features are activated); a first performance enhancement (PE) mode, in which the power output of the wind turbine is increased by a first amount (e.g., above rated power, such as by increasing the power limit of the wind turbine under predetermined wind conditions); and a second performance enhancement (PE) mode, in which the power output of the wind turbine is increased by a second amount greater than the first amount.

[0028] The stop mode and the LE mode may be degraded modes, in which the wind turbine is operated less aggressively (i.e., lower lifetime consumption). The first and second PE modes may be upgrade modes, in which the wind turbine may be operated more aggressively (providing a higher power output at the expense of lifetime). The first PE mode may, for example, be a PE5 mode, in which the power output (in the corresponding part of the operating characteristic) is increased by 5%, and the second PE mode may, for example, be a PE10 mode, in which the power output is increased by 10%. These are only example values, and other increases may also be used.

[0029] According to the first aspect of the present disclosure, the estimation of the optimization parameter may also optionally be based on at least one (e.g., 1, 2, 3 or more) estimated external parameter (in particular, the estimated value of the external parameter disclosed above). It should be clear that multiple external parameters may be used. Obtaining the current value of at least one parameter may include obtaining the current value of the at least one external parameter.

[0030] Determining the actual operation mode may include estimating an adjusted optimization parameter for each of a plurality of different operation mode sequences defining different operation modes at the current time point (in particular, each of the different sequences may define a different operation mode for the current time point) and for which optimization parameters have been estimated during the optimization process. For the current value of the at least one external parameter, the adjusted optimization parameter may be determined for the corresponding operation mode sequence by adjusting the optimization parameter estimated for the corresponding sequence during the optimization process. Based on the estimated adjusted optimization parameter, one of the different operation modes may be selected as the actual operation mode.

[0031] By this method, it is possible to estimate how operation in different operating modes at the current point in time affects the optimization parameters, taking into account the current values ​​of the external parameters. In particular, if the current values ​​of the external parameters deviate significantly from the estimated values ​​on which the sequence is based, it may be beneficial to select a mode different from the mode prescribed by the optimal sequence. In addition, since the adjusted optimization parameters are determined for the current values ​​of the external parameters and the different modes at the current point in time, it is possible to estimate how changing the mode at the current point in time affects the optimization parameters in the long term, which may allow an optimal choice to be made for the actual operating mode. For example, if demand and / or electricity prices are very high, driving the wind turbine in the most aggressive mode may not always be optimal, considering the increased life cycle consumption and associated production losses due to the premature end of the life of the wind turbine.

[0032] Determining the adjusted optimization parameters may optionally take into account the different lifetime consumption caused by each different operating mode at the current point in time. Thus, the impact that selecting a different operating mode for the current point in time has on the adjusted optimization parameters may be taken into account more accurately (e.g., selecting a more aggressive mode may lead to premature end of lifetime, which may be taken into account).

[0033] The obtaining of current values, determination of the actual operating mode and operation of the wind turbine in the determined actual operating mode may be repeatedly performed in an optimization process using the same optimization parameters estimated for a plurality of different operating mode sequences. Thus, as explained above, it is not necessary to repeat the (initial) optimization process, but the adjusted optimization parameters may simply be obtained from the initially determined optimization parameters and the current values ​​of the external parameters. Thus, the control may be made more efficient and more frequent changes of the actual operating mode depending on the current values ​​of the external parameters may be enabled.

[0034] Selecting one of the different operation modes as the actual operation mode based on the estimated adjusted optimization parameters may include: selecting the operation mode in which the operation at the current time point produces the adjusted optimization parameters that meet the optimization goal. The optimization goal may be any optimization goal disclosed herein, for example, it may be the maximization of the adjusted optimization parameters, i.e., selecting the mode with the highest value of the adjusted optimization parameters for the current operation.

[0035] Estimating the adjusted optimization parameters for different operation modes at the current point in time may include: for different operation mode sequences distinguished at least by their operation modes at the current point in time (e.g., the current time interval), estimating the dependency of the optimization parameters obtained for the corresponding sequence on the value of the at least one external parameter, and using the dependency, for the current value of the at least one external parameter obtained, estimating the adjusted optimization parameters for each of the sequences. Thus, a simple method may be provided that can determine how the current value of the at least one external parameter affects the value of the optimization parameter without having to perform a complete re-estimation of the optimization parameter throughout the future time period when the current value changes.

[0036] The plurality of different operation mode sequences may include a plurality of groups, and each group may include a plurality of different operation mode candidate sequences. Sequences within the same group may include the same operation mode for the current time interval of the future time period (i.e., they may define the same mode for the current time interval). Sequences of different groups may include different operation modes for the current time interval (i.e., the group may differ due to the mode in which the sequence starts within the current time interval). Optimization parameters may be estimated for each different candidate sequence within each group. Thus, information may be obtained about how operations in different modes at the current time point affect optimization parameters.

[0037] Determining the sequence of operating modes for the future time period may include determining an optimal sequence for each different group by selecting candidate sequences from the corresponding group whose optimization parameters satisfy the optimization objectives (e.g., any objectives mentioned herein), wherein the adjusted optimization parameters are estimated for each optimal sequence of the groups. Considering these optimal sequences helps to estimate how changing the operating mode at the current point in time affects the optimization parameters (e.g., NPV) over the lifetime of the wind turbine.

[0038] For each set of optimal sequences, a dependency of the optimization parameter on the value of the at least one external parameter may be estimated to determine an adjusted optimization parameter for the respective optimal sequence. This may allow consideration of how a change in the operating mode may change the overall outcome of the wind turbine operation, taking into account the current value of the at least one external parameter.

[0039] Estimating the adjusted optimization parameter may include: employing a linear dependency or a nonlinear dependency (e.g., a polynomial) between the value of the optimization parameter and the value of the at least one external parameter. This may allow for fast and efficient calculation of the adjusted optimization parameter for each optimal sequence even if the current value of the at least one external parameter is frequently updated.

[0040] The slope of the dependency or another parameter may be determined so that the adjusted optimization parameter is estimated for the operating mode depending on the operating parameter estimated for the operating mode at the current point in time (e.g. for the current time interval). The operating parameter may be, for example, the energy production estimated for the respective operating mode. For example, the revenue or NPV may increase linearly with increasing electricity prices, wherein if the energy production of the wind turbine is higher, the growth rate is also higher, which may be taken into account by this method.

[0041] Estimating the adjusted optimization parameters for the operating mode at the current point in time may include obtaining optimization parameters estimated for a sequence of corresponding operating modes including the current time interval (e.g., for a group optimal sequence). The estimation may be based on at least one external parameter estimated for the current time interval (e.g., the initial optimization may be based on the estimation, as indicated above). In addition, the estimated optimization parameters may be adjusted using the current value of the at least one external parameter and the dependency between the optimization parameter estimated for the current time interval and the at least one external parameter (e.g., using the above-mentioned linear relationship) to determine the adjusted optimization parameters. For example, this may be done for each group optimal sequence to obtain adjusted optimization parameters for each of these group optimal sequences.

[0042] The method may further comprise: estimating energy production of the wind turbine during a current time interval when operating in the respective operating mode (e.g. as part of an initial optimization of a first time period), and estimating a dependency between the optimization parameter and the at least one external parameter based on the energy production during the current time interval. Thereby, an efficient and accurate estimation of the optimization parameter adjusted for the current value of the at least one external parameter may be achieved.

[0043] The determined actual operating mode may be set for the current time interval for a future time period, or may be set for a second shorter time period.If a new current value becomes available, it may be updated.

[0044] According to the second aspect of the present disclosure, determining the sequence may further include: selecting an operation mode sequence whose optimization parameters satisfy the optimization target (which may be referred to as a selected (optimal) sequence). The optimization parameters and the optimization target may have any configuration disclosed herein.

[0045] In addition, the current value of the obtained parameter may include the current value of the consumed life of the wind turbine. For example, the consumed life of the wind turbine during actual operation up to the current point in time (e.g., cumulative life consumption) may be determined or estimated based on, for example, sensor data, statistical data, etc.

[0046] Determining the actual operating mode may include: taking into account the deviation between the current value of the consumption life period and the consumption life period expected for operation in the selected operating mode sequence (for example, a target value associated with the consumption life period specified for the selected sequence), selecting the actual operating mode from the multiple different operating modes, wherein the selection is performed so as to keep the current consumption life period within the range of consumption life periods expected for the selected (optimal) operating mode sequence.

[0047] In this way, it is ensured that the objectives of the selected (optimal) sequence of operating modes are achieved. The determination of the sequence of operating modes may be based on the expectation of the lifetime consumption during future time periods. When the wind turbine is operated according to the selected sequence and the actual lifetime consumption deviates, the optimization objectives may no longer be achieved. For example, if the lifetime consumption increases during certain time periods, the end of the life of the wind turbine may occur prematurely, and the energy production in the corresponding time range before the expected end of the life may be lost. This negative impact may significantly affect the overall performance of the selected sequence, for example, if good wind conditions and / or high electricity prices are expected (forecasted) for the last time range. By making the selection of the actual mode based on the deviation between the actual consumed lifetime and the expectation of the consumed lifetime, and by allowing the actual mode to be different from the mode of the selected (optimal) sequence, it is ensured that the wind turbine reaches the end of its lifetime envisaged by the selected (optimal) sequence, thereby avoiding the corresponding negative impact. On the other hand, if the consumed lifetime is less than what is actually expected according to the selected sequence of operating modes, selecting the mode according to the deviation may allow the wind turbine operation to make better use of the available resources, for example by selecting an actual operating mode that consumes more lifetime and provides more energy production. Thus, allowing deviations from the selected sequence of operating modes and selecting the actual mode according to the deviations may provide several benefits and in particular may ensure that optimization goals are achieved and the wind turbine is utilized optimally.

[0048] Keeping the current consumption life within the expected consumption life range according to the selected operating mode sequence may include or be performed in the following manner, namely: maintaining, reducing or minimizing the deviation at least within multiple cycles of selecting the actual operating mode. It should be clear that the actual operating mode can be repeatedly selected during operation, for example, repeatedly selected in cycles as described above. Not every operation in the actual operating mode can make the actual consumption life closer to the expected consumption life, but some selections may result in large deviations, which can then be compensated again in the next (subsequent) selection step for the actual operating mode. This may be due to the nature of the process, because it is usually impossible to stay exactly on the estimated life curve of the selected (optimal) sequence. In addition, operating in a mode that will make the current value of the consumption life closer to the expected consumption life may not be beneficial at every moment, for example at high winds and high energy prices. However, the selection is performed so that there is a tendency for the current consumption life to remain close to the expected consumption life.

[0049] Therefore, in some embodiments, the range may not be a predetermined range, but a range around the expected consumption life period, which is still acceptable and consistent with the trend of keeping the current and expected consumption life period consistent. In other embodiments, the range may have a predetermined boundary, which can be used in the selection process of the current operating mode.

[0050] The expected consumption life span according to the selected sequence of operating modes may be used in different ways for determining the deviation, for example, it may correspond to a required remaining life span, such as the life span required to reach the end of life defined by the selected sequence (e.g., the end of a future time period). Such a required remaining life span may be used to estimate a target consumption life span at the current point in time, or an estimated end of life when starting from the actual current consumption life span, etc.

[0051] In one example, the expected consumed life span for the selected operation mode sequence may be determined by determining a target value for the consumed life span of the wind turbine at the current point in time based on the selected operation mode sequence, wherein the deviation is the deviation between the target value for the consumed life span at the current point in time and the current value of the consumed life span. Thus, it may be determined whether the consumed life span is more or less than expected, and the deviation may be used to make the selection of the actual operation mode.

[0052] As another example, an estimated current end of life of the wind turbine may be determined based on the current value of the consumed life and the expected life consumption of operations in the selected sequence of operating modes, thereby determining an expected consumed life for the selected sequence of operating modes, wherein the end of the future time period may correspond to a target end of life, wherein the deviation may be a deviation between the target end of life and the estimated current end of life. The target end of life may be estimated based on the sequence of operating modes, for example when selecting the sequence, for example when performing an initial optimization based on an optimization objective. The target end of life may be an end of life to be reached for the selected sequence.

[0053] Determining a target value for the consumed lifetime of the wind turbine (for the current point in time) may comprise: calculating a cumulative lifetime consumption associated with operation of the wind turbine in different operating modes of the selected sequence, wherein the lifetime consumption is calculated backwards from the end of a future time period (according to the end of the lifetime of the sequence) to the current point in time to determine a required remaining lifetime of the wind turbine, i.e. the remaining lifetime required to operate the wind turbine according to the selected sequence at the current point in time until the end of the future time period. Such a calculation may provide a reliable reference, which provides a good indication of deviations, and may therefore be used as a basis for selecting an actual operating mode.

[0054] Determining the estimated current end of life of the wind turbine may include adding the lifetime consumption associated with operation of the wind turbine in the selected sequence of operating modes to the current value of the consumed lifetime (e.g., by adding the lifetime consumption for each time interval of the future time period of the corresponding operating mode to the current consumed lifetime). Thus, based on the current consumed lifetime, an end of life may be estimated (referred to herein as the "estimated current end of life"). This may then be compared to the target end of life, i.e., the total lifetime expected when operating in the selected sequence. Thus, it may be quickly and efficiently determined whether the current consumed lifetime is higher or lower than expected.

[0055] In some embodiments, the actual operating mode is selected from a plurality of different operating modes by adopting a random method that takes into account the corresponding deviations. The random method can be configured to reduce the deviations, for example, by selecting the actual operating mode multiple times. Such a random method can provide a fast and efficient selection means, and in particular can allow the actual consumption life period to be consistent with the consumption life period expected according to the sequence within multiple selection cycles.

[0056] The plurality of different operating modes may include at least one or more first operating modes and one or more second operating modes, wherein the one or more first operating modes may have a lower life cycle consumption than the one or more second operating modes. The method may include adjusting the probability of selecting the first operating mode or the second operating mode as the actual operating mode based on a deviation (e.g., a deviation between a target consumption life cycle and an actual consumption life cycle or between a target end of life and an estimated current end of life). Adjusting the probability of selection helps to take into account changes in the deviation when selecting the actual operating mode. The first mode may be a degraded (less aggressive) mode, resulting in lower energy production, and the second mode may be an upgraded (more aggressive) mode, resulting in higher energy production of the wind turbine.

[0057] The method may include assigning selected probabilities to one or more first operating modes and one or more second operating modes, wherein the probabilities depend on the deviations. For example, if the actual current consumption life span is higher than expected according to the selected sequence, a higher probability may be assigned to the one or more first operating modes (and vice versa). Optionally, the probabilities may be further assigned according to external parameters (e.g., electricity prices).

[0058] For example, the probability of selection may be assigned to two or more first operating modes and / or the probability of selection may be assigned to two or more second operating modes. Thus, more than one mode may be used to drive the current consumption life back to the expected value.

[0059] The probabilities may be distributed between the respective modes depending on the deviation and / or an external parameter (e.g. electricity price). For example, depending on the deviation, a first probability may be assigned to the one or more first modes and a second probability may be assigned to the one or more second modes. The first probability may then be distributed between the one or more first operating modes depending on the current value of the external parameter and / or the second probability may be distributed between the one or more second operating modes depending on the current value of the external parameter. For large values ​​of the external parameter, a higher probability may be assigned to a mode with higher energy production (a more aggressive mode) and vice versa. Thus, on the one hand it is ensured that the actual consumption lifespan is consistent with the consumption lifespan expected according to the selected (optimal) sequence, while on the other hand it is ensured that the prevailing conditions are optimally exploited (e.g. for currently high electricity prices a highly overrated mode is selected with a high probability and for very low electricity prices the wind turbine is stopped with a high probability).

[0060] In an exemplary embodiment, selecting an actual operating mode from a plurality of different operating modes taking into account the deviation may include: assigning a first portion (e.g., a portion, a share, or a small portion) of a value range to one or more first operating modes, and assigning a second different portion of the value range to one or more second operating modes, wherein the sizes of the first and second portions are selected based on the deviation; generating a random number within the value range; and selecting the operating mode associated with the portion of the value range in which the random number is located as the actual operating mode. The first small portion and the second small portion may span the value range; they may not overlap; the one or more first operating modes may have a lower lifespan consumption than the one or more second operating modes (as also mentioned above). The boundaries of the value range may be selected to correspond to the boundaries of a range of expected consumption lifespans of the selected sequence, wherein the actual current consumption lifespan will be kept within this range. By adjusting the portions of the value range, the probability of selecting one of the first operating modes or one of the second operating modes may be adjusted in a simple and efficient manner based on the deviation.

[0061] The value range may be configured to extend equally around a value representing an expected consumption life period (e.g., a target consumption life period or a target end of life period) for the selected sequence of operating modes. The position of a boundary (e.g., epsilon, ε) between a first portion and a second portion of the value range may be determined based on the deviation. The value of the boundary may correspond to a current value of the consumption life period. The first range may extend from a first boundary of the value range to the boundary, and the second portion may extend from the boundary to a second boundary of the value range.

[0062] If the deviation indicates that the current value of the consumption lifetime is higher than the consumption lifetime expected according to the selected sequence of operating modes, the boundary between the first part and the second part can be set so that the first part (corresponding to the mode with less lifetime consumption) spans a larger part of the value range than the second part (and vice versa). Thus, it is made more likely that a mode with low lifetime consumption, such as the LE mode or the stop mode, will be selected. Thus, different selection probabilities can be effectively assigned to the respective operating modes according to the deviation.

[0063] As a simple example, if the current value of the consumption life span is to be maintained between +10% and -10% of the expected consumption life span, the boundaries of the value range from 0 to 1 may be set to correspond to the corresponding values. The boundary between the first portion and the second portion may then be set to a value within the range corresponding to the current value of the consumption life span, e.g., 0.5 if there is no deviation, 0.75 if the deviation is +5%, and 0.25 if the deviation is -5%. Thus, the sizes of the first and second portions, and therefore the probability of a random number falling within the portions (and therefore the probability of selecting the corresponding mode), may be effectively adjusted based on the deviation.

[0064] In one embodiment, the actual operation mode is selected from a plurality of operation modes by an epsilon greedy algorithm, wherein the position of the epsilon parameter in the value range of the epsilon greedy algorithm is determined by the deviation.

[0065] It should be clear that the optimal first and second aspects of the present disclosure may be combined. For example, a wind turbine may be operated according to the first aspect and embodiments thereof. Thus, an actual operating mode may be selected which provides an overall optimal result for the optimization parameters based on the current values ​​of the external parameters. If the life period consumed by the operation deviates by a predetermined amount from the expected consumption life period (which may be based on the optimal sequence initially selected or a sequence selected as part of the first aspect), the operation may be changed to a method according to an embodiment of the second aspect of the present disclosure, i.e. a method in which the actual consumption life period is consistent with the expected consumption life period.

[0066] Therefore, in one embodiment, the wind turbine may be operated in a first short-term optimization mode, in which the wind turbine is operated by the method according to any embodiment of the first aspect disclosed herein, and when the deviation between the actual current consumption life span and the target consumption life span of the wind turbine at the current time point reaches or exceeds a threshold, the operation of the wind turbine may be switched to a second short-term optimization mode, in which the wind turbine is operated by the method according to any embodiment of the second aspect disclosed herein. Therefore, benefits of two aspects may be achieved.

[0067] According to another embodiment of the invention, a control system for operating a wind turbine is provided, wherein the control system is configured to operate the wind turbine in a plurality of different operating modes, the operating modes being distinguished by at least one of lifetime consumption of the wind turbine and energy production of the wind turbine. The control system is configured to perform any of the methods disclosed herein. The control system may in particular comprise a processing unit and a memory, wherein the memory stores control instructions which, when executed by the processing unit, cause the processing unit to perform any of the methods disclosed herein.

[0068] According to another embodiment of the present invention, a wind turbine is provided. The wind turbine comprises a control system, wherein the control system is configured to control the operation of the wind turbine in a plurality of different operation modes. The control system may have any configuration disclosed herein, and in particular may be configured to perform any method disclosed herein.

[0069] According to another embodiment of the invention, there is provided a computer program for controlling the operation of a wind turbine, wherein the computer program comprises control instructions which, when executed by a processing unit of a control system controlling the operation of the wind turbine, cause the processing unit to perform any of the methods disclosed herein. The computer program may be provided on a volatile or non-volatile data carrier or storage device and may further be provided via a network connection. The computer program may be operable on any of the control systems disclosed herein.

[0070] Additionally and / or alternatively, another aspect and / or an embodiment in particular being one of the aforementioned aspects and / or embodiments is disclosed, namely a method for operating a wind turbine, in particular an offshore wind turbine, further in particular a floating offshore wind turbine, and / or a method for feeding electrical energy and / or chemical energy to an electrical grid and / or a chemical network, the method comprising the following steps:

[0071] performing the steps of an embodiment or a combination of embodiments of a method as disclosed above, and / or operating an embodiment of a wind turbine as disclosed above,

[0072] generating electrical power and / or electrical energy by means of a wind turbine and in particular generating chemical energy in the form of a gas or liquid, in particular in the form of hydrogen, at least partially from the electrical energy, further in particular by converting the electrical energy into hydrogen by means of electrolysis,

[0073] at least partially transmitting electrical power, electrical energy and / or chemical energy to an energy receiving arrangement, in particular wherein the receiving arrangement is not located in international waters, on shore and / or within a 12 mile zone of a respective sovereign state over which the respective state has jurisdiction, and

[0074] The electrical power, electrical energy and / or chemical energy is at least partially supplied to an electrical and / or chemical utility network, in particular to an onshore electrical and / or chemical utility network and / or to a chemical / electrical storage device.

[0075] The term "chemical energy" includes ways of storing energy in chemical substances, such as hydrogen. For example, the chemical energy network can be a gas (hydrogen) network and / or a storage device.

[0076] It is to be understood that the features mentioned above and the features to be explained below can be used not only in the respective combinations indicated, but also in other combinations or alone, without departing from the scope of the present invention. In particular, the features of different aspects and embodiments of the present invention can be combined with each other, unless otherwise indicated. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The foregoing and other features and advantages of the present invention will become further apparent from the following detailed description read in conjunction with the accompanying drawings, in which like reference numerals represent like elements.

[0078] Figure 1 is a schematic diagram showing a wind turbine including a control system according to an embodiment.

[0079] Figure 2 is a schematic diagram showing functional blocks of a control system according to an embodiment.

[0080] Figure 3 is a flow chart illustrating a method of operating a wind turbine according to an embodiment, which includes determining a sequence of operating modes (as part of long-term optimization).

[0081] Figure 4 Shown is a schematic diagram illustrating different input parameters for an optimization and the progress of the optimization according to an embodiment.

[0082] Figure 5 is a schematic diagram graphically illustrating different operation mode candidate sequences sorted according to the values ​​of estimated optimization parameters.

[0083] Figure 6 is a flow chart illustrating a method of determining an actual operation mode of a wind turbine based on external parameters and a determined sequence of operation states according to an embodiment.

[0084] Figure 7 The diagram shows Figure 6 Schematic diagram of the method for different starting operation modes input parameters, the operation mode om for each time interval of the group optimal sequence and the optimization parameters.

[0085] Figure 8 is a schematic diagram illustrating the estimated dependence of the optimization parameters on the current values ​​of the external parameters for different current operating modes corresponding to different (set of optimal) sequences of operating modes. DETAILED DESCRIPTION

[0086] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. It is to be understood that the following description of the embodiments is given for the purpose of illustration only and should not be understood in a restrictive sense. It should be noted that the accompanying drawings should be considered as schematic representations only, and the elements in the accompanying drawings are not necessarily drawn to scale with each other. On the contrary, the representations of 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 "one", "one" and "a kind of" are intended to also include plural forms, unless the context clearly indicates otherwise. Unless otherwise stated, the terms "include", "have", "comprise" and "contain" will be interpreted as open terms (i.e., meaning "including but not limited to").

[0087] Figure 1 A wind turbine 100 is schematically shown and comprises a rotor 101 with rotor blades 102. A nacelle 103 of the wind turbine 100 is mounted to a wind turbine tower 104 and is rotatable by means of a yaw drive 105. The wind turbine 100 may also comprise a generator 106, an optional gearbox (not shown) and a converter 107. In operation, wind energy is converted into rotational mechanical energy by the blades 102 of the rotor, wherein the rotation of the rotor 101 turns the generator 106 which converts the mechanical energy into electrical energy. The converter 107 may provide an AC voltage at a desired AC frequency. Although Figure 1 A full converter (FC) topology is shown, but different topologies, such as a DFIG topology, may also be used. Of course, there may also be a transformer receiving electrical power from the converter 107 and other components common to such wind turbines (not shown). The wind turbine may be coupled to a grid to provide electrical power to the grid, for example via a collection network of a wind farm or more directly.

[0088] The wind turbine is controlled by a control system 10 comprising a processing unit 11 and a memory 12. The processing unit 11 may be any type of processor, such as a microprocessor, an application specific integrated circuit, a digital signal processor, etc. The memory 12 may comprise volatile and non-volatile memories, in particular RAM, ROM, EEPROM, FLASH memory, a hard disk drive, etc. The control system 10 may further comprise a communication interface 15 for communicating with an external data processing entity, via which the wind turbine may receive external data, in particular data values ​​of one or more external parameters, such as environmental conditions (e.g. wind conditions, wind speed), energy demand, electricity prices, grid-related information, statistical and / or historical information, etc. The control system 10 may further comprise a user interface (not shown) and other communication interfaces, such as a data acquisition interface, for example for receiving data from one or more sensors 20 of the wind turbine. Such sensors may be configured to measure environmental parameters (e.g. wind conditions, such as speed, direction), wind turbine loads (e.g. strain, bending moment, etc.) and other information useful for operating the wind turbine.

[0089] The control system 10 is configured to control the wind turbine according to a plurality of different operating modes, which differ in at least one of the energy production and lifetime consumption of the wind turbine, and preferably in both. To operate the wind turbine in these different modes, the control system 10 may, for example, control at least one, preferably all, of the pitch drives 109 (pitch angle control) of the blades, the yaw drives 105, the converters 107 and the generators 106 (e.g., via the converters), and other controllable components of the wind turbine.

[0090] The control system 10 may operate the wind turbine in one or more first operating modes that are degraded modes and preserve the life of the wind turbine, for example by rotating the rotor 101 at a lower speed (e.g., pitching the blades 102 outward). The first operating modes may include a stop mode (at least stopping the energy production of the wind turbine for output purposes) and an LE mode. In the LE mode, the control system may be configured to detect turbulence at the wind turbine and reduce the output power of the wind turbine if turbulence above a predetermined threshold is detected. Thus, in the LE mode, fatigue loads on the wind turbine may be reduced at the expense of energy production. Optionally, the first operating modes may include: a simple curtailment mode, in which the output power of the wind turbine is curtailed (e.g., by pitching the rotor blades outward, which reduces stress on the rotor and the wind turbine); and / or a HWRT (high wind ride-through) mode. In the HWRT mode, the control system 10 may be configured to perform a load-based reduction in output power above a predetermined wind speed. For example, if the wind speed exceeds a predetermined value (e.g., 23 m / s) for a certain period of time, the rotational speed of the rotor and the power output of the wind turbine may be gradually reduced. The first operating mode may also include a turbine load control (TLC) mode. The control system 10 may implement a turbine load control system that continuously monitors the structural loads on the wind turbine, and if the measured loads exceed normal operating values, the control system may automatically adjust the operation of the wind turbine to bring the loads back into the design range, such as by reducing the rotational speed of the rotor (pitching the rotor blades outward).

[0091] The control system 10 may also operate the wind turbine in one or more second operating modes, which are upgrade modes and increase the energy production of the wind turbine, which is usually at the expense of the life of the wind turbine (increasing life consumption), for example by allowing a rotor speed higher than the rated rotor speed. The first operating mode may include a first and a second performance enhancement (PE) mode. The PE mode may increase the energy production of the wind turbine by increasing the power limit under predetermined conditions. At high wind speeds, the wind turbine may operate at the limit of the output power (e.g., rated power), but the operation may occur below the load limit of the structural loads of the wind turbine. In the PE mode, the control system increases the rotational speed of the rotor in proportion to the increase in output power. For example, in the corresponding PE mode, the net power generation may increase by up to 5% (PE5) or 10% (PE10).

[0092] The control system 10 may implement other operating modes than the first and second operating modes, such as a baseline mode (i.e., normal operation) in which the wind turbine operates at nominal parameters. Preferably, the control system 10 is configured to operate the wind turbine in at least a baseline mode, a stop mode, a LE mode, and two different PE modes, but two modes (stop and baseline) may be sufficient.

[0093] The control system 10 may be implemented by one or more controllers; it may be implemented by a wind turbine controller of the wind turbine 100, a wind farm controller, an external server, or a combination thereof. Some portions of the disclosed method (e.g., overall optimization) may be performed by one controller (e.g., a wind farm controller) and other portions may be performed by another controller (e.g., a wind turbine controller), or the method may be performed by a single controller.

[0094] The control system 10 is configured to perform a long-term (LT) optimization, in which it determines one or more optimal operating mode sequences, and a short-term (ST) optimization, in which, based on the results of the LT optimization, the actual operating mode at the current point in time is selected and used to operate the wind turbine. Two different methods of performing short-term optimization are disclosed herein, which can be used in isolation or can be combined to synergistically improve the performance of the wind turbine. First, the long-term optimization is described.

[0095] The control system 10 may implement an optimizer 200 that takes into account the operating history of the turbine (i.e., lifetime consumption), future lifetime consumption depending on the operating mode of the turbine and environmental conditions, statistical and short-term predictions of environmental conditions, and economic values. The long-term optimization determines a sequence of operating modes for future time periods (which may include sequences of fixed time intervals, such as years or months) until the end of the life of the wind turbine, for which the optimization parameters estimated for the sequence achieve the optimization goal. For example, the optimization parameter is energy production, satisfaction of energy demand, or revenue, in particular reflecting discounted revenue and net present value (NPV) in time intervals for future time periods.

[0096] Figure 3An embodiment of a long-term optimization method that may be performed by the optimizer 200 is illustrated. In step S11, measured data of the operation of the wind turbine in the past is obtained. In step S12, the past lifetime consumption of the wind turbine is estimated for the current point in time based on the obtained data and / or statistical data. These steps may be performed by a lifetime estimator 210, which, for example, obtains long-term wind turbine data 211 (e.g., wind speed (ws), turbulence intensity (ti), generated power, rotor speed, nacelle acceleration, etc.). The lifetime estimator 210 may include a load simulation block 215 (which may use load simulation and / or an aeroelastic model of the wind turbine), which is configured to determine the lifetime consumed by the wind turbine based on historical data and predict future lifetime consumption for different operating modes of the wind turbine. Through the long-term wind turbine data 211 and the simulation block 215, historical damage 212 may be obtained and historical lifetime consumption 213 may be estimated ( Figure 3 Step S12). Therefore, the consumption life span at the current time point can be obtained.

[0097] Furthermore, based on the load simulation block 215, the dependence of damage or lifetime consumption on wind speed (and / or other environmental conditions, such as turbulence intensity, temperature, wind shear, wind direction, etc.) and on the operating mode can be obtained, which is provided in block 216 (step S13). In addition, sensor data of the turbine, such as rotor speed, generated power, strain, etc., can be used in a model for damage, which model can determine in an accurate manner the past damage suffered by the turbine. The optimizer 200 also includes an environmental condition estimator 220, which uses external data 221 (which may include site selection studies, site selection reports, meteorological simulations, meteorological mast data, mesoscale data, etc.) and further uses site-specific information 222 (which may include site-specific Weibull distributions, wind rose diagrams, etc.) to estimate future wind conditions, such as wind speed. Therefore, the wind distribution of the site of the wind turbine can be analyzed and estimates can be made for the future.

[0098] The optimizer 200 also includes a parameter estimator 230, which is configured to estimate optimization parameters according to the operating mode and environmental conditions. As an example, future income can be estimated by the estimator 230, but other optimization parameters, such as energy production or the satisfaction of energy demand, can also be used. The power curve block 231 provides a power curve of the wind turbine for each operating mode. The energy production estimator 232 estimates the energy production of the wind turbine from the power curve according to the operating mode. The estimation can be performed for a time period corresponding to the time interval of the future time period (for example, one year), so that the annual energy production (AEP) of each operating mode can be estimated for each time interval (see also step S13). The estimation can also use environmental data for the corresponding time interval from the estimator 220, in particular the wind distribution. The estimation block 233 can estimate the optimization parameters, for example, for a time interval depending on the corresponding operating mode, for which additional data can be used. An external parameter forecasting unit 234 can be provided, which estimates the external parameters for each time interval of the future time period (see also step S14). If revenue is the optimization parameter, the forecast unit 234 may, for example, provide a forecast of electricity prices, for example from customer expectations of future prices. The additional data block 235 may provide other relevant information for estimating the optimization parameters, such as the internal rate of return when optimizing for revenue. Thus, accurate estimates of the optimization parameters for different operating modes and different time intervals may be obtained by the estimator 230.

[0099] The optimization unit 201 can now determine one or more, preferably a plurality of, sequences of operation modes of the wind turbine for the future time period. As an example, the estimation can be performed for all time intervals and all combinations of operation modes for the future time period. For example, if the life of the wind turbine has been consumed, the estimation may end for a particular sequence. The optimization unit 201 can compile different candidate sequences, each candidate defining an operation mode for each time interval until the end of the life of the wind turbine (step S15).

[0100] The lifetime estimator 210 may include a lifetime consumption block 217 that determines the lifetime consumption (or damage) of the wind turbine based on the estimated environmental conditions (from the estimator 220, such as wind speed, such as a statistical wind speed distribution or mesoscale data obtained from the site) and the corresponding operating mode. The lifetime consumption may refer to the rate at which the lifetime is consumed in the corresponding mode, so that different operating modes may have different slopes in the lifetime graph, and the unit 217 may determine this rate or slope for each mode (e.g., driving the wind turbine harder or in high wind conditions will result in a higher slope of lifetime consumption within the time interval). Thus, for a particular candidate sequence, the lifetime consumption within each time interval may be estimated. The end of life estimator 218 determines the maximum lifetime of the wind turbine for the corresponding sequence of operating modes (and therefore determines the end of life), i.e. determines the time interval over which the lifetime of the wind turbine has been consumed.

[0101] For each operating mode candidate sequence, the value of the optimization parameter is determined at the end of the lifetime of the wind turbine (i.e. at the end of a future time period for the respective wind turbine). For example, the annual discounted revenue is calculated and the net present value (NPV) can be derived from this. The candidate sequences can then be sorted in a list according to the value of the optimization parameter (e.g. if the optimization goal is to maximize the optimization parameter, such as revenue, the highest value can be ranked first). The result is Figure 5 In the figure, Figure 5 A future time period 301 is shown, which has five time intervals 302, each corresponding to one year. The horizontal axis is the time axis (a) in years, whereby the years are counted from the start of operation of the wind turbine. The vertical axis represents the value of the optimization parameter op in arbitrary units (au), which may be, for example, revenue. At the top, the operating mode candidate sequences 305 that produce the highest values ​​of the estimated optimization parameter are shown. Note that for some sequences (e.g., 306, 307), the lifetime of the wind turbine has ended before the 20th year, where Figure 5 The hatching indicated by reference numeral 43 in FIG. 3 corresponds to a wind turbine "death". Mode 41 is LE mode, mode 42 is baseline mode, mode 44 is PE5 mode, mode 45 is PE10 mode, and mode 46 is stop mode. As can be seen, the sequence 305 that produces the highest values ​​stops the wind turbine in years 16, 17, and 18, and operates in baseline and LE modes in years 19 and 20. Note that Figure 5 Only a subset of the candidate sequences are shown.

[0102] Figure 4A diagram illustrating different input parameters of the optimization and the progress of the optimization is shown. The upper diagram 401 shows an estimate of an external parameter ep for a future time period provided by block 234, such as the price of electricity per MWh in Euro (expected future price assumption). Diagram 402 shows the damage rate (or lifetime consumption) Dr as a function of wind speed for different operating modes, in damage values / year. As can be seen, at higher wind speeds, the upgrade modes 44, 45 cause higher damage to the wind turbine, while the stop mode 46 results in almost zero damage.

[0103] Figure 4 The last graph 403 of illustrates the accumulated damage D or consumed life of a wind turbine over time t (in years) (a) until the current time point 405, and further illustrates the estimated future damage (lifetime consumption) for different operating modes, showing different slopes for each mode. The value range is selected so that D=0 corresponds to no damage and D=1 corresponds to lifetime consumption (i.e., end of life). The consumed lifetime can be a health indicator of a wind turbine, which represents the health state as a numerical value, for example, as a value between 0 and 1, where 1 means that the wind turbine is damaged to the extent that it can no longer operate. The thick solid line 404 indicates the previous damage accumulated over the life of the turbine until the current time point 405. The diagonal dashed line 409 indicates the design case. It is assumed that once the accumulated damage (vertical axis) reaches a value of 1, the wind turbine stops operating. In this example, the current value of the consumed lifetime is higher than the design case. This indicates that the wind turbine has been operating more aggressively in its past than expected in the design case. If the wind turbine continues to operate in this manner, it will not reach its design life span, which is shown here as a black vertical line 410. Different operating modes result in different slopes in graph 403, as calculated by block 217. Graph 403 graphically indicates whether the wind turbine will reach its design life span 410 when operated in a particular operating mode.

[0104] In this example, the current year is year 16, leaving the wind turbine in operation for another four years. Due to the price assumptions shown in graph 401 (low prices for the first 18 years, and high prices for the remaining years until the end of life), the optimization unit estimates the highest values ​​of the optimization parameters for a sequence that stops the turbine in years 16, 17, and 18, and operates the turbine in a less aggressive mode in the last two years when prices are high. Figure 5 It can be seen that when operated in a more aggressive mode in the 19th year, the wind turbine reaches the end of its life and fails in the 20th year.

[0105] It should be clear that, in addition to calculating the optimized parameters for all possible combinations of patterns in the sequence, an optimization algorithm, such as a search algorithm or an algorithm from the field of reinforcement learning, can also be used to find the sequence of patterns that produces the highest optimized parameters. The above explanations apply accordingly to this method.

[0106] The results of the optimization unit 201, such as the determined one or more operating mode sequences and their associated estimated optimization parameters, can be selected and set for operating the wind turbine (for example, a sequence that meets the optimization goal, such as the sequence that generates the highest revenue, can be selected as the optimal sequence for operation), and / or the results provided by the optimization unit 201 can be used in further short-term optimization.

[0107] Based on one or more determined sequences of operating modes, the method may further determine an actual operating mode of the wind turbine and operate the wind turbine in the actual operating mode, wherein the actual operating mode is allowed to deviate from the operating mode of the sequence determined, for example, by the long-term optimizer to produce the highest / optimal estimated values ​​of the optimization parameters (e.g., a global optimal sequence). By means of such a short-term optimization, it may be determined whether there is another operating mode, different from the operating mode determined by the long-term optimization, which operating mode produces better optimized parameter values ​​(e.g., higher revenue) taking into account current conditions, for example, current values ​​of external parameters such as wind speed or electricity price, and / or current values ​​of operating parameters such as the current consumption life span.

[0108] According to a first aspect, current values ​​of external parameters may be used for short-term optimization. Such current values ​​may be values ​​received by unit 15 or obtained by short-term prediction, wherein the current values ​​may include current values ​​at the current time point and / or values ​​for a second time period in the near future, e.g. starting at the current time point and extending into the future, but shorter than the future first time period 301 and preferably shorter than the time interval 302 thereof. Thus, current values ​​of external parameters (e.g. electricity prices) may be more reliable than estimated values ​​of external parameters used in long-term optimization. Thus, for example, if the wind turbine is operated in a stop mode according to the determined optimal sequence in year 17 due to low electricity prices, e.g. Figure 4 and Figure 5 As shown in the example of , and the current electricity price has risen to much higher Figure 7 If the price is high enough, the short term optimization may select a different actual operation mode to operate the wind turbine, thereby avoiding the loss of energy production and revenue.

[0109] In this example, the short-term optimization may utilize the value of lifetime consumption estimated in the long-term optimization, as outlined above.

[0110] Figure 6A flow chart illustrating an example of a corresponding method for determining an actual operating mode is shown. Figure 3-5 The explained method determines a part of the sequence of operating modes, determining an optimal sequence for each of the different groups of candidate sequences (step S61), wherein the optimal sequence of the group is the sequence in which the optimization parameters best satisfy the optimization goal (e.g., as mentioned above, maximizing energy production or revenue). Such sequences may be referred to as "group optimal sequences". Each group comprises sequences defining the same operating mode for the current time interval and therefore for the current point in time, i.e. all sequences starting with the same mode (from the current point in time) may form a group. The candidate sequences within each group are different (differ in at least one mode for the time interval). In this way, an optimal sequence may be obtained for the operation of the wind turbine in each available mode at the current point in time. It should be noted that the corresponding data may be obtained from estimates performed by a long-term optimization, in particular from Figure 5 , which shows only a portion of the calculated candidate sequences. All sequences starting with stop mode 46 may form a group, and the one with the highest value of the optimization parameter is selected as the group optimal sequence. Sequences 305, 306, and 307 may be group optimal sequences for groups starting with stop mode, LE mode, and baseline mode, respectively. For different operation modes at the current time point, Figure 7 Graph 702 illustrates the operation mode om for each time interval of a group optimal sequence 745 (starting with PE10 mode 45), 744 (starting with PE5 mode 44), 742 (starting with baseline mode 42), 741 (starting with LE mode 41), and 746 (starting with stop mode 46). For each group optimal sequence 741-746, the estimated optimization parameters and the estimated lifetime consumption and thus end of life are known, as Figure 7 As shown in diagrams 702 and 703 of FIG. 703 illustrates the values ​​of the optimization parameter op over the lifetime of the wind turbine determined by the corresponding group optimal sequence for different operation modes in the current time interval (year 16). As can be seen, and as expected from the long-term optimization, the group optimal mode starting with the stop mode in the current time interval achieves the highest value of the optimization parameter.

[0111] In step S62, the dependence of the optimization parameter op on the value of the external parameter is estimated for each group optimal sequence. For example, a linear dependence may be assumed. Figure 8. The slope of the linear dependency can be determined depending on the respective operating mode. As an example, the optimization parameter op can be the revenue (e.g. NPV). The external parameter ep can be the electricity price. The revenue for the current time interval will depend on the electricity price and the energy production of the wind turbine in this time interval, which in turn will depend on the operating mode (and the wind conditions, but the wind conditions are the same for each group of optimal sequences). Modes with higher energy production will naturally lead to a stronger dependence of the revenue on the electricity price, so that the slope of the dependency can be determined by the energy production estimated for the respective operating mode. Furthermore, from the estimated value of the external parameter used in the long-term optimization, the optimization parameter for this external parameter value is known. Knowing this value and the slope of the linear dependency, it is possible to estimate how the current value of the external parameter affects the optimization parameter op for each candidate optimal sequence, such as Figure 8 As shown in the diagram 801 of . Therefore, for the current time interval, different linear dependencies 841-846 between the optimization parameters and the external parameters for different corresponding groups of optimal sequences 741-746 are obtained.

[0112] As can be seen in the diagram 801, the electricity price has no effect on the optimization parameters of the group optimal sequence 746 / 846, because the sequence defines a stop mode for the current time interval, so that no energy is produced (and no revenue is generated, no matter what the energy price is). On the other hand, for the group optimal sequence 745 / 845 that starts with an aggressive mode (e.g., PE10), there is a strong dependence on the electricity price. As can be seen from the diagram 801, depending on the current value of the electricity price, different group optimal sequences will produce the highest values ​​of the optimization parameters.

[0113] In step S63, the current value of the external parameter is obtained. This value may be received, for example, via the communication interface 15, for example from an external service provider, or may be measured, for example, by the sensor 20 (e.g., the current wind speed). In step S64, based on the current value and the (estimated linear) dependency between the optimization parameter and the external parameter (e.g., Figure 8 ), the adjusted optimization parameters for each group optimal sequence are estimated. The term "adjusted optimization parameter" means that the value corresponds to the optimization parameter obtained from the long-term estimation, which is adjusted for the difference between the estimated value and the current value of the external parameter. For example, for the current value of the external parameter of 10, the optimization parameter value for ep=10 can be obtained from the chart 801 for each group optimal sequence. It should be clear that obtaining the adjusted optimization parameters is performed digitally by the control system 10 (i.e., no chart is required).

[0114] In step S65, the optimal sequence of the group whose adjusted optimization parameters satisfy the optimization target (eg, have the highest value) is determined. Figure 8In the example, for ep < 10 EUR / MWh, the group-optimal sequence 746 that defines the stop mode for the current time interval yields the highest value of op; for 10 EUR / MWh < ep < 15 EUR / MWh, sequence 741 yields the highest op (starting with LE); for 15 EUR / MWh < ep < 20 EUR / MWh, sequence 742 yields the highest op (starting with the baseline); for 20 EUR / MWh < ep < 25 EUR / MWh, sequence 744 yields the highest op (starting with PE5); and for ep > 25 EUR / MWh, sequence 745 yields the highest op (starting with PE10).

[0115] In step S66, the operating mode defined by the selected group-optimal sequence for the current time interval is set as the actual operating mode. For example, for ep = 22 EUR, the group-optimal sequence 744 yields the highest value of the optimization parameter, and the operating mode 44 (PE5 mode) defined by this sequence 744 for the current time interval is set as the actual operating mode. Then, in step S67, the wind turbine operates in this actual operating mode.

[0116] Thus, the method allows the actual operating mode to be determined in such a way that the optimization parameter is optimized in the long term, while allowing deviations from this long-term strategy to exploit short-term effects, in particular the deviation between the estimation of the external parameter (on which the long-term optimization is based) and the current value of the external parameter. Thus, the short-term optimization can select the actual operating mode that yields the best optimization result, such as generating the maximum revenue (e.g., NPV). This actual operating mode can be updated repeatedly within the time interval, for example when an updated current value of the external parameter is obtained or with the periodic updates mentioned above.

[0117] It should be clear that the method can be carried out not only by using the optimization parameter in the form of revenue, but also other optimization parameters can be envisaged, such as energy production. In this case, the external parameter may be the wind speed. Thus, based on the current value of the wind speed, the adjusted energy production can be determined for each group-optimal sequence, and the group-optimal sequence that yields the highest energy production can be selected, and the actual operating mode can be determined.

[0118] Additionally or alternatively, the method may also include using the method described above according to the second aspect to determine the actual operating mode. The determination of the actual operating mode may include, for example, determining the operating mode according to the first aspect (e.g., as described with respect to Figure 6-8The method of the first aspect is to determine the operating mode according to the second aspect, when the deviation between the current value of the consumption life span and the expected consumption life span (e.g. defined by the determined sequence of operating modes) exceeds a predetermined (static or dynamic) value. In this way, it can be ensured that the life span consumption of the wind turbine is not excessive, for example during a long period of very high energy prices, during which the method of the first aspect may tend to operate the wind turbine very aggressively. When the deviation falls below a second predetermined (dynamic or static) value, the operation can continue according to the first aspect.

[0119] When operating the wind turbine according to the second aspect, a deviation in the consumption of the lifetime may be determined for the current point in time (e.g. by using a forward or backward calculation of the determined sequence), and the probability of selecting a specific operating mode may be made dependent on the deviation, in particular in order to reduce the deviation. In this case, the determined sequence may be, for example, a global optimal sequence or a currently selected group optimal sequence. Thus, both aspects interact synergistically to ensure that the lifetime consumption is not excessive, while at the same time maximizing the optimization parameters taking into account the current environmental conditions (e.g. electricity price or wind speed).

[0120] Although specific embodiments are disclosed herein, various changes and modifications may be made without departing from the scope of the invention. The present embodiments should be considered in all aspects as illustrative and not restrictive, and all changes coming within the meaning and equivalent range of the appended claims are intended to be included therein.

Claims

1. A method of operating a wind turbine (100), wherein: The wind turbine (100) is operable in a plurality of different operating modes (41-46), the plurality of different operating modes differing in at least one of lifetime consumption of the wind turbine and energy production of the wind turbine, wherein the method comprises: In the optimization process, a sequence (305; 741-745) of the operating modes for a future time period (301) is determined, wherein determining the sequence (305; 741-745) comprises: for the future time period (301) and for each of a plurality of different sequences comprising different combinations of operating modes, estimating an optimization parameter (op) based at least on an estimated lifetime consumption and an estimated energy production associated with operations in the operating mode of the respective sequence, wherein the estimation of the optimization parameter (op) is also based on at least one estimated external parameter (ep); Get the current value of at least one external parameter (ep); Determining an actual operation mode of the wind turbine for a current time point, wherein determining the actual operation mode comprises: estimating adjusted optimization parameters for each of a plurality of different sequences of operation modes defining different operation modes (41-47) at the current point in time and for which the optimization parameters have been estimated during the optimization process, wherein, for a current value of the at least one external parameter (ep), the adjusted optimization parameter is estimated for the respective sequence of operating modes by adjusting the optimization parameter estimated for the respective sequence during the optimization process, and Also included is selecting one of the different operating modes (41-47) as the actual operating mode based on the estimated adjusted optimization parameters; and The wind turbine is operated in the determined actual operating mode.

2. The method according to claim 1, wherein: The obtaining of current values, determining the actual operation mode and operating the wind turbine in the determined actual operation mode are repeatedly performed in the optimization process using the same optimization parameters estimated for the plurality of different operation mode sequences.

3. The method according to claim 1 or 2, wherein: Selecting one of different operation modes as the actual operation mode based on the estimated adjusted optimization parameters includes: selecting an operation mode in which the operation at the current time point produces the adjusted optimization parameters that meet the optimization target, particularly an operation mode in which the adjusted optimization parameters have a maximum value or a minimum value.

4. A method according to any one of the preceding claims, wherein: Estimating the adjusted optimization parameters comprises: For different operation mode sequences (741-746) distinguished at least by their operation mode (41-47) at a current point in time, the dependence (841-846) of the optimization parameter (op) obtained for the corresponding sequence during the optimization process on the value of the at least one external parameter (ep) is estimated, and the adjusted optimization parameter of each of the sequences is estimated using the dependence for the current value of the at least one external parameter obtained.

5. A method according to any one of the preceding claims, wherein: The multiple different operation mode sequences include multiple groups, each group includes multiple different operation mode candidate sequences, wherein the sequences within the same group include the same operation mode (41-46) for the current time interval (302) of the future time period (301), wherein the sequences of different groups include different operation modes for the current time interval, and wherein, in the optimization process, the optimization parameter (op) is estimated for each different candidate sequence within each group.

6. The method according to claim 5, wherein: Determining the operation mode sequence for the future time period (301) includes: determining the optimal sequence (741-746) for each different group by selecting candidate sequences in which the optimization parameters meet the optimization objectives from the corresponding groups, wherein the adjusted optimization parameters are estimated for each of the optimal sequences (741-746) of the group.

7. The method according to claim 6, wherein: For each group of optimal sequences (741-746), the dependence of the optimization parameters (op) on the value of the at least one external parameter (ep) is estimated (841-846) to determine adjusted optimization parameters for the corresponding optimal sequence.

8. A method according to any one of the preceding claims, wherein: Estimating the adjusted optimization parameter includes exploiting a linear dependency (841-846) or a non-linear dependency between a value of the optimization parameter and a value of the at least one external parameter.

9. The method according to claim 7 or 8, wherein: The slope of the dependency (841-846) or another parameter is determined to depend on an operating parameter estimated for the operating mode (41-46) at the current point in time at which the adjusted optimization parameter is estimated, wherein the operating parameter is preferably an energy production estimated for the corresponding operating mode (41-46).

10. A method according to any one of the preceding claims, wherein: Estimating the adjusted optimization parameters for the corresponding sequence of operating modes includes: obtaining optimization parameters estimated during the optimization process for a sequence (741-746), the sequence (741-746) comprising a corresponding operation mode (41-46) for a current time interval, the estimation being based on the at least one estimated extrinsic parameter for the current time interval; and The adjusted optimization parameter is determined by adjusting the obtained optimization parameter using a current value of the at least one external parameter and a dependency between the optimization parameter and the at least one external parameter estimated for the current time interval.

11. The method according to claim 10, further comprising estimating an energy production of the wind turbine (100) in the current time interval when operating in the respective operating mode (41-46), and estimating a dependency between the optimization parameter (op) and the at least one external parameter (ep) based at least on the energy production in the current time interval.

12. A method according to any one of the preceding claims, wherein: The at least one external parameter (ep) is at least one of wind speed and electricity price.

13. A control system for operating a wind turbine, wherein: The control system (10) is configured to operate the wind turbine (100) in a plurality of different operating modes (41-46), the plurality of different operating modes differing in at least one of lifetime consumption of the wind turbine (100) and energy production of the wind turbine (100), wherein the control system (10) is configured to perform a method according to any of the preceding claims.

14. A wind turbine, wherein: The wind turbine (100) comprises a control system (10) according to claim 13, wherein the control system (10) is configured to control the operation of the wind turbine (100) in a plurality of different operating modes (41-46).

15. A computer program for controlling the operation of a wind turbine (100), wherein: The computer program comprises control instructions which, when executed by a processing unit (11) of a control system (10) controlling the operation of the wind turbine (100), cause the processing unit (11) to perform a method according to any one of claims 1 to 12.

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