DK178505B1 - Model based wind turbine component monitoring - Google Patents
Model based wind turbine component monitoring Download PDFInfo
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- DK178505B1 DK178505B1 DKPA201400743A DKPA201400743A DK178505B1 DK 178505 B1 DK178505 B1 DK 178505B1 DK PA201400743 A DKPA201400743 A DK PA201400743A DK PA201400743 A DKPA201400743 A DK PA201400743A DK 178505 B1 DK178505 B1 DK 178505B1
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
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Abstract
The invention relates to a method of monitoring a wind turbine component and comprising the steps of measuring at least one component value of a wind turbine component to obtain a measured component value, measuring at least one operation environment values having at least indirect impact on the value of the measured component value, estimating at least one estimated component value by means of a modelling algorithm, comparing the measured component value and the estimated component value to obtain a deviation value, wherein the measurements are real-time measurements performed by sensors and communicated to a wind turbine controller and wherein the estimation of the estimated component value and, the comparison to obtain the deviation value are performed in real-time by the wind turbine controller.
Description
Model based wind turbine component monitoring Field of the invention
The invention relates to a method of monitoring at least one wind turbine component by means of a modelling algorithm and the use of such method.
Background of the invention
In monitoring of wind turbine components it is seen that defects in components / systems are first detected when extreme limits are reached and the wind turbine must be stopped. Typically a warning is reported if e.g. a generator temperature reaches 80°C while an alarm is reported and the wind turbine is stopped when the temperature reaches 95 °C.
US patent application with publication number US2011020122 discloses an integrated condition based maintenance system using model-based diagnostics to detect faults and degradations. Using a wind turbine model and operating conditions, model prediction residuals are computed. Fault parameter severities are then estimated based on the residuals. Fault detection or process monitoring is done by periodically taking new values of the input variables that represent a new process condition or state. Early detection of changes in the process can be detected as the statistics e.g. leaves a model hyper plane.
This US application however does not solve the problem of determining if e.g. a temperature or a pressure is at a critical level even though such temperature or pressure is within allowed limits.
US20120029892 describes a method of monitoring a subsystem of a wind turbine. This is done by provi ding a simulation model of the subsy stem which is executed by a separate controller extern and parallel to the controller controlling the wind turbine. The descried method has the drawback that it requires a separate controller for executing the simulation and a separate controller for controlling the wind turbine.
Brief description of the invention
It is an object of the invention to provide a method for monitoring a wind turbine which improves and overcomes know monitoring systems for wind turbines. The invention relates to a method of monitoring at least one wind turbine component of a wind turbine, the method comprising the steps of: - measuring at least one component value of the at least one wind turbine component to obtain a measured component value, - measuring at least one operation environment values of a plurality of values of one or more operation environments having at least indirect impact on the value of the measured component value, - estimating at least one estimated component value of the at least one wind turbine component by means of a modelling algorithm, wherein the at least one operation environment values are provided as input to the modelling algorithm, - comparing the measured component value and the estimated component value to obtain a deviation value, wherein the measuring of the measured component value and the measuring of the at least one operation environment values is real-time measurements performed by one or more sensors and communicated to a wind turbine controller and wherein the estimation of the estimated component value and, the comparison to obtain the deviation value are performed in real-time by the wind turbine controller.
Real-time measuring and estimation should preferably be understood as performed without undue delay as data is measured and comes in i.e. not temporary stored before measuring and / or estimation. Hence real-time estimation is carried out continuously as measured data is presented to the data processor performing the estimation. In the same way the real-time measurements are made continuously at the wind turbine component and communicated to the data processor of the wind turbine controller
Wind turbine controller should preferably be understood as comprising a data processor which is part of the continuously control of the wind turbine or is part of the continuously control of a plurality of wind turbines in a wind park.
A deviation value should preferably be understood as the value of the difference between A wind turbine component should preferably be understood as part of the drive train, pitch system, yaw system, panels, construction parts such as blades, hub, nacelle, tower and components hereof such as converter, hydraulic system, cooling system, lubrication system.
A component value should preferably be understood as a value related to a component of a wind turbine in terms of e.g. temperature, pressure, time since last start / stop, vibration, noise, flow, speed, current, voltage, acceleration, etc. Further a component value may also include derivable values i.e. values not measured in relation to one particular wind turbine component. Examples could be power production, power quality, humidity in the wind turbine, congestion on data network, etc.
The operation environment should preferably be understood as the meteorological environment of the wind turbine and of the wind turbine component inside the wind turbine. In addition operation environment should preferably be understood as other wind turbine components or things related hereto which as impact on the component value examples hereof could be pressure of cooling fluid of a wind turbine component, noise from a wind turbine component, temperature, vibration, etc. of the wind turbine component.
Appropriate sensors such as transducers, encoders, temperature sensors, accelerometers, gyroscopes, anemometers, flow meters, etc. are chosen depending on the component value / operation environment value to be measured.
It should be mentioned that the one or more operation environments may have either direct or indirect impact on the value of the measured component value.
According to an advantageous embodiment of the invention the estimated component value is an estimate of the measured component value, the estimate being based on the modeling of at least to operation environment values. As a very simple example could be mentioned that measuring values of the operation environment such as e.g. rotor speed and wind speed could be modeled resulting in an estimated component valued of e.g. the power output of wind turbine. Hence now this estimated component value represents the power output and can be compared to the actual measured component value which in this case would be the power output. Any difference in the estimated power output and the measured power output is referred to as a deviation value.
According to an advantageous embodiment of the invention the modeling algorithm receive inputs which are compared, correlated or aggregated i.e. modeled resulting in an estimation of a predetermined value. Hence according to a preferred embodiment of the invention the modelling algorithm is designed to by means of mathematical manipulation such as addition, subtraction, amplification, averaging, filtering, integration, differentiation, etc. estimate a value of a predetermined wind turbine component based on predetermined input values. Preferably the value to be estimated is not part of the input to the modeling algorithm.
According to an embodiment of the invention, component health is established based on the deviation value, preferably in real-time by the wind turbine controller.
Component health should be understood as the how close the component is to be worn out i.e. current state of health and/or if a component does not work as intended i.e. fails, introduce vibrations, temperature, pressure, noise, etc. which is not considered normal for that particular wind turbine component when taking age, type etc. into consideration. It should be mentioned that component here also includes the sensors for monitoring. Hence in an advantageous embodiment of the invention the method of the invention may facilitate or be part of a predictive maintenance system of wind turbines.
According to an embodiment of the invention, one or more threshold values for the control of the wind turbine is established based on the deviation value, preferably in real-time by the wind turbine controller.
The inventive method is especially advantageous in that it is an easy way to implement monitoring of wind turbine components for establishing dynamic limiting of e.g. component or wind turbine performance. Hence instead of having fixed alarm and warning limits triggering different control actions such as derating or shutting down the wind turbine, such limits may be based the real-time obtained deviation value and thereby adapted to the current mode of operation of the wind turbine, surroundings such as grid and metrological environment, etc.
An advantage is that component defects or emerging defects by the inventive method can be found before the wind turbine must be stopped due to such defects which will increase the availability. E.g. a fault in the cooling system can be found also during winter time at low ambient temperatures and therefore be repaired during a planned service instead of stopping the wind turbine at a hot summer day where the fault most likely would have caused a threshold value of an alarm or a warning to be exceeded.
Establishing a threshold value based on a short-term or long-term monitoring of the deviation value is especially advantageous if it is not obvious what the threshold should be e.g. from technical specification of the wind turbine component.
According to an embodiment of the invention, the modelling algorithm facilitates mathematically manipulation of input to the modelling algorithm.
The one or more transfer function is preferably implemented as at least one first order transfer function having predetermined input values from the operation environment preferably obtained from sensor. These operation environment values are by the one or more transfer functions mathematically manipulated in terms of addition, subtraction, comparison, correlation, aggregation, averaging, division, multiplication, amplified, damping, etc. Hence in this way it is possible to estimate a predetermined component value by providing operation environment values to a modeling algorithm of the present invention.
According to an embodiment of the invention, the modeling algorithm comprises a low-pass filter or integrator.
According to an embodiment of the invention, the component value is defined by a measure selected from the list comprising: temperature, pressure, load, torque, power production, power quality, acceleration, voltage, current, flow, speed, noise, metrological values such as wind speed, wind direction, humidity, temperature and turbulence. According to an advantageous embodiment of the invention all existing values which are possible to measure of a wind turbine could be regarded as a component value.
According to an embodiment of the invention, the operation environment having at least indirect impact on the value of the measured component value is selected from the list comprising: metrological measures outside the wind turbine, metrological measures inside the wind turbine, utility grid and wind turbine components.
Operation environment should be considered the close surroundings having impact on the value of the measured component value.
According to an advantageous embodiment of the invention metrological measures comprise temperature, humidity, wind speed, wind direction, sunlight and shadow.
Further wind turbine components includes bearing, motors, cables, tubes or hoses, fastening mean, cooling systems, lubrication systems and structural components such as blades, hub, nacelle and tower and components hereof.
According to an embodiment of the invention, wherein the wind turbine controller is part of a wind park control system.
A wind turbine controller which is part of a wind park control system facilitates modeling of input from a plurality of different wind turbines. In this way the inventive method could also be applied on a wind park level.
According to an embodiment of the invention, the method further comprising the step of initializing the modelling algorithm.
An initializing step is preferably used for verification of the mathematical manipulation of input performed by the modelling algorithm. Such verification could be based on measurements of a new component, component data of a data sheet, burin test, etc. which is then compared to the value of the estimation performed by the modeling algorithm.
According to an embodiment of the invention, wherein the method is carried out when the wind turbine is in a non-production mode.
This is advantageous in that e.g. startup conditions such as temperature of gear oil, controllers, etc. may then be monitored. It should be mentioned that the method of the present invention is preferably also used in a wind turbine in a production mode.
According to an embodiment of the invention, the method is used to provide information of a particular malfunction.
Depending on if the change of the deviation value happens fast or slow or is increasing or decreasing this will point at different root cause for a malfunction of part of the wind turbine.
Moreover the invention relates to the use of a method according to any of the preceding claims to monitor a wind turbine component.
Figures A few exemplary embodiments of the invention will be described in more detail in the following with reference to the figures, of which fig. 1 illustrates a wind turbine, fig. 2 illustrates the steps of the method according to an embodiment of the invention and fig. 3A and 3B illustrates modelling algorithms according to embodiments of the invention.
Detailed description of the invention
Figure 1 illustrates a wind turbine 2 comprising a plurality of wind turbine components 1 such as wind turbine controller 10, tower 11, nacelle 12, hub 13, blades 14 and a plurality of sub-components attached to or located within the above mentioned wind turbine components 1. At least part of the wind turbine components 1 are communicatively connected e.g. by means of one or more pressurized systems based on e.g. hydraulic or air, data communication network 15, electric systems, optical or mechanical connection systems. Thereby interaction between the wind turbine components 1 is obtained facilitating power production of the wind turbine 2 under various conditions which is considered common general knowledge by the skilled person and therefore not disclosed further in the description.
In figure 1 a few sub-components as well as the wind turbine 2, tower 11, nacelle 12, hub 13, blades 14 and communication network 15 are all denoted 5 to indicate that they all may also be referred to as operation environment 5.
Hence depending on the input needed by the modelling algorithm 7 and the wind turbine component 1 to be measured, the measurements from sensors 9 may either be seen as the measured component values 3 or operation environment values 4. This will be clear from the examples below.
Figure 1 also illustrates a meteorology station 17 which may also be seen as both a wind turbine component 1 and an operation environment 5 but is primly used for measuring operation environment values 4.
It should be mentioned that in this description the wind turbine component 1 of which the component value 3 is measured is denoted 1 and referred to as wind turbine component. Other wind turbine components (or meteorological entities) having direct or indirect impact on the component value 3 is denoted 5 and referred to as an operation environment 5. Hence in one example e.g. a pitch motor could be denoted 1 if a component value 3 of this pitch motor is monitored. The same pitch motor could be denoted 5 if in another example e.g. the component value 3 to be measured is e.g. the load of the blade. The pitch motor has indirect impact on the load on the blade in that the pitch motor upon instructions e.g. from the wind turbine controller 10 changed the pitch angle of the blade 14 and thereby reduces or increases the load introduced from the wind on the blade 14.
Figure 2 illustrates a method according to an embodiment of the invention. In step 1 the sensor 9E is measuring a component value 3 of a wind turbine component 1 and as soon as the measurement is made it is communicated to a wind turbine controller 10. This is preferably what is understood by real-time measuring and communication.
In step 2 sensors 9A-n measures different operation environment values 4A-n of a plurality of values of one or more operation environments 5A-n. These operation environments 5 could e.g. be wind turbine components 1 or values related to meteorology / environment within the wind turbine 2 or outside the wind turbine 2 having direct or indirect impact on the value of the measured component value 3. The measurements made in step 2 are as in step 1 measured and communicated to the wind turbine controller 10 in real-time.
In step 3 the modelling algorithm 7 of the wind turbine controller 10 receives all operation environment values 4A-n from the sensors 9A-n obtained from the operation environments 5A-n. The modelling algorithm 7 then estimates at least one estimated component value 6 of the at least one wind turbine component 1. Hence the estimated component value 6 could be considered as a representation of the measured component value 3. This means that at least in some situations if the sensor 9E fails the wind turbine controller 10 may continue operation of the wind turbine based on the estimated component value 6.
In step 4 the wind turbine controller 10 compares the measured component value 3 and the estimated component value 6 to obtain a deviation value 8. In one example, if the deviation value 8 is zero the estimated component value 6 has the same value as the measured component value 3 measured directly at the wind turbine component 1. In this way the wind turbine component 1 is monitored by a model based monitoring system.
It should be mentioned that the estimation of the estimated component value 6 and the comparison to obtain the deviation value 8 are performed in real-time by the wind turbine controller 10 (step 3 and 4). As in step 1 and 2 this means that no data is stored before used by the wind turbine controller 10 according to the invention. With this said sometimes averaging of a plurality of measurements may occur. According to this invention use of such averaged measures are still considered as real-time
In step 5 the deviation value 8 is used for various purposes as will be explained below.
Further in a not illustrated step 0 parameter defined threshold values may be determined which could be done in at least two different ways. The first is based on the current status of the wind turbine 2 i.e. the current state of the wind turbine components 1 of the wind turbine 2. Hence if the wind turbine 2 is brand new and optimized after commissioning, then all wind turbine components 1 must be expected to be new and therefore cannot be expected to perform better than at this point in time. Alternatively the definition of parameter defined threshold values 16 of e.g. a five years old wind turbine 2 is made based on the current status of the wind turbine components 1. In this latter case it is assumed that none of the wind turbine components 1 are defect and is well functioning their age taken into consideration.
The definition of parameter threshold values 16 may be done manually or autonomous by a (not illustrated) definition module comprising software dedicated hereto. Both ways the definition is a matter of measure a parameter under a normal operation mode / conditions and then by threshold values 16 define a band of values in which the parameter is allowed to vary without setting a warning or an alarm. This band is also sometimes referred to as a hysteresis band.
Hence after the initialization step 0 the parameter defined threshold values 16 are determined and then if the deviation value 8 exceeds or go below these parameter defined threshold values 16 warning or alarms are set which may be used in the control of the wind turbine 2.
The second way of determining the parameter defined threshold values 16 is simply by copying these values 16 from a similar wind turbine 2. This is especially relevant if the inventive way of monitoring wind turbine components 1 is to be used in a wind park.
Prior to or during the initialisation step 0 the modelling algorithm 7 should be made. The modelling algorithm 7 is made by selecting input values and determines how to aggregate or by means of mathematic modelling model these input values to obtain a deviation value 8 which is possible to compare to the parameter defined threshold value 16 to obtain information of a wind turbine component 1.
In the example illustrated in figure 2 the wind turbine component 1 of which a value 3 is monitored is an inverter modules 1 of the power converter and the measured component value 3 hereof is its temperature.
In step 1 the sensor 9E is located at the power converter facilitating monitoring of the temperature 3 of the inverter module 1.
In step 2 the sensors 9B, 9C are measuring values of the temperature 4B and the level 4C of cooling fluid in the cooling system. The sensor 9A is measuring a value of the ambient temperature 4A outside the wind turbine 2 and the sensor 9D is measuring a value of the generator such as the generator speed 4D. According to this example the temperature 4B, the level of cooling fluid 4C and the generator speed 4D could be said to have direct impact on the measured temperature 3 of the inverter module 1. The ambient temperature 4A could be said to have only indirect impact on the inverter module temperature 3 in that it only has influence on the temperature e.g. in the nacelle or panel where the inverter module 1 is located and thereby the starting temperature of the inverter module 1. It says nothing about the load of the inverter module 1.
Accordingly examples of direct impact on the measured component value 3 may e.g. be: pitch activity has direct impact on temperature of pitch motor, wind speed has direct impact on blade root torque, leak of a hydraulic hose has direct impact on the pressure of the hydraulic system, etc.
Examples of indirect impact on the measured component value 3 may e.g. be: the wind applies to the blades 14, which makes the rotor rotate so that the wind turbine 2 generates power, thus the wind speed has an indirect impact on the active (current) power production. Likewise ambient temperature impacts the nacelle temperature directly and thereby indirectly impacts the temperature of each wind turbine component 1 inside the nacelle 14.
In this example the operation environment 5B is the cooling system of the power converter which has direct impact on the temperature 3 of the inverter module 1 since these wind turbine components 1 are physically connected. If this cooling system 5B does not manage to keep the right temperature of the inverter module 1 this may have indirect influence on the value of e.g. the pitch angle. The pitch angle may then have to pitch the blades out of the wind to reduce power production to secure that the power converter can operate under the specified temperature conditions.
In step 3 values of the ambient temperature 4A, the temperature of the cooling fluid 4B, the level of the cooling fluid 4C and the generator speed 4D is communicated to the wind turbine controller 10. Here it is input to a modelling algorithm 7 where the values 4A-C are mathematically manipulated as described below resulting in an estimate component value 6.
In step 4 the estimated component value 6 and the measured component value 3 is compared and the result of the comparison is a deviation value 8. In case the measured component value 3 is 75°C and the estimated component value 6 is 65°C the deviation value 8 is -10°C.
Below is examples of use of the deviation value 8 described under step 5. It should be note that step 5 is preferably also controlled by the wind turbine controller 10 even though this is not illustrated in figure 2.
In embodiments of the invention the deviation value 8 may be compared to a parameter defined threshold value 16 such as e.g. a predefined temperature level. In an example if the difference between the measured component value 3 and the estimated component value 6 exceeds a parameter defined threshold of e.g. 10°C an alarm is set. When an alarm is active it indicates a problem which has to be further investigated. In case the problem requires e.g. a shutdown of the wind turbine 2 it might be advantageous to add a parameter defined threshold which when exceeded sets a warning e.g. at 5°C in order for the wind turbine controller 10 to take actions preventing the deviation value 8 to increase further thereby preventing the shutdown.
Hence the outcome of the comparison in step 5 is preferably alarms or warnings, but could also be values which may be used in the control of the wind turbine e.g. for determining threshold values. Further step 5 does not have to include a comparison of values in that the change of the deviation value 8 in itself may provide valuable information relating to the wind turbine component which is monitored.
According to a very advantageous embodiment of the invention, the deviation value 8 is used to monitor wind turbine components 1 over a period of time. In short a deviation value 8 which suddenly changes more than expected may indicate a fault whereas when the deviation value 8 changes slowly over time it may indicate wear. Hence by a monitoring over time more details and thereby e.g. indication of root cause of the change of the deviation value 8 may be achieved.
As an example could be mentioned the monitoring of pressure in a hydraulic system where the following information could be derived from the change of the deviation value 8: deviation value 8 increases fast: a valve is malfunctioning or a sensor is defect deviation value 8 increases slow: a hose is beginning to be clogged or other which slowly over time change the pressure of system deviation value 8 decreases fast: a large leakage or burst on a hose deviation value 8 decreases slow: a smaller leakage or wear of the system such as e.g. a worn pump which pumping capacity is reduced.
In the example the limits for fast and slow will be defined by parameter settings which may vary from one system to another. In any case slow could be changes measured over minutes, hours, days, weeks or even years depending on what is monitored whereas fast typically will be a change measured in milliseconds, seconds or minutes.
Further in the example an increase of the deviation value 8 is when the measured component value 3 increases more than the estimated component value 6. Likewise a decrease of the deviation value 8 is when the measured component value 3 decreases more than the estimated component value 6.
In a yet further embodiment of the invention the deviation value 8 may be used for optimizing the control of a wind turbine 2. This is possible if deviation values 8 of different wind turbines 2 are compared. If a deviation value 8 in one wind turbine 2 deviates from similar deviation values of other wind turbines 2 e.g. of a wind park this is an indication that the wind turbine 2 having the deviated deviation value 8 is not running in an optimal manner.
In a yet further embodiment of the invention the deviation value 8 may be analysed by going back to the modelling algorithm 7 and see how the values 4A-D was manipulated. A deviation value 8 of -10°C in the above example could indicate at least one of the sensors 9A-E is not working properly. Thereby the deviation value 8 could be used in identification of faults
In relation to measurements made on a park level, such measurement may be normalized and compared to values of the different wind turbines 2 which then may indicate if something is wrong in the individual wind turbines 2.
In yet another embodiment of the invention the wind turbine 2 is considered as the wind turbine component 1 of which a value 3 is measured. This could be the case where the power production of the wind turbine 2 is the measured component value 3 and the wind speed and generator torque is the operation input values 4 which are input to the modelling algorithm 7. In this example the deviation value 8 would indicate if the wind turbine 2 produces the expected amount of power.
Figure 3A and 3B illustrates examples of the modelling algorithm 7 according to embodiments of the invention.
Figure 3A illustrates an example of a modelling algorithm 7 suitable for a hydraulic pressure deviation calculation. The hydraulic pressure 3 (measured component value) is measured by a sensor 9C in wind turbine component 1 such as the yaw system. In the illustrated example, the hydraulic pressure 3 is built up when a hydraulic motor is running and is temporarily decreasing if a yaw brake is activated. This is because the hydraulic oil flows from the hydraulic system into the hydraulic yaw brake, when the brake is activated. Once the brake piston is at the stop position, the pressure will start to increase again, because the pumps are continuously building up pressure.
The hydraulic pressure 3 is estimated by the modelling algorithm 7 implemented as a mathematical model 18 with two inputs 4A, 4B (operation environment values) and one output 6 (estimated component value) which in this example is an estimate of the hydraulic pressure 3. The two inputs are digital Boolean signals indicating if the hydraulic motor 5A (operation environment) is active and if the hydraulic yaw brake 5B (operation environment) is active respectively. Both Boolean signals are feed through individual low-pass filters 19, to model the system characteristics. Within the mathematical model 18, the output of the yaw brake low-pass filter is subtracted from the hydraulic pressure low-pass filter to generate the estimated value 6 of the hydraulic pressure. As explained above when the estimated value 6 is compared to the measured value 3 this is resulting in a deviation value 8.
Figure 3B illustrates an example of a modelling algorithm 7 suitable for a generator winding temperature deviation calculation. The temperature 3 (measured component value) of the generator winding 1 (wind turbine component) is measured by a temperature sensor 9C. The generator winding temperature 3 is affected by two variables - ambient temperature 4A (operation environment value) and active (current) power production 4B (operation environment value). The ambient temperature 4A and the active power 4B are used as shown by the modelling algorithm 7 implemented as a mathematical model 18 including a low-pass filter 19 to estimate a value 6 (estimated component value) of the generator winding temperature 3. As explained above when the estimated value 6 is compared to the measured value 3 this is resulting in a deviation value 8.
The modelling algorithm 7 is preferably always predefined and input hereto is also preferably always predefined. These are predefined and determined based on the wind turbine component 1 / component value 3 to be monitored. The modelling algorithm 7 and associated input may be defined external to the wind turbine 2 and uploaded to the wind turbine 2 e.g. if the root cause to an error is investigated, unexpected phenomenon occur, additional information in relation to control is required e.g. for optimizing, surveillance of components 1 or sensors 9, etc. In addition, specific wind turbine components 1 may be monitored this could be relevant if it is known that such component are failing, expensive to service or replace, causes the wind turbine 2 to stop, etc. The present invention is very advantageous in relation to such predictive maintenance.
List of reference numbers 1. Wind turbine component 2. Wind turbine 3. Measured component value 4. Operation environment value 5. Operation environment 6. Estimated component value 7. Modelling algorithm 8. Deviation value 9. Sensor 10. Wind turbine controller 11. Tower 12. Nacelle 13. Hub 14. Blades 15. Communication network 16. Parameter threshold value 17. Meteorology station 18. Mathematic model 19. Low-pass filter
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| JP2017220081A (en) * | 2016-06-09 | 2017-12-14 | 富士電機株式会社 | Sensor device, sensor system and measurement method |
| US11549491B2 (en) | 2017-06-14 | 2023-01-10 | Kk Wind Solutions A/S | Independent monitoring system for a wind turbine |
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