DETECTION OF ABNORMAL CONDITIONS ON A WIND TURBINE GENERATOR

Abstract

Disclosed is a method of detecting abnormal conditions, e.g. of a blade or a rotor, on a wind turbine generator. Also disclosed is a system for detecting abnormal conditions, e.g. of a blade or rotor on a wind turbine generator.

Claims

1. A method of detecting abnormal conditions of a blade on a wind turbine generator, comprising acts of: measuring sensory input from the wind turbine generator; and identifying signatures of abnormal conditions of the blade from the sensory input.

2. The method according to claim 1, wherein the act of identifying signatures of abnormal conditions of the blade involves spectral analysis of the sensory input, statistical analysis of the sensory input, pattern recognition of the sensory input, or analysis by way of machine learning/artificial intelligence on the sensory input.

3. The method according to claim 1, wherein the act of identifying signatures of abnormal conditions of the blade involves identifying signatures by comparing normal signatures or pre-calibrated signatures of the sensory input with measured signatures of the sensory input.

4. The method according to claim 1, wherein the act of identifying signatures of abnormal conditions of the blade is performed by labelling identified signatures established by an external instrument.

5. The method according to claim 1, wherein the act of measuring sensory input from the wind turbine generator is performed by use of one or more vibration sensors arranged in one or more blades of the wind turbine generator.

6. The method according to claim 1, wherein the act of measuring sensory input from the wind turbine generator is performed by use of an acoustic sensor.

7. The method according to claim 1, wherein the act of measuring sensory input from the wind turbine generator is performed by use of one or more vibration sensors arranged in one or more blades of the wind turbine generator or an acoustic sensor.

8. The method according to claim 1, wherein the act of measuring sensory input from the wind turbine generator is based on time-stamped and synchronized sensory data.

9. The method according to claim 1, wherein the act of identifying signatures of abnormal conditions of the blade is performed locally in connection with measuring sensory input from the wind turbine generator.

10. The method according to claim 1, wherein the act of measuring sensory input from the wind turbine generator is performed by dynamically adjusting sampling according to the signature and abnormal condition.

11. The method according to claim 1, wherein the act of identifying signatures of abnormal conditions of the blade includes identifying signatures indicative of weather conditions.

12. The method according to claim 11, wherein the act of identifying signatures of abnormal conditions of the blade includes identifying signatures indicative of rain conditions.

13. The method according to claim 12, wherein the act of identifying signatures of abnormal conditions of the blade includes identifying differential signatures of rain being: no rain, light rain, moderate rain and heavy rain.

14. The method according to claim 1, wherein the act of identifying signatures of abnormal conditions of the blade is performed by means of only accelerometers as vibration sensors.

15. The method according to claim 1, wherein the abnormal condition is heavy rain of hail, and the act of measuring is performed by means of means of accelerometers as vibration sensors.

16. The method according to claim 1, wherein the abnormal condition is a condition of heavy rain; and wherein the act of identifying signatures is performed by way of artificial intelligence or machine learning fed by the sensory input provided by one or more vibration sensors and/or one or more acoustic sensors located in one or more blades of the wind turbine generator.

17. A method of operating a wind turbine generator by an act of controlling the wind turbine generator as a function of detected abnormal conditions according to the method of claim 1.

18. The method according to claim 17, wherein the act of controlling involves at least decreasing the rotational speed below a certain limit, pitching, or yawing during the abnormal conditions.

19. The method according to claim 18, wherein the act of controlling is performed temporally during detected abnormal conditions and at lower power generation levels.

20. The method according to claim 19, wherein the detected abnormal conditions are heavy rain conditions.

21. The method according to claim 20, wherein the rotational speed is temporarily reduced to reduce the tip speed below a speed of 200 km/h.

22. A system for detecting abnormal conditions on a wind turbine generator, the system comprising sensory means configured to provide a sensory input indicative of vibrations; and means adapted for executing the method steps of claim 1.

23. The system according to claim 22, wherein the sensory means include one or more vibration sensors and/or acoustic sensors configured to be placed in a blade of a wind turbine generator, and measure vibrations and/or noise indicative of abnormal conditions (120).

24. The system according to claim 22, wherein the sensory means include one or more means adapted for execution of the method of claim 1.

25. A system for operating a wind turbine generator comprising: the system for detecting abnormal conditions according to claim 22; means configured and arranged for controlling a wind turbine generator as a function of detected abnormal conditions; and means configured and arranged for executing the method acts according to claim 1.

Description

BRIEF DESCRIPTION OF DRAWINGS

[0116] Embodiments of the invention will be described in the figures, whereon:

[0117] FIG. 1 illustrates a method of detecting abnormal conditions;

[0118] FIG. 2 illustrates a further aspect of identifying signatures;

[0119] FIG. 3 illustrates a method of operating a wind turbine generator;

[0120] FIG. 4 illustrates a wind turbine generator and sensory arrangement;

[0121] FIG. 5 illustrates a sensory arrangement on blades;

[0122] FIG. 6 illustrates further aspects of a sensory arrangement;

[0123] FIG. 7 illustrates further optional or alternative aspects of sensory arrangement on a wind turbine generator, including a sensor node arrangement;

[0124] FIG. 8 illustrates a wind turbine generator with a sensory arrangement in interaction with a remote/cloud-based processor;

[0125] FIG. 9 illustrates a configuration of a sensor node;

[0126] FIG. 10 illustrates the estimation of power spectral densities (PSD) of sensor data from acoustic recording;

[0127] FIG. 11 exemplifies detection of a bird-strike, lighting strike, or other flaws as an abnormal condition;

[0128] FIG. 12 illustrates the estimation of power spectral densities (PSD) of different signatures of rain conditions sensor data from acoustic recordings;

[0129] FIG. 13 illustrates by way of example an algorithm or statistical setup for detecting abnormal conditions; and

[0130] FIG. 14 illustrates use of principal component analysis for detecting abnormal conditions.

[0131]

TABLE-US-00001 Item # Rotary device 10 Wind Turbine Generator (WTG) 12 Rotor 14 Rotor sector 18 Set of rotor blades 20 Rotor blade/blade 22 Dataset 30 Data 31 Timestamped data 32 Timestamp 34 Set of blade sensors 40 Sensor means 41 Blade sensor 42 Sensor node 45 Node Power 46 Vibration sensor 50 Acoustic sensor 60 System for detecting abnormal conditions 70 Computational means/Processor 72 Communication 74 Storage 76 System for operating a wind turbine generator 90 Detecting 100 Normal condition(s) 110 Abnormal condition(s) 120 Heavy rain/precipitation 121 Sensory input 140 Signatures 150 Signatures of normal conditions 152 Signatures of calibrated conditions 154 Signatures of abnormal conditions 155 No Rain Signature 160 Light Rain Signature 162 Moderate Rain Signature 163 Heavy Rain Signature 164 Measuring 200 Synchronizing 240 Identifying 300 Operating 500 Controlling 600 Rotational speed 610 Rotational speed limit 612 Pitching 620 Yawing 630

DETAILED DESCRIPTION OF THE INVENTION

[0132] FIG. 1 illustrates a method of detecting 100 abnormal conditions 120 of a blade 22 on a wind turbine generator 12 as illustrated in FIGS. 4 to 9. The method 100 comprising an act of measuring 200 sensory input 140 from the wind turbine generator 12.

[0133] The method comprising an act of identifying 300 signatures of abnormal conditions 155 from the sensory input 140 say experienced by the blade 22.

[0134] FIG. 2 illustrates further aspects of identifying signatures 150 of abnormal conditions 120, that is abnormal signatures 155.

[0135] The act of measuring 200 may involve an act of synchronizing 240 the sensory input. The act of synchronizing 240 may in principle be performed as part of identifying 300.

[0136] As will be exemplified, the act of identifying 300 may involve identifying signatures 150 by comparing normal signatures 152 or pre-calibrated signatures 154 of sensory input 140 with measured signatures 150 of sensory input 140.

[0137] The act of identifying a signature 150 may involve spectral analysis of the sensory input 140, it may involve statistical analysis of the sensory input 140, it may involve pattern recognition of the sensory input 140, it may be with use of machine learning (ML), or be with use of artificial intelligence (AI), on the sensory input 140.

[0138] The act of identifying 300 a signature 150 may be that of a condition of heavy rain or other heavy precipitation 121. The act of identifying 300 signatures 150 may be by way of artificial intelligence (AI) or machine learning (ML) fed by sensory input 140 provided by one or more sensory inputs 140 from a wind turbine generator 12.

[0139] FIG. 3 illustrates a method of operating 500 a wind turbine generator 12 (not shown, but exemplified in other figures) by an act of controlling 600 the wind turbine generator 12 as a function of detected abnormal conditions 120, according to methods or acts 100 as outlined.

[0140] The act of controlling 600 may involve at least decreasing the rotational speed 610 below a certain limit 612 (not shown), pitching 620 one or more blades, or yawing 630 the nacelle during the abnormal condition 120.

[0141] FIG. 4 illustrates a wind turbine generator 12 comprising a rotor 14 with a set of rotor blades 20. The set of rotor blades 20 comprises here three rotor blades 22A, 22B, 22C. The wind turbine generator 12 may operate during normal conditions 110 and experience abnormal conditions 120. The abnormal conditions may be temporarily in nature.

[0142] FIG. 5 illustrates a sensory arrangement on blades 22A, 22B, 22C on a wind turbine generator 12 with a rotor 14. The blades 22A, 22B, 22C is a set of rotor blades 20. Each blade 22A, 22B, 22C comprises a set of blade sensors 40A, 40B, 40C as sensor means 41. In the present case, each set of blade sensors 40A, 40B, 40C comprises a blade sensor 42A, 42B, 42C. The blade sensor may be a vibration sensor 50 (not shown) or an acoustic sensor 60 (not shown).

[0143] The sensory arrangement may be part of a system for detecting abnormal conditions 70.

[0144] A blade sensor 42 is configured to be in communication 74 with a controller or computational means 72. The communication 74 may be wired or wireless as illustrated here.

[0145] FIG. 6 illustrates further aspects of sensory arrangements on a wind turbine generator 12. There may be a timer or clock configured to provide data 31 from the sensory means 41 with a time stamp 34, which thus provides timestamped data 32 or synchronized data for processing by the computational unit 72.

[0146] The wind turbine generator (WTG) 12 is with a rotor 14 and a set of rotor blades 20. The set of rotor blades 20 is with three rotor blades 22A, 22B, 22C.

[0147] Each blade 22A, 22B, 22C comprises a set of blade sensors 40A, 40B, 40C. In the present case each set of blade sensors 40A, 40B, 40C comprises a blade sensor 42A, 42B, 42C.

[0148] A further sensor means 41 is shown. In this embodiment, the further sensor is a rotary sensor (RPM-sensor or vibration sensor), such as a high sampling speed sensor measuring the rotational speed 610. The system may be configured for an act of synchronizing 240, as shown in FIG. 2 or variations thereof, and based on sensors 42ABC, and synchronization is performed against at least one other sensor 41.

[0149] The sensory arrangement may be part of a system for detecting abnormal conditions 70. The computational means 72 or controller may be a single unit or distributed as illustrated here.

[0150] FIG. 7 illustrates further optional or alternative aspects of a sensory arrangement on a wind turbine generator 12.

[0151] The sensors 42 may be implanted as a sensor node 45 (see FIG. 9) as illustrated by sensor nodes 45A, 45B, 45C. A sensor node may have a vibration sensor 50, and an acoustic sensor 60. There may also be additional sensor means 41.

[0152] A sensor node 45 may comprise essential processing 72 and be adapted for performing the acts or at least part of the acts of detecting 100.

[0153] A set of sensors 40 may be understood as a sensor node 45 with one or more sensors. Such a sensor node 45 may comprise processors or means to configure, collect, store and process generated sensor data. A sensor node 45 may have communication means to communicate with a controller (not shown) or other sensor nodes. A sensor node 45 may have means to synchronize 240 (as illustrated previously) say sensors 50, 60.

[0154] FIG. 8 illustrates a wind turbine generator 12 with a sensory arrangement in interaction with a remote/cloud-based processor 72 as part of a system for detecting abnormal conditions 70.

[0155] The rotary device 10 comprises a set of rotor blades 20. The set of rotor blades 20 consists of three rotor blades 22A, 22B, 22C.

[0156] Each blade 22A, 22B, 22C comprises a set of blade sensors 40A, 40B, 40C. In the present case, each set of blade sensors 40A, 40B, 40C comprises a blade sensor 42A, 42B, 42C.

[0157] The data sets 30 are processed by computational means 72. The wind turbine generator 12 may have a clock for generating a timestamp 34. In this case the time stamp is further synchronized and delivered from a global time server. Hence, the datasets 30 may be timestamped data 32. Alternatively, each sensor node 45 may be synchronized and the timestamp 34 may be applied at sensor node level.

[0158] The system 70 may interact with an operator system, a mobile device, a client server and a storage or database via a cloud/connection service. Further access or mirroring or monitoring may be available via the cloud for long term monitoring, alerts or service programmes.

[0159] The methods and acts of detecting 100 disclosed herein may be performed in a single processor 72 device or be distributed as illustrated here.

[0160] FIG. 9 illustrates a configuration of a sensor node 45. There is a node power 46 management layout, which may include a source of energy, storage of energy, a controller of power management and an interface for configuration/control and possibly charging.

[0161] The sensor node 45 is illustrated with a processor or computational means 72. The sensor node 45 includes sensory means 41 generating data 31. Illustrated is a vibration sensor 50, which in this case has three lines of output and in example could be a triaxial accelerometer. Optionally, there is an acoustic sensor 60. Optionally, there are further sensor(s) means 41.

[0162] The computational means 72 may be adapted to perform instructions to perform one or more, or all of the acts as outlined to perform detection 100 and to support or fully determine abnormal conditions 120.

[0163] The sensor node 45 is configured with communication means 74 and here with storage means 76.

[0164] FIG. 10 exemplifies detection 100 of and operation during heavy rain as an abnormal condition.

[0165] The detection 100 of abnormal conditions is exemplified by the following acts.

[0166] There is an initial act of setting a start process every set time or minutes. There is an act of making a microphone be in a state of listening.

[0167] There is an act of checking if a microphone wakeup threshold is exceeded. In a positive case proceed to a next check or in a negative case return to a previous act or start.

[0168] There is an act of checking if the WTG is in operation. In a positive case proceed to a next check or in a negative case return to a previous act or start.

[0169] There is an act of checking if the blade tip speed >200 km/h. In a positive case proceed to a next check or in a negative case return to a previous act or start.

[0170] There is an act of checking if a sound threshold is exceeded. In a positive case proceed to a next check or in a negative case return to a previous act or start.

[0171] There is an act of checking if an accelerometer vibration signature 150 is present. In a positive case proceed to a next check or in a negative case return to a previous act or start.

[0172] There is an act of checking if a heavy rain signature 150 is present. In a positive case proceed to a next check or in a negative case return to a previous act or start.

[0173] In case of heavy rain detection 100 there is an act of sending the detection to a WTG controller. In a negative case, return to a previous act or start.

[0174] There is an act of, if needed, controlling the WTG by a corrective action such as a blade pitch angle, a rotor speed reduction or combinations thereof. If needed, then WTG controller will change blade pitch and/or reduce rotor speed.

[0175] There is an act of ending (completing) detection.

[0176] FIG. 11 exemplifies detection 100 of abnormal conditions as a bird-strike, lighting strike, or other flaws.

[0177] The detection 100 of abnormal conditions is exemplified by the following acts.

[0178] There is an initial act of setting a start process every set time or minutes. There is an act of making a microphone be in a state of listening.

[0179] In this case there is an act of making a microphone (optionally) and vibration sensor set to listening.

[0180] There is an act of checking if a microphone wakeup threshold is exceeded. In a positive case proceed to a next check or in a negative case return to a previous act or start.

[0181] There is an act of checking if a sound (acoustic) threshold peak is instantaneous. In a positive case proceed to a next check or in a negative case return to a previous act or start.

[0182] There is an act of checking if an accelerometer vibration peak is instantaneous. In a positive case an abnormal condition is detected or in a negative case return to a previous act or start.

[0183] With an abnormal condition detected, the abnormal condition is classified as e.g. a bird collision, a lightning strike, or other flaws.

[0184] There is an act of checking which corrective action or communication is to be performed such as inform an owner and/or an operator.

[0185] There is an act to end detection.

[0186] FIG. 12 illustrates the estimation of power spectral densities (PSD) of sensor data from acoustic recordings, with the similar signatures for acceleration data, for different rainfall intensities. There is a case of a “No Rain”-signature 160, and also a signature of normal conditions 152. There is a case of a “Light Rain”-signature 162, and also a signature of normal conditions 152. There is a case of a “Heavy Rain”-signature 164, and also a signature of abnormal conditions 155. The arrows are located at a frequency band that mostly differentiates among rainfall levels (i.e. 600-1200 Hz approx.).

[0187] A person skilled in the art will by way of collecting and classifying power spectral densities with actual conditions be able to identify or distinguish “heavy rain” both qualitatively (i.e. rain from no rain) and quantitatively (i.e. heavy rain from light rain).

[0188] FIG. 13 illustrates by way of example a setup for detecting 100 abnormal conditions.

[0189] There are acts of measuring 200 and identifying 300 abnormal conditions signatures 155. The setup may be performed to enable machine learning to perform the identification.

[0190] There is an act of collecting data 31, from e.g. vibration sensors 50 and/or acoustic sensors 60. The data 31 may be time stamped data 32.

[0191] As such there is an act of measuring 200 input from sensors. There is an act of comparing or generating associated conditions (i.e. “Heavy Rain”-, “Light Rain”-, or “No Rain”-conditions.

[0192] Furthermore, there is an act of identifying 300 signatures 150 of abnormal conditions 155.

[0193] The act of identifying 300 involves acts of prepressing data and/or use of metrics engineering. There is an act of associating vibration and acoustic features. Such features may be an amplitude, a correlation length other associable features. There is an act of quantifying conditions of characteristics based on the previously associated conditions.

[0194] The act of identifying 300 involves acts of using machine learning algorithms. Machine learning will classify (say cluster) signatures 150. Illustrated in example is an illustration of clustering/classification according to “No Rain”-conditions (triangles), and “Rain”-conditions (circles and squares), where “Heavy Rain” conditions (squares) are abnormal conditions 155.

[0195] FIG. 14 illustrates by way of example signatures 150 for detecting 100 abnormal conditions.

[0196] The signatures 150 are established by detecting a projection of transformed vibration data featuring values of one wind turbine into a lower 2 dimensional space by using Principal Components Analysis (PCA).

[0197] Each point represents a fixed duration of vibration data of the same wind turbine at a different date, here 1 minute.

[0198] The colour (intensity) represents the rain intensity in mm/h. The figure clearly shows the differential signature of rain and its intensity, which is stratified as colours going from dark (No Rain 160) to green (Light Rain 162 and Moderate Rain 163) to yellow (Heavy Rain 164); as indicated by placed arrows in the figure.

[0199] In this particular case only data from accelerometers in a blade of a wind turbine is used. A person skilled (i.e. including a statistician) in the art will appreciate multiple different algorithms or packages to perform the statistical analysis (here classical principal component analysis) or variants thereof. The interesting observation is that rain conditions clearly are distinguishable by use of data from accelerometers, which accelerometers may be readily or already installed and used for other purposes.