EXTREME LOAD CONTROL

Abstract

Methods for calculating a maximum safe over-rated power demand for a wind turbine operating in non-standard conditions include the steps of determining a value indicative of a risk of exceeding an ultimate design load during operation in a standard operating condition, and establishing a maximum over-rated power demand corresponding to a maximum power that the turbine may produce under the non-standard operating condition without incurring an increased risk of exceeding the ultimate design load, with respect to operation in the standard condition. A method of over-rating a wind turbine, a wind turbine controller, a wind turbine and a wind power plant are also claimed.

Claims

1. A method for calculating a maximum safe over-rated power demand for a wind turbine operating in a non-standard operating condition, the method comprising the steps of: determining a value indicative of a risk of exceeding an ultimate design load during operation in a standard operating condition; and establishing a maximum over-rated power demand corresponding to a maximum power that the turbine may produce under the non-standard operating condition without incurring an increased risk of exceeding the ultimate design load, with respect to operation in the standard condition.

2. The method of claim 1, wherein establishing the maximum over-rated power demand comprises: determining, for each of a plurality of over-rated power demands, a value indicative of the risk of exceeding the ultimate design load during operation in the non-standard operating condition; and selecting the largest of the plurality of over-rated power demands for which the determined risk is not greater than the determined risk of exceeding the design load in the standard operating condition.

3. The method of claim 1, wherein the operating condition comprises one or more of wind speed, yaw error, air density, vertical wind shear, horizontal wind shear, inflow angle and turbulence intensity.

4. The method of claim 1, wherein determining the value indicative of the risk of exceeding the ultimate design load comprises calculating a maximum value of the ultimate load experienced by the turbine during operation in an extreme event.

5. The method of claim 4, wherein the extreme event is an extreme design load case.

6. The method of claim 1, wherein determining the value indicative of the risk of exceeding the ultimate design load comprises: calculating a maximum value of the ultimate load experienced by the turbine during operation in each of a plurality of extreme events; and selecting the largest of the calculated maximum ultimate load values.

7. The method of claim 1, wherein the ultimate load comprises one or more of a tower base over-turning moment, a flapwise bending moment on a blade of the wind turbine, an edgewise bending moment on a blade of the wind turbine, a torque of the drive train, a tilt bending moment on a rotor of the wind turbine, and a yaw-wise bending moment on a rotor of the wind turbine.

8. The method of claim 4, wherein calculating the maximum value of the ultimate load experienced in the extreme event comprises: performing a plurality of simulations of operation of the turbine in the extreme event; for each of the simulations, establishing the maximum value of the ultimate load experienced during the extreme event; and selecting the largest of the plurality of established maximum values.

9. The method of claim 4, wherein calculating the maximum value of the ultimate load experienced in the extreme event comprises: performing a plurality of simulations of operation of the turbine in the extreme event; for each of the simulations, establishing the maximum value of the ultimate load experienced across the extreme event; and calculating the average of the plurality of established maximum values.

10. The method of claim 8, wherein each of the plurality of simulations is characterised by different wind series starting points.

11. The method of claim 1, wherein determining the value indicative of the risk of exceeding the ultimate design load comprises: calculating a baseline value of the ultimate load, the baseline value comprising the maximum value of the ultimate load experienced by the turbine in an extreme design load case during standard operation; and for each of a plurality of time intervals in the extreme design load case, simulating operation of the turbine for a pre-determined time period, to determine a probability of exceeding the baseline ultimate load value.

12. A method of populating a look-up table for a wind turbine controller, the method comprising calculating a maximum safe over-rated power demand for a wind turbine for each of a plurality of non-standard operating conditions of the wind turbine, the calculating comprising: determining a value indicative of a risk of exceeding an ultimate design load during operation in a standard operating condition; and establishing the maximum over-rated power demand corresponding to a maximum power that the turbine may produce under the non-standard operating condition without incurring an increased risk of exceeding the ultimate design load, with respect to operation in the standard condition.

13. A method of over-rating a wind turbine, the method comprising: determining an operating condition of the wind turbine; and determining a maximum safe over-rated power demand for the wind turbine given the determined operating condition by interrogating a look-up table; wherein the look-up table is populated by calculating a maximum safe over-rated power demand for the wind turbine for each of a plurality of non-standard operating conditions of the wind turbine, the calculating comprising: determining a value indicative of a risk of exceeding an ultimate design load during operation in a standard operating condition; and establishing a maximum over-rated power demand corresponding to a maximum power that the turbine may produce under the non-standard operating condition without incurring an increased risk of exceeding the ultimate design load, with respect to operation in the standard condition.

14. The method of claim 13, further comprising reducing the determined maximum safe over-rated power demand in response to a warning from a condition monitoring system for the wind turbine.

15. The method of claim 13, further comprising the steps of: establishing a frequency of exceedance of a load level for a wind turbine component that is close to an ultimate load level for the wind turbine component; and reducing the determined maximum safe over-rated power demand based on the established frequency.

16. The method of claim 13, further comprising the steps of: establishing a frequency of triggering of one or more ultimate load control features for the turbine; and reducing the determined maximum safe over-rated power demand based on the established frequency.

17. (canceled)

18. (canceled)

19. (canceled)

20. A controller for a wind power plant, the controller configured to perform an operation of over-rating a wind turbine in the wind power plant, the operation comprising: determining an operating condition of the wind turbine; and determining a maximum safe over-rated power demand for the wind turbine given the determined operating condition by interrogating a look-up table; wherein the look-up table is populated by calculating a maximum safe over-rated power demand for the wind turbine for each of a plurality of non-standard operating conditions of the wind turbine, the calculating comprising: determining a value indicative of a risk of exceeding an ultimate design load during operation in a standard operating condition; and establishing a maximum over-rated power demand corresponding to a maximum power that the turbine may produce under the non-standard operating condition without incurring an increased risk of exceeding the ultimate design load, with respect to operation in the standard condition.

21. (canceled)

22. (canceled)

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings in which:

[0033] FIG. 1A is a schematic front view of a conventional wind turbine;

[0034] FIG. 1B is a schematic representation of a conventional wind power plant comprising a plurality of wind turbines;

[0035] FIG. 2 is a graph illustrating a conventional power curve of a wind turbine;

[0036] FIG. 3 is a schematic of a wind turbine controller arrangement;

[0037] FIG. 4 illustrates in schematic overview the operation of the control system of the present invention according to one embodiment;

[0038] FIG. 5 is a flowchart outlining the configuration of the controller of FIG. 3 according to one embodiment of the invention;

[0039] FIG. 6 is a graph illustrating the wind speed profile of a typical EOG event; and

[0040] FIG. 7 is a schematic of a wind turbine control arrangement according to further embodiments of the present invention.

DETAILED DESCRIPTION

[0041] A common approach to wind turbine design and, more particularly, to engineering a turbine to withstand the various loads it is expected to experience, is to consider in turn the various states or situations in which a turbine may be at any given time. Each of these so-called load cases represents a design situation characterised by a set of loads and other conditions to be taken into account. The design load cases set out in IEC 64100-1 are given in Table 1 below. In the table, the letter U in the penultimate column designates a load case analysed as an ultimate load case, and F a fatigue load case.

[0042] As mentioned above, fatigue load cases are not the direct subject of this invention. The ultimate load cases can, for the purposes of the present discussion, be conveniently divided into three main classes. A first class includes non-operational load cases, such as the extreme (or 50-year) wind speed model (EWM) considered within the Parked situation. These load cases are unaffected by a decision to over-rate the turbine, and so are ignored in the present context. Of the operational load cases, some are characterised by maximum component loads that are not a function of the wind condition. A fault, such as a generator short circuit, at nominal power would be one example (DLC 2.2). Again, the present invention is not concerned with those situations.

[0043] A third group of ultimate load cases includes those operational situations in which the magnitude of the maximum component loads to which the turbine is subjected is a function of the wind conditions. Examples of load cases falling within this third category and with which the present invention is concerned include: load case 1.3, power production during extreme turbulence (ETM); load case 1.4, power production with an extreme change of wind direction (ECD); and load case 3.2, startup with an extreme operating gust (EOG). Embodiments of the invention provide a control strategy that ensures that a turbine can be over-rated without materially increasing the risk of failure due to ultimate loads of this type.

TABLE-US-00001 TABLE 1 Design Load Cases (or DLCs) (taken from IEC 61400-1) Partial Type of safety Design situation DLC Wind condition Other conditions analysis factors 1) Power production 1.1 NTM V.sub.in < V.sub.hub < V.sub.out For extrapolation of U N extreme events 1.2 NTM V.sub.in < V.sub.hub < V.sub.out F * 1.3 ETM V.sub.in < V.sub.hub < V.sub.out U N 1.4 ECD V.sub.hub = V.sub.r 2 m/s, U N V.sub.r, V.sub.r + 2 m/s 1.5 EWS V.sub.in < V.sub.hub < V.sub.out U N 2) Power production 2.1 NTM V.sub.in < V.sub.hub < V.sub.out Control system fault or U N plus occurrence of loss of electrical fault network 2.2 NTM V.sub.in < V.sub.hub < V.sub.out Protection system or U A preceding internal electrical fault 2.3 EOG V.sub.hub = V.sub.r 2 m/s External or internal U A and V.sub.out electrical fault including loss of electrical network 2.4 NTM V.sub.in < V.sub.hub < V.sub.out Control, protection, or F * electrical system faults including loss of electrical network 3) Start up 3.1 NWP V.sub.in < V.sub.hub < V.sub.out F * 3.2 EOG V.sub.hub = V.sub.in, U N V.sub.r 2 m/s and V.sub.out 3.3 EDC V.sub.hub = V.sub.in, U N V.sub.r 2 m/s and V.sub.out 4) Normal shut down 4.1 NWP V.sub.in < V.sub.hub < V.sub.out F * 4.2 EOG V.sub.hub = V.sub.r 2 m/s U N and V.sub.out 5) Emergency shut 5.1 NTM V.sub.hub = V.sub.r 2 m/s U N down and V.sub.out 6) Parked (standing 6.1 EWM 50-year occurrence U N still or idling) period 6.2 EWM 50-year occurrence Loss of electrical U A period network connection 6.3 EWM 1-year occurrence Extreme yaw U N period misalignment 6.4 NTM V.sub.hub < 0.7 V.sub.ref F * 7) Parked and fault 7.1 EWM 1-year occurrence U A conditions period 8) Transport, assembly, 8.1 NTM V.sub.maint to be U T maintenance stated by the and repair manufacturer 8.2 EWM 1-year recurrence U A period

[0044] Over-rating implementations are described in earlier publications of the applicant (refer, for example, to GB 2491548). Moreover, the specific manner in which over-rating is achieved is not critical to the present invention. A detailed discussion of over-rating control is, therefore, not required. Nevertheless, an example of an over-rating method will be discussed briefly for ease of understanding of the invention.

[0045] FIG. 3 shows a schematic example of a turbine control arrangement in which an over-rating controller 301 generates an over-rating control signal that can be used by wind turbine controllers (not shown) to apply over-rating to the turbine. The over-rating control signal may be generated depending upon the output of one or more sensors 302/304 that detect operating parameters of the turbine and/or local conditions such as wind speed and direction. The over-rating controller 301 comprises one or more functional control modules that may be used in various aspects of over-rating control. Additional functional modules may be provided, the functions of modules may be combined and some modules may be omitted. The over-rating controller may be realised within a controller for a given wind turbine, or may in other embodiments form part of the central PPC for a wind power plant, configured to control the over-rating of one or more of the turbines within the plant.

[0046] Design loads calculated in accordance with one of the standard IEC Classes (IEC1A, IEC1B, and so on), are typically conservative on some sites, due to the variation in annual mean wind speed and turbulence intensity from site to site. For example, a turbine that has been designed to IEC2, for which the design annual mean wind speed is 8.5 ms.sup.1, will often be deployed on sites with annual mean wind speeds of below 8.0 ms.sup.1. In such cases, there is a gap between the design loads and the more benign loads actually experienced in operation. Over-rating exploits this gap.

[0047] The LUC 305 may use lifetime usage estimators (LUEs) to control the lifetime of the associated components. This control function compares the current estimate of component life used with a target value for life use at the current time in the life of the turbine. The amount of over-rating applied to the wind turbine is then manipulated to limit the rate of life use. The actuating signal for the LUC function at any time is the difference between the estimate of component life used and the target value for life use at that time.

[0048] Over-rating causes the power demand for the turbine to be increased in high winds until either an operating limit specified by an operational constraint (such as a temperature) is reached, or until an upper power limit is reached that has been set to prevent component design loads from being exceeded. Operational constraints, implemented by operational constraints control module 306, limit the possible over-rating power demand as a function of various operating parameters. For example, where a protection function is in place to initiate shut down when the gearbox oil temperature exceeds 65 C. as mentioned above, an operational constraint may dictate a linear decrease in the maximum possible over-rating set point signal as a function of gearbox oil temperature for temperatures over 60 C., with no over-rating possible (i.e., a power set-point signal equal to the nominal rated power) at 65 C.

[0049] Currently, however, wind turbine control typically respects ultimate design loads absolutely. The present inventor has appreciated that those, as well as the fatigue design loads, represent a conservative approach, and that an increase in power output that might initially be identified and excluded as dangerous on the basis of those design loads might, by considering the full range of operating conditions in more detail, in fact be seen to carry a risk of failure that is no greater than that calculated under the standard set of load cases in IEC 64100-1.

[0050] Accordingly, the present invention provides an extreme load controller for use in over-rating, and FIG. 4 illustrates in schematic overview the operation of that controller within a global over-rating control scheme according to one embodiment.

[0051] As shown in FIG. 4, the over-rating controller 401 for wind turbine 40 in this example includes, in addition to control modules 303, 305, 306 described above, an extreme load controller 410 that is arranged to receive and to process measurements taken from sensors 402 onboard the turbine. As discussed in further detail below, the output of the extreme load controller is a power demand P.sub.D.sub.ext that is passed to minimum (or MIN) function 308. As also shown in FIG. 3, MIN function 308 also receives further power demands from the remaining over-rating control modules; and its output may be fed to an overall turbine power demand MIN function 309 to determine the prevailing power demand, the definitive power set point to be realised by the controller 420 for turbine 40.

[0052] In this example, the over-rating controller 401 receives measurements indicative of the following parameters: [0053] 1. wind speed; [0054] 2. yaw error; [0055] 3. air density; [0056] 4. vertical wind shear; [0057] 5. horizontal wind shear; [0058] 6. inflow angle; and [0059] 7. turbulence intensity.

[0060] Wind sensors are commonly adopted on large commercial wind turbines, and can be used to measure the wind speed directly.

[0061] Yaw error refers to the misalignment of the turbine nacelle with respect to the incoming wind direction, and can be derived from measurements of wind direction.

[0062] Air density may be estimated straightforwardly based on a knowledge of site elevation and ambient temperature, the latter being measured by conventional temperature sensors on board the turbine.

[0063] Vertical and horizontal wind shear can be estimated from measurements of blade loading by appropriate blade load sensing systems. For example, a blade load sensing system that measures blade root flapwise and edgewise strain signals at 50 Hz gives blade load measurements that can be used, together with measurements of the given blade's pitch angle and azimuth, to estimate the wind speed at the various azimuthal positions as it rotates. If the rotor rotates at 10 rpm, a 50 Hz measurement frequency gives 300 measurements per full blade rotation. This gives sufficient resolution to make an estimate of horizontal and vertical wind shear across the rotor.

[0064] The inflow angle of the incoming wind refers here to the angle of the incoming wind relative to wind travelling parallel to flat land; thus, for example, wind approaching a turbine up a steep slope has a positive inflow angle. The inflow angle may be estimated in one of several ways. It may be measured directly, for example using conventional tower- and/or nacelle-mounted anemometry or more sophisticated sensors such as LIDAR. Alternatively, measurements of wind direction may be used in conjunction with a look-up table for a turbine's geographical location to give inflow angles for each direction. The look-up table in this case will be constructed using topographic (contour) data for the land around the turbine.

[0065] Finally, turbulence intensity, defined as the ratio of the standard deviation of the wind speed to the mean wind speed in a certain averaging time, may again be measured or estimated using nacelle-mounted or ground-based LIDAR, or nacelle or spinner anemometer readings.

[0066] The extreme load controller 410 includes a look-up table which, given the measurements (or estimations) just outlined, may be used to determine the maximum power at which the turbine may be run without incurring an increased risk of exceeding one or more relevant ultimate design loads.

[0067] Specifically, this method exploits changes in operating conditions which give rise to a significant lowering of extreme loads, and in those cases the power level is increased until, for each load variable, the highest load reaches the level experienced in standard operation conditions. In the examples below, it is assumed that the design loads are driven by operational, rather than non-operational, load cases. Examples include:

Air Density

[0068] Power in the few stream wind is proportional to air density and the calculations in IEC 61400-1 are typically carried out for an air density of 1.225 kgm.sup.3. However, wind power plants on certain northern hemisphere locations experience air densities as low as 0.9 kgm.sup.3 under hot summer conditions, with annual average levels as low as 1.05 kgm.sup.3. The power in the wind incident upon the turbine in this case is 14% lower; therefore, extreme loads for a given load case are substantially lower, and the power output in this example can safely be raised while giving no higher risk of ultimate load failure than for operation at 1.225 kgm.sup.3.

Turbulence

[0069] Turbulence intensity affects extreme loads in IEC 61400-1 through the calculations in the ETM. Thus, a turbine with a low reference turbulence intensity relative to the original design can have its power output safely raised without taking on a higher risk of ultimate load failure than that of an identical turbine operating on a site with turbulence intensity equal to the design value.

[0070] The overall reference turbulence intensity of a given site depends on a range of factors. All of the following can give rise to a reference turbulence intensity that is significantly lower than the design value: [0071] topography A turbine that has been designed for IEC Turbulence Class A, but is installed on a flat or nearly flat site, is likely to be over-designed with respect to the extreme loads; [0072] inter-turbine spacings A turbine that has been designed for IEC Turbulence Class A, but is installed on a site with very large inter-turbine spacings (for example, greater than 10 rotor diameters in the prevailing wind direction(s)) is more likely to be over-designed with respect to the extreme loads than a turbine with small inter-turbine spacings (for example, four rotor diameters or less in the prevailing wind direction(s)); and [0073] atmospheric turbulence intensity A turbine that stands on a wind power plant location that is an area that benefits from generally low atmospheric turbulence, and hence a low reference turbulence intensity, I.sub.ref in IEC 61400-1 (for example, a coastal site with prevailing wind direction(s) coming from the sea) is more likely to be over-designed with respect to the extreme loads than a turbine in an area with high atmospheric turbulence (such as a site surrounded by mountains).

[0074] The look-up table may be populated by means of off-line simulations before the turbine 40 begins operation. Some alternative approaches to population of the look-up table in accordance with the invention will be described in detail with reference to the flowchart of FIG. 5.

Approach 1

[0075] A first approach to population of the look-up table follows the flow-chart outlined schematically in FIG. 5.

[0076] In a first step 502, the baseline extreme loads L.sub.B are identified by performing simulations of turbine operation under all of the extreme load cases of IEC 61400-1, i.e. those marked with a U in the column headed Type of analysis in Table 1. The output baseline loads are suitably stored, for example in a linear (or one-dimensional) array, for later use. These initial calculations, which assume the same, standard operating parameters used to inform the design of the turbine (in particular, a rated power output), constitute a first phase of the method of this approach.

[0077] In a second phase, the wind conditions that are not fixed as part of a load case are identified and are given new values, which may in operation cause extreme loads to be reduced. For example, air densities lower than 1.225 kgm.sup.3 generally lead to lower ultimate loads; as do reduced levels of vertical wind shear. Thus, at step 504, the wind conditions used in the simulations are set to represent a first such alternative scenario, and the simulations are then repeated at step 506. The resulting maximum loads for this first set of alternative wind conditions, and again assuming a rated turbine power output, are recorded as a linear array L.sub.max.sub._.sub.P.sub.R. Some or all of these loads are likely to be lower than the baseline loads L.sub.B.

[0078] Next, at step 508, with the wind conditions now held at the values defined in step 504, the power demand P.sub.D of the turbine is incremented by, for example, 1% of the rated power P.sub.R. The load cases are run once again at step 510 so as to calculate the maximum loads L.sub.max.sub._.sub.P.sub.D resulting from this over-rated operation at more benign wind conditions.

[0079] These loads are then compared at step 512 with the baseline loads calculated at step 502.

[0080] If none of the baseline loads is found to be exceeded, the method returns to step 508: the power demand of the turbine is incremented; the simulations repeated; and the maximum loads compared with their baseline equivalents. This process is iterated until one of the baseline loads is found to be exceeded by a turbine operating in the given wind conditions and at the assumed over-rated power demand level. Once this is found to happen, the highest safe power demand P.sub.max (that is, the maximum power demand at which the turbine can run without exceeding any baseline extreme loads) is recorded.

[0081] The method then returns, in a third phase, to step 504: the iterative process just described is repeated for a further set of alternative wind conditions. When there are no further wind conditions to consider (decision step 514), the method ends.

[0082] The result of the process of FIG. 5 is a look-up table documenting the highest power demand that can safely be used for each combination of wind conditions; that is, the greatest possible operational envelope for over-rating control which does not materially increase the risk of ultimate load failure with respect to operation at rated power under standard conditions. The set of wind conditions to consider may, for example, consist of all permutations of decrementing values of air density, turbulence intensity and vertical wind shear exponent (this last parameter remaining greater than zero), each through a suitable range.

Approach 2

[0083] A variant of the method outlined in FIG. 5 may eliminate or reduce possible variations in calculated extreme loads that arise in individual load cases as the power demand is varied. Possible variations can arise due to the specific turbine operating state at a specific time (or times) when a potentially high load event (or events) occurs during a given extreme load case, specifically differences in the azimuth of each blade, the pitch angle of each blade and similar parameters. These parameters are a function of, amongst other variables, the turbine power demand. The maximum loads can be sensitive to azimuth and similar parameters, and can therefore be sensitive to the wind series starting point in a given load case.

[0084] In this embodiment, each load case is run (at steps 502, 506 and 510) multiple times (for example, 10 times), each of those simulations being characterised by different wind series starting points. In one example, the maximum extreme loads recorded at each step are then taken to be the largest values obtained across the 10 runs. Alternatively, the average of the maxima obtained may be used.

Approach 3

[0085] The approaches just described check that the risk of exceeding one or more ultimate design loads is not increased as a result of over-rating, in that they ensure that the loads themselves are not higher than those experienced at rated power output and under standard conditions.

[0086] The simulations of IEC 64100-1, which may be used in the methods just described, adopt a deterministic approach to load calculation for all extreme load cases with the exception of 1.3 (which uses the ETM). A further approach within the scope of the invention replaces those calculations with probabilistic or stochastic estimations. Here, actual probabilities that one or more design loads will, in fact, be exceeded are calculated both for standard and for over-rated operation. These probabilities are then compared so as to establish the maximum safe operating power for a turbine, given a presumed set of wind conditions. It is assumed here that Approach 1 or Approach 2 is used for load case 1.3.

[0087] The approach will be described in detail in the context of the extreme event defined in load case 3.2 of table 1 (startup with an EOG).

[0088] The EOG, illustrated schematically in FIG. 6, is defined to be the worst gust to be expected at a turbine site during a start or stop over a fifty-year period. A gust of this magnitude can impart a considerable thrust on the turbine rotor, and risks causing serious damage to the turbine. As shown in FIG. 6, gusts of this sort are typically characterised by comparatively short timescales, making it difficult to implement a protective control function in time to prevent or mitigate that damage. In addition and as also illustrated by the graph of FIG. 6, EOGs are often preceded by a dip in wind speed. This deceives the turbine controller, which may attempt to increase the rotational speed of the turbine by adjusting the pitch angle of the blades to extract more power from the wind. This can make adequate, and adequately swift, protective action even more difficult to realise.

[0089] Furthermore, the eventual combination of an EOG with a grid failure may cause the turbine to undergo an emergency stop. Since such an emergency stop results in rotor thrust dropping quickly to zero, the arrival of an EOG may give rise to significant oscillations in the turbine tower.

[0090] Together, these considerations lead to the assumption that the EOG load case causes the maximum tower base over-turning moment (OTM), which is one of the loads that must be taken into account in designing the turbine. The maximum OTM that the tower of the non-over-rated turbine is equipped to withstand during this load case is defined as the baseline OTM. The look-up table may then be populated as follows.

Step 1

[0091] The method according to this approach begins as in Approach 2 with the calculation of the baseline extreme loads L.sub.B, by running each load case (including the ETM) multiple (for example, 10) times with a power demand P.sub.D equal to rated power P.sub.R. Again as in Approach 2, the baseline loads may be taken to be either the largest or the averages of the values obtained across the repeated runs.

Step 2

[0092] Next, and still with the turbine's power demand equal to rated power, each load case (apart from the ETM)for example the EOGis analysed as follows. [0093] a. First, the load case is run multiple times, each assuming a different azimuthal starting position of the turbine rotor. For example, 12 simulations of the load case may be performed: one assuming blade 1 to be pointing vertically upwards (i.e., 0); and the remaining 11 assuming the blade to begin 10 further around than the previous run (so that in the 12.sup.th simulation, blade 1 begins at 110). [0094] b. Each of the twelve simulations is then divided into suitable time slices. For example, an EOG lasting 10 s may be divided into 20 intervals, each of 0.5 s. [0095] c. At each of these defined times, the average of the turbine's key operating parameters is taken from across the twelve simulations to obtain an average value for the operating point for that time in the load case. These values include the pitch angles of each of the blades, the rotational speed of the rotor and the rotor torque. [0096] d. Using those average operating points, and with turbulence applied, a number of stochastic simulations of turbine operation are then performed. For example, the operating points obtained at step c may be input to 10 simulations, each with a different seed factor for the wind input and each of 10 minute duration. During each run, the various loads (for example, the OTM, the drive-train torque, and so on) are tracked and plotted as a function of time. The values of these loads may be logged at a frequency of 20 Hz, for example. [0097] e. Once the simulations are complete, the total time for which each load variable has been found to exceed its baseline value can be computed and converted into a probability of that occurrence. This could be done by calculating the probability for each of the 10 simulations individually, and then taking the average of those probabilities; or, equivalently, by summing the time for which the baseline load value is exceeded over all of the simulations and then dividing, in this case, by 100 minutes. As this probability is for non-over-rated operation, it is defined here as the baseline probability. [0098] f. Thus, for non-over-rated operation and for each pre-defined point in time during the given extreme load case, a baseline probability is obtained that each load variable will exceed the baseline value calculated at Step 1 above. These probabilities are then integrated over time to give a baseline probability that each load variable will exceed the respective baseline value over the course of the EOG.

[0099] This stochastic process is repeated for all load cases, with the exception of the ETM.

Step 3

[0100] The next step of this variant method follows steps 504 to 514 of the method of FIG. 5 but applying, rather than the deterministic simulations of IEC 61400-1, the probabilistic approach just described. Thus, a new set of wind conditions is assumed, which may cause loads to be reduced (step 504). To give a simple example, air density may be lowered to below 1.225 kgm.sup.3, with all other wind conditions fixed. At step 506 of FIG. 5, points a to f above are followed to obtain a probability that each load variable will exceed the baseline value over the course of an EOG under these revised conditions. The power demand is then incremented (step 508), and the probabilities calculated at each higher power level (510) until one load variable is found, for the given wind conditions, to exceed the baseline value with a probability greater than that with which it is exceeded during operation at rated power and under standard conditions. Once this is found to happen, the highest safe power demand P.sub.max (that is, the maximum output power at which the turbine can run without the probability exceeding the baseline probability) is recorded.

Step 4

[0101] Steps 1 to 3 are then repeated for all permutations of wind conditions of interest.

[0102] Thus, this process gives, for each foreseeable combination of wind conditions, the maximum power output level at which the turbine can operate without materially increasing the probability of exceeding one or more design loads with respect to the original operating strategy.

[0103] Thus, Approaches 1 to 3 each populate the look-up table with a maximum safe power demand as a function of wind conditions. Preferably, the power demand is interpolated between the discrete points for which it is calculated explicitly. The interpolation may be linear, for example.

[0104] Returning then to FIG. 4, during operation the extreme load controller 410 is able to discern, on the basis of the measurements received from the turbine 40, the maximum power demand at which the turbine can run without risking damage should an extreme event occur. Specifically, the controller looks up the maximum safe power demand as a function of each measurement received, and outputs this as a power demand signal P.sub.D.sub.ext. The extreme load controller receives input measurements and repeats this look-up process periodically. The extreme load controller may operate with a timestep of 1 second, for example.

[0105] As mentioned, the off-line calculations presented above take as a starting point the standard conditions of IEC 61400-1. Further embodiments of the invention may tailor the level of over-rating determined through those calculations by making use of real-time data relating to the operation of a given turbine. Thus, an over-rated power demand for that turbine may be derived that may be more appropriate in view of turbine-specific circumstances. Examples of methods in accordance with these embodiments will be discussed with reference to FIG. 7. As shown in FIG. 7, an extreme load controller 710 within an over-rating controller 701 according to the present embodiments may be an extension of the controller 410 discussed above with reference to FIG. 4. In particular, in addition to the look-up table 712 generated and consulted as described above, extreme load controller 710 may include one or both of two additional control modules 714 and 716, described below. (The over-rating controller modules 303, 305, 306 of FIGS. 3 and 4, which may be included in over-rating controller 701, are omitted here for simplicity of illustration, as indicated generically by the symbol). Similarly to the system of FIG. 4, the power demand P.sub.D.sub.ext output by the extreme load controller 710 is fed, together with the outputs of the remaining over-rating controller modules, to MIN functions 308 and 309 so as to determine the final power demand to be realised by turbine controller 720 in control of the turbine 70.

[0106] Further shown in FIG. 7 are measurements fed from turbine sensors 702 to look-up table 712 as described above, and further inputs to extreme load controller 710 that will be discussed below.

[0107] A first extension to the methods described herein makes use of the condition monitoring system (CMS) commonly in place on commercial wind turbines, and shown as 724 in FIG. 7. As is known, these systems monitor the condition of various wind turbine components (in particular, components of the drive train), issuing first a warning and subsequently an alarm whenever a fault causes one or more operational limits of those components to be reached. Where a fleet of turbines has been in operation for a substantial number of years, turbine component failure cases can be examined statistically and a correlation sought between CMS signal outputs and ultimate failures of specific components.

[0108] For example, a gearbox failure mode that is known to be caused by ultimate loads may correlate with given changes in the CMS's outputs in the days, weeks or months leading up to the failure. In such a case, the given change in CMS output may be used as an input from the turbine system to a CMS control action module 714 within the extreme load controller. If a warning is observed during operation, the CMS control action module may override the output of the look-up table 712, issuing a power demand that includes 0% over-rating so as to minimise the risk of damage to the component in question. Alternatively, the output of CMS control action module 714 may act upon that of look-up table 712 to reduce the over-rating demand P.sub.D.sub.ext that is fed to the turbine controller. For example, the CMS control action module may act to reduce the default over-rating demand signal determined on the basis of the look-up table alone by half, for example, from 110% of rated power to 105%; or by any other amount that is determined to be suitable based on the nature of the warning. This multiplication may be performed by logic block 708. In other embodiments, module 714 may receive the output of look-up table 712 as input, modify it in accordance with the control action determined on the basis of CMS output, and send this modified signal directly to block 708.

[0109] Similarly, a second extension to the methods described above makes use of a load control action module 716 to modify the output of look-up table 712 based on external loading conditions that are specific to the particular microsite at which a given turbine is situated. Again where a fleet of turbines has been in operation for a number of years, a statistical analysis can be applied to those cases in which a major component failure has taken place, the root cause of which was ultimate loading of the turbine.

[0110] In this case, a correlation may be sought between the given failure and inputs from load sensors 726, or from sensors whose output can be processed to give a real-time estimate of a component load. Similarly to the CMS control action described above, a correlation between the frequency with which a load crosses a level which is close to (for example, 80% of) the ultimate load level of the given component and eventual failure due to extreme loading can be used to inform the power demand issued by the extreme load controller 710. Specifically, when the real-time frequency of load level exceedances is calculated from the signals fed from load sensors 726 to the load control action module 716, that module may again act to override or otherwise to temper the power demand that results from looking up the suitable over-rated power level in look-up table 712.

[0111] A further option for the load control action module 716, which can be used in addition to, or instead of, the method just described, is to seek a correlation between the given failure and the actions or outputs of existing ultimate load control features 727 that may be in place to protect against high or extreme loads. One example of such a control feature is described in EP 2 655 875, and is designed to realise rapid, infrequent individual blade pitch control actions that keep blade extreme loads within design loads when a turbine is situated in complex terrain. Similarly to both the CMS control action and the load sensing action described above, a correlation between the frequency with which one or more of these load control features is triggered and eventual failure due to extreme loading can be used to inform the power demand issued by the extreme load controller. Specifically, when the real-time frequency of triggering is fed from load control features 727 to the load control action module 716, that module may again act to override or otherwise to temper the power demand that results from looking up the suitable over-rated power level in look-up table 712. The reductions in over-rating commanded by modules 714 and 716 may again be implemented either within these modules, or by logic block 708.

[0112] Where two or more of the extension modules just described are implemented, the power demand P.sub.D.sub.ext output by the extreme load controller 710 should be the lowest of the various power demands determined by each of those modules to be safe or appropriate given the conditions on the basis of which they operate. Accordingly, logic block 708 may include a MIN function, the task of which is to receive the compare the various safe power demands determined by modules 712, 714 and/or 716 and to pass the lowest of these as the maximum safe power demand P.sub.D.sub.ext, which may or may not be an over-rated power demand, to the global MIN function 308 of the over-rating controller 701.

[0113] The advantage of the CMS-based approach, the use of real-time load sensor input and the use of real-time load control trigger frequency input is that the operation of the extreme load controller 710 is adapted more precisely to the local site conditions of the location on which the turbine stands via the use of high-frequency real-time data, rather than relying solely on purely offline calculations.

[0114] It should be noted that embodiments of the invention may be applied both to constant-speed and to variable-speed turbines. The turbine may employ active pitch control, whereby power limitation above rated wind speed is achieved by feathering: rotating all or part of each blade to reduce the angle of attack. Alternatively, the turbine may employ active stall control, which achieves power limitation above rated wind speed by pitching the blades into stall, in the opposite direction to that used in active pitch control.