METHOD FOR OPERATING A CLUSTER OF WIND TURBINES

Abstract

A method for operating a cluster of a plurality of wind turbines is disclosed. For each of the wind turbines, one or more parameter values of a parameter being indicative for a condition occurring at the wind turbine are derived, based the measurements obtained by the wind turbine. In the case that a derived parameter value for a specific wind turbine exceeds a trigger value, measures for mitigating an effect of the condition at the specific wind turbine are initiated. The derived parameter values for the wind turbines of the cluster of wind turbines are compared to an expected distribution of the parameter values. In the case that a distribution of the derived parameter values differs from the expected distribution of the parameter values, the trigger value is adjusted, and the adjusted trigger value is subsequently applied when comparing the derived parameter values to the trigger value.

Claims

1. A method for operating a cluster of wind turbines, the cluster of wind turbines comprising a plurality of wind turbines, the method comprising the steps of: each of the wind turbines of the cluster of wind turbines obtaining one or more measurements related to the operation of the wind turbine, for each of the wind turbines, deriving one or more parameter values of a parameter being indicative for a condition occurring at the wind turbine, based on the measurements obtained by the wind turbine, comparing the derived parameter values for each wind turbine to a trigger value for triggering measures for mitigating an effect of the condition, based on the parameter, and in the case that a derived parameter value for a specific wind turbine exceeds the trigger value, initiating measures for mitigating an effect of the condition at the specific wind turbine, wherein the method further comprises the steps of: comparing the derived parameter values for the wind turbines of the cluster of wind turbines to an expected distribution of the parameter values, the expected distribution defining a mean value and a deviation, and in the case that a distribution of the derived parameter values differs from the expected distribution of the parameter values, adjusting the trigger value and subsequently applying the adjusted trigger value when comparing the derived parameter values to the trigger value.

2. A method according to claim 1, wherein the parameter being indicative for a condition occurring at the wind turbine is or comprises a rotor efficiency loss of the wind turbine.

3. A method according to claim 1, wherein the condition occurring at the wind turbine is ice formation at one or more wind turbine blades.

4. A method according to claim 3, wherein the step of initiating measures for mitigating an effect of the condition comprises initiating heating of the wind turbine blades of the specific wind turbine.

5. A method according to claim 1, wherein the step of initiating measures for mitigating an effect of the condition comprises derating the wind turbine and/or pausing the wind turbine and/or reducing a rotor speed of the wind turbine.

6. A method according to claim 1, wherein the steps of obtaining one or more measurements, deriving one or more parameter values, and comparing the derived parameter values to an expected distribution are performed substantially continuously.

7. A method according to claim 1, further comprising the step of deriving parameter values of one or more further parameters for the wind turbines of the cluster of wind turbines, and wherein the one or more further derived parameter values are taken into account for deciding whether or not to initiate measures for mitigating an effect of the condition.

8. A method according to claim 1, wherein the step of comparing the derived parameter values for the wind turbines of the cluster of wind turbines to an expected distribution of the parameter values comprises the steps of: deriving a distribution of the derived parameter values for the wind turbines of the cluster, and deriving a mean of the derived distribution, and comparing the mean of the derived distribution to the mean of the expected distribution of the parameter values, and wherein the step of adjusting the trigger value is performed on the basis of a difference between the mean of the derived distribution and the mean of the expected distribution.

9. A method according to claim 1, wherein the cluster of wind turbines forms part of a wind farm.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The invention will now be described in further detail with reference to the accompanying drawings in which

[0053] FIG. 1 shows a cluster of wind turbines being controlled in accordance with a method according to an embodiment of the invention,

[0054] FIG. 2 is a graph illustrating rotor efficiency loss of a wind turbine,

[0055] FIGS. 3-9 are graphs illustrating a method according to an embodiment of the invention related to ice formation on the wind turbine blades, and

[0056] FIG. 10 is a flow chart illustrating a method according to an embodiment of the invention.

DETAILED DESCRIPTION OF THE DRAWINGS

[0057] FIG. 1 shows a cluster 1 of wind turbines 2 in the form of a plurality of wind turbines 2, three of which are shown, forming part of a wind farm. The wind turbines 2 extract energy from the wind and generate electrical power, which is supplied to a power grid 3.

[0058] During operation, each of the wind turbines 2 obtains various measurements being relevant with regard to the operation of the individual wind turbine 2. Such measurements may include measurements of power produced by the wind turbine 2, measurements by various load sensors, measurements related to wind conditions, such as wind speed, wind direction, turbulence condition, etc., measurements related to other weather conditions, such as precipitation, air density, solar influx, etc., and/or any other suitable kind of measurements which may be required in order to ensure appropriate operation of the wind turbine 2.

[0059] The wind turbines 2 apply the obtained measurements for their own control. Furthermore, the measurements may be supplied to a central power plant controller (PPC) 4, via a communication connection 5. The PPC 4 may apply the received measurements for handling overall control of the wind farm, e.g. in order to ensure that obligations towards the power grid 3 are met. The PPC 4 may further communicate control signals to the wind turbines 2 via the communication connection 5. Finally, the PPC 4 may be able to handle communication outside the wind farm via external communication connection 6. This may, e.g., include communicating data from the wind turbines 2 to a data centre, e.g. for statistical and/or monitoring purposes, and/or receiving control commands related to the wind farm.

[0060] When operating the cluster 1 of wind turbines 2, the measurements obtained by the wind turbines 2 are applied for deriving one or more parameter values of a parameter being indicative for a condition occurring at the respective wind turbines 2. This may, e.g., be done by the wind turbines 2 themselves, by the PPC 4, or by an external data centre.

[0061] For each wind turbine 2, the derived parameter values are compared to a trigger value for triggering measures for mitigating an effect of the condition. The trigger value is a value of the parameter which is appropriate for distinguishing between a situation where the condition is likely occurring and a situation where the condition is likely not occurring. Thus, in the case that the derived parameter value for a specific wind turbine 2 exceeds the trigger value, it can be concluded that it is likely that the condition is occurring at that wind turbine 2. Therefore, when this is the case, measures are initiated for mitigating an effect of the condition at that specific wind turbine 2.

[0062] Furthermore, the derived parameter values for the wind turbines 2 of the cluster 1 of wind turbines 2 are compared to an expected distribution of the parameter values. In the case that a distribution of the derived parameter value differs from the expected distribution, this is an indication that there might be general conditions which affect the parameter. Therefore, the trigger value being applied may not be appropriate under the prevailing operating conditions. Accordingly, when this is the case, the trigger value is adjusted in order to compensate for the discrepancy. Subsequently, the adjusted trigger value is applied during control of the wind turbines 2. Thereby it can be established with higher accuracy whether or not a specific condition is occurring at the wind turbines 2, and it is ensured with higher certainty that relevant measures are initiated if the condition is in fact occurring, as well as that no measures are initiated if the condition is not occurring.

[0063] FIG. 2 is a graph illustrating rotor efficiency loss for a wind turbine. Solid curve 7 represents a normal power curve for the wind turbine, i.e. normal power production of the wind turbine as a function of wind speed, and thereby the maximum power production which may be expected from the wind turbine at the specified wind speed.

[0064] The dashed curve 8 represents a similar power curve for the wind turbine in a scenario where there is ice formation on the wind turbine blades of the wind turbine.

[0065] It can be seen that the power production of the wind turbine with ice formation on the wind turbine blades, represented by dashed curve 8, is lower than the normal power production of the wind turbine, represented by solid curve 7, in the partial load region, i.e. below the nominal power. The difference between the normal power production 7 and the power production 8 of the wind turbine with ice formation on the wind turbine blades is referred to as rotor efficiency loss. In the graph of FIG. 2, the rotor efficiency loss is caused solely by ice formation on the wind turbine blades. It should, however, be noted that rotor efficiency loss may, alternatively or additionally, be caused by other causes.

[0066] FIGS. 3-9 are graphs illustrating a method according to an embodiment of the invention related to ice formation on the wind turbine blades.

[0067] FIG. 3 is a graph illustrating an expected distribution 9 of rotor efficiency loss of a plurality of wind turbines forming a cluster of wind turbines, when there is no ice formation on the wind turbine blades of the wind turbines. More particularly, the graph of FIG. 3 illustrates normalised probability density of the cluster of wind turbines as a function of rotor efficiency loss. The distribution 9 is a normal distribution defining a mean of 0. Thus, on average the wind turbines are expected to deliver maximum power production under the given operating conditions, corresponding to no, i.e. zero, rotor efficiency loss. However, some of the wind turbines are expected have a slightly higher power production and some of the wind turbines are expected to have a slightly lower power production, as illustrated by the distribution 9 of FIG. 3. This may, e.g., be caused by differences in wake conditions, yaw errors, inaccuracy in measurements, etc., among the wind turbines. It can be seen that most of the wind turbines of the cluster of wind turbines are expected to detect a rotor efficiency loss which is within 10% of the expected power production.

[0068] FIG. 4 is a graph illustrating a distribution 10 of rotor efficiency loss of a cluster of wind turbines corresponding to the cluster of wind turbines illustrated in FIG. 3. Similarly to FIG. 3, the graph of FIG. 4 illustrates normalised probability density of the cluster of wind turbines as a function of rotor efficiency loss. However, in FIG. 4 there is ice formation on the wind turbine blades of the wind turbines. Similarly to FIG. 3, the distribution 10 illustrated in FIG. 4 is also a normal distribution. However, in this case the distribution 10 defines a mean of approximately 20%. Accordingly, on average the wind turbines of the cluster of wind turbines detect a rotor efficiency loss of approximately 20% of the expected power production, i.e. a power production which is approximately 20% below the maximum possible power production under the given circumstances.

[0069] FIG. 5 illustrates the expected distribution 9 of rotor efficiency loss, without ice formation, of FIG. 3, and the distribution 10 of rotor efficiency loss, with ice formation, of FIG. 4. It is clear from FIG. 5 that the wind turbines with ice formation on the wind turbine blades, corresponding to distribution 10, have a significantly lower power production than the wind turbines without ice formation on the wind turbine blades, corresponding to distribution 9.

[0070] Thus, if there is in fact ice formation on the wind turbine blades of a wind turbine, it is desirable to remove this ice, in order to reduce the rotor efficiency loss caused by the ice formation, i.e. in order to increase the power production of the wind turbine. This is often done by heating the wind turbine blades, thereby melting the ice. However, if the wind turbine blades are heated when there is in fact no ice formation on the wind turbine blades, this may cause damage to the wind turbine blades, e.g. due to glue or resin forming part of the wind turbine blades being damaged or partly resolved. In addition, this represents an unnecessary energy consumption. Therefore, it is desirable to be able to determine whether or not there is ice formation on the wind turbine blades of a wind turbine, and apply this determination for triggering heating of the wind turbine blades.

[0071] It can be seen that, if a wind turbine detects a rotor efficiency loss of 10% or lower, it is significantly more likely that there is ice formation on the wind turbine blades of the wind turbine, corresponding to the wind turbine following distribution 10, than that there is no ice formation on the wind turbine blades, corresponding to the wind turbine following expected distribution 9.

[0072] It can also be seen that, if a wind turbine detects a rotor efficiency loss of 1% or higher, it is significantly more likely that there is no ice formation on the wind turbine blades of the wind turbine, corresponding to the wind turbine following expected distribution 9, than that there is ice formation on the wind turbine blades, corresponding to the wind turbine following distribution 10.

[0073] However, if a wind turbine detects a rotor efficiency loss within the region between 10% and 1%, it is difficult to determine whether the wind turbine follows distribution 9 or distribution 10, i.e. whether or not this is an indication that there is ice formation on the wind turbine blades.

[0074] In order to ensure with high certainty that heating of the wind turbine blades is not initiated when there is no ice formation on the wind turbine blades, a trigger value of 10% rotor efficiency loss may be selected. This will result in very few wind turbines without ice formation on the wind turbine blades being categorised as having ice formation on the wind turbine blades, corresponding to the part of distribution 9 being below 10%. Thereby the risk of causing damage to the wind turbine blades, due to heating, is very small. Furthermore, this will result in most of the wind turbines which actually have ice formation on the wind turbine blades being categorised as such, corresponding to the part of distribution 10 being below 10%.

[0075] However, selecting 10% rotor efficiency loss as the trigger value for initiating heating of the wind turbine blades will have the consequence that a significant number of wind turbines which actually have ice formation on the wind turbine blades will not be categorised as such, corresponding to the part of distribution 10 being above 10%. For these wind turbines, heating will not be initiated, and they will continue operating with a reduced power output.

[0076] FIG. 6 is a graph showing the expected distribution 9 of rotor efficiency loss, without ice formation, of FIG. 3. Furthermore, derived rotor efficiency loss values for a plurality of wind turbines forming part of a cluster of wind turbines are plotted as discrete points marked by +. It is noted that the marked points only represent derived rotor efficiency loss values for individual wind turbines, i.e. the points do not reflect normalised probability density for the derived values. The points have been plotted on the expected distribution 9 in order to allow for easy comparison between the derived rotor efficiency loss values and the expected distribution 9, as will be described in further detail below.

[0077] It can be seen that the derived rotor efficiency loss values are all below the mean value of the expected distribution 9, i.e. below 0%. If the derived rotor efficiency loss values had followed the expected distribution 9, derived values should have been present along the entire curve 9. Accordingly, it can be concluded that the derived rotor efficiency loss values are not following the expected distribution 9. Therefore, if a trigger value for initiating heating of the wind turbine blades is selected based on the assumption that the rotor efficiencies of the wind turbines are distributed as expected, then there is a significant risk that wind turbines which actually have ice formation on the wind turbine blades are not identified. This will lead to the power production of these wind turbines being lower than the optimal or maximum power production under the given operating conditions.

[0078] Thus, by comparing the derived rotor efficiency losses for the wind turbines to the expected distribution 9 of rotor efficiency losses, it can be seen that a trigger value of 10% rotor efficiency loss for initiating heating of the wind turbine blades is not appropriate. In fact, none of the wind turbines would trigger heating of the wind turbine blades, since they all detect a rotor efficiency loss which is above 10%. Yet, the low derived values indicate that there might be ice formation on the wind turbine blades of at least some of the wind turbines.

[0079] FIG. 7 is a graph illustrating the actual distribution 11 of the derived rotor efficiency losses of the wind turbines, which were plotted in FIG. 6, in the form of normalised probability density as a function of rotor efficiency loss, derived from the values illustrated in FIG. 6. It can be seen that the distribution 11 is a normal distribution defining a mean value of approximately 5%. Thus, on average the wind turbines detect a power production which is approximately 5% lower than the maximum power production under the given operating conditions.

[0080] Based on the actual distribution 11, a value of the rotor efficiency loss is now selected in such a manner that 70% of the wind turbines detect a rotor efficiency loss below that value, indicated by line 12. It can be seen that this value is approximately 4%. The value 12 is now used for adjusting the trigger value in order to define a more suitable boundary for distinguishing between wind turbines with ice formation on the wind turbine blades and wind turbines without ice formation on the wind turbine blades. This will be described in further detail below with reference to FIG. 8.

[0081] FIG. 8 is a graph illustrating the expected distribution 9 and the rotor efficiency losses derived by the wind turbines, which were also illustrated in FIG. 6. Based on the actual distribution 11, illustrated in FIG. 7, and the selected value 12, the trigger value is adjusted by an amount corresponding to the selected value 12, i.e. 4%, from 10% to 6%. It can be seen that this has the consequence that some of the wind turbines detect a rotor efficiency loss which is below the adjusted trigger value, i.e. below 6%, and these wind turbines will therefore initiate heating of their wind turbine blades. Accordingly, the new trigger value strikes a better balance between ensuring that ice formation is removed and preventing heating of wind turbine blades without ice formation. Accordingly, the total power production of the cluster of wind turbines can be increased without risking damage to the wind turbine blades of the wind turbines.

[0082] FIG. 9 is a graph illustrating the expected distribution 9 and derived rotor efficiency losses, similar to FIG. 8. However, in the graph of FIG. 9, the trigger value has been adjusted more, i.e. to a higher value, than is the case in FIG. 8. More particularly, in the situation illustrated in FIG. 9, a value of the rotor efficiency loss is selected, based on the actual distribution of the derived rotor efficiency losses, essentially in the manner described above with reference to FIG. 7. However, in this case the value is selected in such a manner that 30% of the wind turbines detect a rotor efficiency loss below the value. Referring to FIG. 7, this value is approximately 6%. Therefore, in the situation illustrated in FIG. 9, the trigger value is adjusted by 6%, from 10% to 4%. It can be seen that this has the consequence that even more wind turbines detect a rotor efficiency loss below the adjusted trigger value, causing these wind turbines to initiate heating of their wind turbine blades.

[0083] Referring to FIGS. 8 and 9, FIG. 8 represents a more conservative approach than FIG. 9, i.e. FIG. 9 represents a more aggressive approach than FIG. 8. Selecting a very conservative approach provides a low risk of initiating heating of wind turbine blades without ice formation, while accepting that some wind turbines with ice formation on the wind turbine blades will not initiate heating of their wind turbine blades. Similarly, selecting a very aggressive approach provides a low risk of failing to heat wind turbine blades with ice formation, while accepting that some wind turbine blades without ice formation will be heated.

[0084] If the cluster of wind turbines comprises few wind turbines, it may be appropriate to select a conservative approach. On the other hand, if the cluster of wind turbines comprises many wind turbines, the statistical foundation is better, and a more aggressive approach may therefore be selected without unduly increasing the risk of heating wind turbine blades without ice formation. Alternatively or additionally, selecting a conservative or aggressive approach may also be based on a deviation of the distribution of the derived rotor efficiency losses. For instance, a conservative approach may be selected if the deviation is large, and an aggressive approach may be selected if the deviation is small.

[0085] FIG. 10 is a flow chart illustrating a method according to an embodiment of the invention. The process is started at step 13. At step 14 the wind turbines of a wind turbine cluster each obtains measurements related to the operation of the wind turbine.

[0086] At step 15, parameter values are derived for each of the wind turbines, based on the measurements obtained at step 14. The parameter values are values of a parameter which is indicative for a condition occurring at the wind turbine.

[0087] At step 16, the derived parameter values are compared to a trigger value for triggering measures for mitigating an effect of the condition, based on the parameter. In the case that step 16 reveals that the derived parameter value for one of the wind turbines exceeds the trigger value, it is concluded that the condition is occurring at that wind turbine. Therefore, when this is the case, the process is forwarded to step 17, where measures are initiated for that wind turbine, in order to mitigate an effect of the condition.

[0088] On the other hand, in the case that step 16 reveals that the derived parameter value for a given wind turbine does not exceed the trigger value, then it is concluded that the condition is not occurring at that wind turbine, and the process is returned to step 14 for continued measurements.

[0089] Furthermore, at step 18, the parameter values derived at step 15 are compared to an expected distribution of the parameter values. In the case that the derived parameter values follow the expected distribution, i.e. if they do not differ from the expected distribution, it is concluded that the trigger value which is applied in step 16 is appropriate, and the process is returned to step 14.

[0090] One the other hand, in the case that step 18 reveals that the derived parameter values differ from the expected distribution, then the trigger value applied in step 16 is adjusted in accordance with the difference. Subsequently the adjusted trigger value is applied when performing step 16.