Method for computer-implemented determination of control parameters of a turbomachine

Abstract

The invention describes a method for computer-implemented determination of control parameters (CP) of a turbine () in the event of a component failure. The turbine () is a wind turbine with a generator or a gas turbine with a generator. The method takes into account the influence of individual turbine characteristic values ​​on the turbine performance in a turbine model in order to determine the control parameters of the turbine without damaging the turbine. The invention comprises the following steps: receiving information indicating a component failure via an interface (IF); identifying, by a processing unit (PU), at which power level the turbine () can be operated by simulating the operation of the turbine (), the simulation being performed using a given turbine model (TM), wherein the identified component is set to operate with reduced functionality and wherein one or more characteristic values ​​(AG, MP, MDM, TC, CR) of the wind turbine () are used as input parameters; and deriving, by the processing unit (PU), a control parameter (CP) of the wind turbine (Tn) from the identified power level.

Description

Technical Field

[0001] The present invention relates to a method and a system for computer-implemented determination of control parameters of a turbine in the event of a component failure. In particular, the present invention relates to a method and a system for computer-implemented determination of control parameters of a single wind turbine or a wind turbine of a wind farm with a generator or a gas turbine with a generator. Background Art

[0002] The operation of a wind turbine is based on nominal characteristic values ​​of the wind turbine, which characterize the wind turbine in terms of power output as a function of wind speed. The use of nominal parameters enables the manufacturer of wind turbines to guarantee a specific annual energy production (AEP) to the customer, since the wind turbine is considered to have the same performance at its contractual rating point. Therefore, the nominal parameters are used as a basis for deriving turbine control parameters (short: control parameters), which are related to a specific power output under specific environmental conditions, in particular wind speed.

[0003] When a problem occurs in a wind turbine that causes a component failure, for example when one of the cooling fans stops functioning, the wind turbine is usually shut down. As a result, the power output is equal to zero. Due to environmental conditions, especially due to being at sea, repairing the turbine may take a long time, possibly up to days / weeks / months. Therefore, the shutdown results in huge AEP losses.

[0004] The same problem arises in other industrial processes, such as the operation of gas turbines. Summary of the invention

[0005] Therefore, it is an object of the present invention to provide a computer implemented method and system which minimizes AEP losses in case of component failure of a turbomachine. Another object of the present invention is to provide a computer program product.

[0006] These objects are solved by the method, computer program product and system disclosed according to the present invention.

[0007] According to a first aspect of the invention, a method for computer-implemented determination of control parameters of a turbine having a component failure is proposed. The turbine may be a single wind turbine. The turbine may be a wind turbine of a wind farm. In the case where the turbine is a wind turbine, it comprises a generator. Alternatively, the turbine may be a gas turbine having a generator.

[0008] The method comprises the step of receiving, via an interface, information indicating a component failure. The information contains not only an indication that a failure has occurred, but also information elements about which component of the turbomachine has the failure and / or information elements about the extent of the failure, such as partial or complete failure of the component and / or temporary or permanent failure of the component.

[0009] As a further step, the method includes identifying, by the processing unit, at what power level the turbine can be operated by simulating the operation of the turbine. The simulation is performed using a given turbine model in which the identified components are set to operate with reduced functionality. Setting the components identified in the simulation as having a fault to operate with reduced functionality includes situations where the functionality is only partial (e.g., 90%, 70%, 50%, 20%, etc. of the nominal functionality) as well as situations where the identified components are faulty. In addition, one or more characteristic values ​​of the turbine are used as input parameters in the turbine model.

[0010] As a final step, the method comprises deriving, by the processing unit, control parameters of the turbine from the identified power level.

[0011] The method is based on the consideration that a turbine with a faulty component can be operated at a reduced power. By identifying the power level at which the turbine can still operate despite the faulty component, a corrected operating point can be estimated with the help of a given turbine model in order to produce as much power as possible without damaging the turbine due to (thermal, electrical or mechanical) overloads that reduce the overall life of the turbine.

[0012] Considering suitable characteristic values ​​for a turbine enables the formation of a customized turbine "DNA", which can be considered as a unique map characterizing the parameters of the turbine. Having known the manufacturing tolerances of the turbine, a given turbine model can be fed with characteristic values ​​and information indicative of component failures to determine whether the turbine is still able to produce power without damaging or degrading its lifetime characteristics. Determining whether a failed turbine can be controlled enables, in the case of a wind turbine, the power to be derived from an associated power versus wind speed diagram, which can be derived from the output of a given turbine model, which processes one or more characteristic values ​​of the wind turbine as input parameters, in addition to information about the failed component.

[0013] Therefore, the characteristic values ​​and the information indicating component failures are taken into account in the turbine model to derive actual and turbine-specific control parameters in case of a failed turbine. This mechanism of achieving power maximization by using a given turbine model does not negatively impact existing turbine structures, such as generators, power converters, and blades, etc., since their operation takes into account the nominal and / or actual characteristic values ​​and functions of the turbine components.

[0014] According to a preferred embodiment, the turbine model is a physical model based on a plurality of equations found by simulation and / or validated test data and / or look-up tables. Furthermore, the turbine model may take into account a plurality of measured performance parameters, such as temperature, current load curve, etc., to determine, in the case of a wind turbine, a power versus wind speed diagram for a particular wind turbine.

[0015] The one or more characteristic values ​​may be retrieved and received from a database by an interface. The interface and the processing unit are part of a computer system. The computer system may be part of a control instance of a wind turbine. Alternatively, the computer system may be part of an external control system. The database may be stored on the computer system or may be an external database connected to the computer system. The one or more characteristic values ​​may consist of nominal parameters of the characteristic values ​​(i.e., nominal characteristic values) and / or manufacturing tolerances of the characteristic values ​​(i.e., actual characteristic values), the manufacturing tolerances of the characteristic values ​​being obtained by measurements during the manufacturing process and collated in a database for a plurality of turbines.

[0016] The one or more characteristic values ​​include one or more of the following: air gap (between rotor and stator), magnet performance, magnet size, thermal conductivity and coil resistance. In addition to the characteristic values, other characteristic values ​​may also be considered, such as variations in the coils.

[0017] According to another preferred embodiment, in the case of a wind turbine, identifying the power level corresponds to determining a power versus wind speed diagram, the power versus wind speed diagram being calculated based on a given turbine model and information indicative of a component failure. In the case where manufacturing tolerances of the wind turbine are known, the given turbine model may use the manufacturing tolerances as input to determine whether the wind turbine is capable of producing more power than considering only nominal parameters. The determination of whether the turbine can be controlled with improved control parameters to calculate its maximum power output at a given wind speed may be derived from the associated power versus wind speed diagram, which is derived from the output of the given turbine model, the given turbine model processing as input parameters one or more manufacturing tolerances of the respective wind turbine and information indicative of a component failure.

[0018] As another embodiment, in the case of a wind farm, the method includes as a step deriving control parameters for a plurality of wind turbines of the wind farm from the information of the component failure and the identified turbine power levels. The increased power levels of neighboring wind turbines of the turbine with the failure can be used to fill any unused capacity in the grid connection.

[0019] According to another preferred embodiment, as further input parameters of the turbine model, historical turbine sensor data and / or historical operating conditions are processed by the processing unit for determining a power versus wind speed diagram for the wind turbine. Taking into account historical sensor data captured by physical and / or virtual sensors enables receiving information about component behavior relevant for lifetime considerations. Historical sensor data to be taken into account may include, among others, component temperature, ambient temperature, wind speed. Historical data may be compared with real-time sensor data during past operation of the wind turbine. The comparison of measured lifetime data with data derived from the turbine model allows for a flexible utilization of large manufacturing margins in order to still have a maximum / optimum AEP in the event of a failure.

[0020] According to another preferred embodiment, the turbine model considers the drive train consisting of the rotor hub, the generator and the converter of the wind turbine. In the case of a gas turbine, the turbine model considers the drive train consisting of the rotor hub, the generator and the converter. Additionally or alternatively, the turbine model may consider the blades and / or the gearbox and / or the nacelle and / or the tower and / or the cables and / or the transformer of the wind turbine.

[0021] According to another preferred embodiment, in order to identify at what power level the turbine can operate, the thermal performance of the turbine is modeled by a given turbine model. This will provide a new reference power requirement while staying within the limits of the turbine and while taking into account any specific characteristics of the turbine.

[0022] According to a second aspect of the invention, a computer program product directly loadable into the internal memory of a digital computer is provided, comprising software code portions for performing the steps of the method described herein when the product is run on the computer. The computer program product may be in the form of a storage medium, such as a CD-ROM, DVD, USB stick or memory card. The computer program product may also be in the form of a signal transmittable via a wired or wireless communication link.

[0023] According to a third aspect, a system for computer-implemented determination of control parameters of a turbine is proposed. The turbine may be a single wind turbine or a wind turbine of a wind farm with a generator or a gas turbine with a generator. The system comprises an interface adapted to receive information indicating a component failure, and a processing unit adapted to determine at what power level the turbine can operate by simulation of turbine operation, the simulation being performed using a given turbine model in which the identified component is arranged to operate with reduced functionality (i.e., reduced or partially reduced), and wherein one or more characteristic values ​​of the turbine are used as input parameters and are arranged to derive control parameters of the turbine from the identified power level. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present invention will be explained in more detail with reference to the accompanying drawings;

[0025] Figure 1 A flow chart illustrating the steps of the method according to the invention is shown;

[0026] Figure 2 A schematic diagram illustrating steps for performing a method for determining improved control parameters for a plurality of wind turbines of a wind park is shown;

[0027] Figure 3 A schematic diagram illustrating a turbine model for determining improved control parameters of a wind turbine is illustrated. DETAILED DESCRIPTION

[0028] In the following section, examples of the invention will be described by referring to wind turbines. As will be appreciated by those skilled in the art, the method may also be used in other industrial applications, in particular in the field of gas turbines.

[0029] Currently, when a problem occurs in a wind turbine that leads to a component failure, a decision must be made whether to shut down the wind turbine or to operate the wind turbine with a reduced power output until the problem is resolved. For example, such a problem may be that one of the multiple cooling fans stops functioning. If the wind turbine is shut down due to the fault, further damage can be avoided. However, as a result, the power output is equal to zero. Due to environmental conditions, especially at sea, it may take a long time to repair the turbine, which may be up to days / weeks / months. Operation with reduced power is usually considered, which has the risk of damaging other components of the wind turbine. In order to avoid damage to the wind turbine, the control parameters of the turbine are selected so that the power output is very low, resulting in significant AEP losses compared to normal operation.

[0030] The method described below enables a computer system to find a compromise between minimizing the risk of damage to the wind turbine due to operation at reduced power despite component failures and maximizing the AEP.

[0031] refer to Figure 1 In step S1, the processing unit PU ( Figure 2 ) receives information ML indicating a component failure. The information ML contains not only an indication that a failure has occurred, but also information elements which component of the wind turbine has the failure and / or information elements about the extent of the failure, such as partial or complete failure of the component and / or temporary or permanent failure of the component. For example, one of the cooling fans has stopped functioning.

[0032] In step S2, the processing unit PU identifies at what power level the wind turbine can be operated by simulating the operation of the wind turbine. The simulation is performed with a given turbine model in which the identified components are set to operate with reduced functionality (i.e. in the chosen example, one of the fans in the cooling air fails). Furthermore, one or more manufacturing tolerances of the characteristic values ​​AG, MP, MDM, TC, CR of the wind turbine are used as input parameters.

[0033] In step S3, control parameters CP of the wind turbine are derived by the processing unit PU from the identified power level.The method is based on the consideration that a wind turbine with a faulty component may be operated at reduced power.

[0034] By identifying the power level at which the wind turbine can still operate despite the failed component, a corrected operating point can be estimated with the help of a given turbine model to produce as much power as possible without damaging the wind turbine due to (thermal, electrical, mechanical) overloads that reduce the overall lifetime of the turbine.

[0035] The method may also take into account the impact of individual turbine manufacturing tolerances on turbine performance, thereby avoiding underutilization of failed wind turbines, in other words maximizing the output power of failed turbines based on their manufacturing parameters and dimensions. Due to the consideration of the manufacturing tolerances of the individual turbines, at least some of the turbines can be operated in an optimized manner, resulting in an increase in the AEP of the wind turbine. However, it should be understood that the turbine model may also be fed with nominal characteristic values. In a further implementation, both the nominal characteristic values ​​and the actual characteristic values ​​taking into account manufacturing tolerances may be used as input information in the turbine model.

[0036] refer to Figure 2 In a first or preparatory step, a measurement of the manufacturing data MMV is performed. Manufacturing tolerances that have an influence on the turbine performance are, for example, the air gap AG, the magnet performance MP (due to the magnet material and / or dimensions MDM and / or the manufacturing process), the thermal conductivity TC and the coil resistance CR. Each of these manufacturing tolerances is a real characteristic value, individual for each turbine to be considered. The manufacturing tolerances of these characteristic values ​​AG, MP, MDM, TC, CR do have a direct influence on the turbine performance, both in normal operation without any problems and during operation when problems occur.

[0037] The manufacturing tolerances of the characteristic values ​​AG, MP, MDM, TC, CR, which are typically different for each turbine, are collated and stored in a database DB (turbine DNA). n(where n corresponds to the number of wind turbines in the wind farm WP), manufacturing dataset MD T1 , ..., MD Tn It can be stored as a manufacturing data set containing characteristic values ​​AG, MP, MDM, TC, and CR. T1 , ..., MD Tn It can be seen as the DNA of each individual wind turbine T1, ..., Tn. It should be understood that the storage of manufacturing data consisting of manufacturing tolerances of characteristic values ​​AG, MP, MDM, TC, CR can be done in any way, such as a lookup table, an association map, etc.

[0038] Manufacturing tolerances of characteristic values ​​AG, MP, MDM, TC, CR are received at an interface IF of a computer or computer system. The computer or computer system comprises a processing unit PU. The database DB may be stored in a memory of the computer (system) or in an external storage device of the computer (system). In another implementation, the database DB may be cloud-based. The processing unit PU is adapted to determine a power versus wind speed map M for each of a plurality of wind turbines T1, ..., Tn. T1 , ..., M Tn . Power and wind speed diagram M T1 ,…,M Tn Calculated according to the above mentioned given turbine model TM, which uses as input parameters the actual characteristic values ​​AG, MP, MDM, TC, CR taking into account the manufacturing tolerances of the corresponding wind turbines T1, ..., Tn and / or the actual characteristic values ​​AG, MP, MDM, TC, CR and component faults MF (if any).

[0039] For each type of wind turbine, a specific turbine model may be provided. In an alternative embodiment, a specific turbine model may be used for the respective wind turbine of the wind farm. In another alternative embodiment, a generic turbine model may be used for all wind turbines of the wind farm.

[0040] A turbine model is a physical model based on multiple equations found through simulations and / or validated test data. The turbine model can be considered as a "digital twin" of each individual wind turbine. The power versus wind speed diagram M for each individual wind turbine T1, ..., Tn T1 , ..., M Tn are unique maps derived from the turbine model and the nominal and / or characteristic values ​​AG, MP, MDM, TC, CR. They are created for turbines without faults. In addition, additional maps are created for each turbine for all possible faults. These maps can be created in advance, i.e. before the respective problem is determined. Alternatively, these maps can be created when information M1 indicating a component fault is received.

[0041] Figure 3 An embodiment of a turbine model for modeling a single wind turbine is illustrated. In this embodiment, the turbine model TM considers the drive train of the wind turbine consisting of the rotor hub ROT, the generator GEN, the converter CON, the cable CAB and the supplementary / auxiliary components AUX and the transformer TRF. However, the turbine model may also consider components of the wind turbine such as blades, nacelle, tower, substation, gearbox (for geared turbines), etc.

[0042] The turbine model TM calculates the losses of the components within the drive train to account for power / energy losses between the turbine blade input and the output to the grid during electromechanical energy conversion and to auxiliary or support systems. Since the loss mechanisms are temperature dependent and they themselves generate heat, the turbine model TM is coupled to or includes a thermal model of the generator GEN (generator thermal model GTM) and / or a thermal model of the converter CON (converter thermal model CTM) and is solved iteratively. The generator thermal model GTM and the converter thermal model CTM are coupled to components that affect drive train cooling, such as the cooling system COOLS (e.g., cooling fan), the heat exchanger HX, and the cabin environment NAAMB.

[0043] The turbine model TM calculates the available power P at the (grid) output based on the input environmental conditions of wind speed WS and temperature ATMP out . The Turbine Model TM can be used to assess the potential AEP for a given wind turbine and site by inputting historical and / or predicted wind conditions over a given time period. The use of the thermal models GTM, CTM allows for any control features such as high temperature curtailment to be accurately accounted for. Alternatively, the Turbine Model TM can be employed in real time to assess the potential output and / or impact of control decisions on a specific generator operating point. Further, it can be used as a reference against the actual turbine, comparing actual and predicted operation in response to operating conditions to act as a health monitor.

[0044] The Turbine ModelTM can be implemented in many different environments / programming codes. Typically, it can handle both thermal coupling and control algorithms based on iterative solver routines. Where possible, reduced order models, lookup tables or functions (equations) are used to represent complex behavior using appropriate approximations and / or assumptions to ensure short computation times while maintaining an appropriate level of accuracy.

[0045] like Figure 3 As shown, the turbine model TM can be extended to include a blade model or a structural model of the turbine. Such a model can be used to represent any electric drive / generator system other than a wind turbine.

[0046] The more detailed turbine model TM includes the following sub-models:

[0047] The rotor model is used to convert the wind speed WS into the rotor / blade speed RS and mechanical power P mech (ie input torque M) is used to model the rotor ROT.

[0048] Optional bearing model for simulating the bearing by accounting for non-ideal main bearings and therefore power losses.

[0049] Generator model for modeling the generator by considering the conversion of primarily mechanical energy to electrical energy, accounting for torque capability, voltage production, and losses induced in the conversion. This can be accomplished by numerical calculation of electromagnetic properties (e.g., finite element analysis), analytical models, or a hybrid of these using reduced order models (ROMs), where the generator performance is derived by a priori numerical modeling and extracted into simpler functions or lookup tables. The generator model is also suitable for calculating losses induced in the conversion, such as winding copper losses and stator electrical steel losses. Control decisions are accounted for.

[0050] Converter model for modeling the converter CON: For example, in a direct-drive permanent magnet generator, the variable frequency output of the generator is interfaced with the fixed frequency grid via a power electronic converter (active rectifier - DC link - inverter), allowing for control of the generator's operating conditions. Load-dependent switching and conduction losses in the converter are taken into account.

[0051] Cable loss model for modeling cable CAB by taking into account the ohmic losses in the connecting cables.

[0052] A supplementary / auxiliary loss model for modeling the supplementary / auxiliary components AUX by accounting for the power consumed by support services such as cooling fans, pumps, and hydraulic control systems, as these losses reduce the available power at the grid.

[0053] Transformer loss model for modeling the transformer TRF by accounting for ohmic winding losses and core losses that depend on the load conditions.

[0054] Thermal Model of Generator GEN and Converter CON: The performance and losses of the above components are temperature dependent. For example, the resistance and therefore copper losses generated by the stator electrical windings increase due to the dependence of copper resistivity on temperature, and the magnetic flux generated by the permanent magnets (field sources in the generator) varies due to the change in material remanence with temperature. Since the losses themselves increase the component temperature, the above loss models are iteratively calculated using the corresponding thermal models GTM, CTM. As with the generator model, this can be achieved by reducing the model order using parameters derived from numerical modeling (such as CFD and thermal FEA) to create an equivalent circuit or lumped parameter network.

[0055] Multiple graphs M derived from the turbine model TM R 、M T1 and M T3 This is illustrated in the P-WS diagram (Power vs. Wind PWM). In this diagram, the wind turbine power calculated based on nominal parameters is illustrated. R and two diagrams M for turbines T1 and T3 T1 and M T3 By way of example, the diagrams of turbines T1, T3 T1 and M T3 It is shown that (at least some of) the manufacturing tolerances of the characteristic values ​​AG, MP, MDM, TC, CR are more favorable than for the nominal turbine, resulting in an additional power P for a given speed WS.

[0056] Based on their associated power versus wind speed diagrams, control parameters CP for controlling the wind turbines can be derived for each individual turbine (with or without faults). Figure 2 In the illustration of , the wind farm consists of five turbines T1, ..., T5. However, it should be understood that the number of wind turbines can be arbitrary. The number of wind turbines can be one (1), i.e. the wind farm corresponds to a single wind turbine. If the number of wind turbines is greater than one, the wind turbines are preferably arranged close to each other to provide the power generated in total at a single point to the energy grid. The actual power output P1, ..., P5 associated with the turbine rated output PR with nominal characteristic values ​​is shown below the turbines T1, ..., T5. As can be easily seen, the turbines T1, T3 and T5 generate power outputs P1, P3, P5 that are higher than the rated output of the turbine with the nominal characteristic value. The turbine T2 with a fault generates a power significantly lower than the rated output. The power output P4 of the wind turbine T4 corresponds to the rated output of the turbine with the nominal characteristic value.

[0057] By using a turbine specific model and assessing the specific problem, decisions can be made about at what power level the turbine can safely operate. Therefore, power can still be produced from a turbine with component problems that are within the safety limits found in the turbine model. For example, if at turbine T2 one of the cooling fans has stopped working, the thermal performance of the turbine model for that particular turbine T2 can be modeled with one fan less. This will provide a new reference power requirement while staying within the generator limits and while also accounting for any specific characteristics of that turbine.

[0058] However, if turbine T2 were to operate at reduced power, without the above assessment, the revised operating point could be underestimated, thereby losing AEP, or overestimated, resulting in additional failures, thermal overloads (reducing overall turbine life), or damage.

[0059] Taking into account the impact of individual turbine manufacturing tolerances on turbine performance and using them in the turbine model of each individual turbine allows for maximizing AEP through wind farm optimization by operating the turbines in an optimized manner based on their individual turbine performance, even in the event of component failures.

[0060] Comparison of measured life data received from the processing unit in the form of historical data AD in addition to manufacturing data allows for flexible use of large manufacturing margins to maximize the AEP. Furthermore, the processing unit PU is able to incorporate health monitoring features through comparison of measured parameters, such as comparison of component temperatures against temperatures that can be predicted by the turbine model TM.

[0061] The physical turbine data can be compared to the associated turbine model TM to monitor situations where the turbine may be underperforming, and to provide possible insight into the causes of underperformance. The comparison can flag potential problems and require repairs, as well as provide learning for future turbine development.

[0062] The turbine model can also be used to specify hardware changes and control parameter changes of the turbine. These hardware changes can be implemented in a revision of the turbine (including updating the turbine parameters stored in the database) before or during series production of the turbine, or retrospectively during maintenance of the turbine.

[0063] The invention consists in using a turbine specific model in order to model certain scenarios, in particular within the turbine drive train, to extract power even from a partially operating turbine. The turbine model introduces a level of model fidelity that allows these different scenarios to be modeled. For example, the turbine model consists of a thermal model that accounts for the number of fans operating within the generator cooling system. Within the turbine model, fans can be selectively switched off, mimicking any problems with fans in reality. This will increase the availability of the wind farm and allow strict availability minimum limits to be met.

Claims

1. A method for computer-implemented determination of a control parameter (CP) of a turbine (T1, ..., Tn) in the event of a component failure, the turbine (T1, ..., Tn) being a wind turbine with a generator or a gas turbine with a generator, the method comprising the following steps: S1) receiving information indicating a component failure via an interface (IF); S2) identifying, by a processing unit (PU), at what power level the turbine (T1, ..., Tn) can be operated by simulating the operation of the turbine (T1, ..., Tn), said simulation being performed using a given turbine model (TM), wherein the identified component having a fault is set to operate with reduced functionality, and wherein one or more characteristic values ​​(AG, MP, MDM, TC, CR) of the turbine (T1, ..., Tn) are used as input parameters; and S3) deriving, by the processing unit (PU), control parameters (CP) of the turbines (T1, . . . , Tn) from the identified power levels, The one or more characteristic values ​​(AG, MP, MDM, TC, CR) are manufacturing tolerances of the characteristic values ​​(AG, MP, MDM, TC, CR) obtained by measurement.

2. The method according to claim 1, wherein the turbine model (TM) is a physical model based on a plurality of equations found by simulation and / or validated test data and / or look-up tables.

3. The method according to claim 1, wherein the one or more characteristic values ​​(AG, MP, MDM, TC, CR) are retrieved from a database (DB).

4. The method according to any one of claims 1-3, wherein the one or more characteristic values ​​(AG, MP, MDM, TC, CR) further include nominal parameters of the characteristic values ​​(AG, MP, MDM, TC, CR).

5. The method according to any one of claims 1 to 3, wherein the one or more characteristic values ​​(AG, MP, MDM, TC, CR) include one or more of the following: - Air gap (AG); -Magnetic properties (MP); -Magnet size (MDM); - Thermal conductivity (TC); - Coil resistance (CR).

6. The method according to any of claims 1-3, wherein identifying the power level corresponds to determining a power versus wind speed diagram (PWM), the power versus wind speed diagram (PWM) being calculated according to a given turbine model (TM).

7. Method according to claim 6, wherein as further input parameters of the turbine model (TM) historical turbine sensor data and / or operating conditions are processed by the processing unit (PU) for determining a power versus wind speed diagram (PWM).

8. The method according to any one of claims 1 to 3, wherein the turbine model (TM) considers a drive train consisting of a rotor hub, a generator or a motor, and a converter of the turbine.

9. Method according to any of claims 1-3, wherein the turbine model (TM) considers blades and / or a gearbox and / or a nacelle and / or a tower and / or cables and / or a transformer of the wind turbine.

10. Method according to one of claims 1 to 3, wherein the thermal behavior of the turbine (T) is modeled by a given turbine model (TM) in order to identify at which power level the turbine (T) can be operated.

11. A computer program product directly loadable into the internal memory of a digital computer, comprising software code portions for performing the steps of one of the preceding claims when said product is run on a computer.

12. A system for computer-implemented determination of improved control parameters (CP) of a turbine (T1, ..., Tn), said turbine (T1, ..., Tn) being a wind turbine of a wind farm with a generator or a gas turbine with a generator, said system comprising: An interface (IF), the interface (IF) being adapted for: - receiving information indicating a component failure; and a processing unit (PU) adapted to: - determining at what power level the turbine (T1, ..., Tn) can be operated by simulating the operation of the turbine (T1, ..., Tn), the simulation being performed using a given turbine model (TM), wherein the identified component having a fault is set to operate with reduced functionality, and wherein one or more characteristic values ​​(AG, MP, MDM, TC, CR) of the turbine (T1, ..., Tn) are used as input parameters, wherein the one or more characteristic values ​​(AG, MP, MDM, TC, CR) are manufacturing tolerances of the characteristic values ​​(AG, MP, MDM, TC, CR) obtained by measurement; and - deriving control parameters (CP) of the turbines (T1, ..., Tn) from the identified power levels.

13. System according to claim 12, wherein the interface (IF) and the processing unit (PU) are components of a computer system of the turbomachine.

14. The system according to claim 12, wherein the interface (IF) and the processing unit (PU) are components of an external computer system.

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