Processing

Please wait...

Settings

Settings

Goto Application

1. WO2021037751 - DETECTION OF ABNORMAL CONDITIONS ON A WIND TURBINE GENERATOR

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

CLAIMS

1. A method of detecting (100) abnormal conditions (120) of a blade (22) on a wind turbine generator (12), comprising acts of:

- measuring (200) sensory input (140) from the wind turbine generator (12);

- identifying (300) signatures (150) of abnormal conditions (155) of the blade (22) from the sensory input (140).

2. The method (100) according to claim 1, wherein the act of identifying (300) in-volves spectral analysis of the sensory input (140), statistical analysis of the sensory input (140), pattern recognition of the sensory input (140), or analysis by way of ma chine learning/artificial intelligence on the sensory input (140).

3. The method (100) according to claim 1 or 2, wherein the act of identifying (300) involves 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).

4. The method (100) according to any one or more of claims 1 to 3, wherein the act of identifying (300) is performed by labelling identified (100) signatures established by an external instrument.

5. The method (100) according to any one or more of claims 1 to4, wherein the act of measuring (200) is performed by use of one or more vibration sensors (50) arranged in one or more blades (22) of the wind turbine generator (12).

6. The method (100) according to any one or more of claims 1 to 5, wherein the act of measuring (200) is performed by use of an acoustic sensor (60).

7. The method (100) according to any one or more of claims 1 to 6, wherein the act of measuring (200) is performed by use of sensory input according to claim 5 and 6.

8. The method (100) according to any one or more of claims 1 to 7, wherein the act of measuring (200) is based on time-stamped (34) and synchronized sensory data (31).

9. The method (100) according to any one or more of claims 1 to 8, wherein the act of identifying (300) is performed locally in connection with measuring (200) sensory input (140).

10. The method (100) according to any one or more of claims 1 to 9, wherein the act of measuring (200) is performed by dynamically adjusting the sampling according to the signature (150) and abnormal condition (155).

11. The method (100) according to any one or more of claims 1 to 10, wherein the act of identifying (300) is of weather conditions.

12. The method (100) according to claim 11, wherein the act of identifying (300) is of rain conditions.

13. The method (100) according to claim 12, wherein the act of identifying (300) sig natures (150) involves identifying differential signatures (150) of rain being: no rain (160), light rain (162), moderate rain (163) and heavy rain (164).

14. The method (100) according to any one or more of claims 1 to 13, wherein the act of identifying (300) is performed by means of only accelerometers as vibration sen sors.

15. The method (100) according to claim 12, 13, and 14.

16. The method (100) according to any one or more of claims 1 to 13, wherein the

abnormal condition (120) is a condition of heavy rain (121) and wherein the act of identifying (300) signatures (150) is performed by way of artificial intelligence (AI) or machine learning (ML) fed by sensory input (140) provided by one or more vibration sensors (50) and/or one or more acoustic sensors (60) located in one or more blades (22) of the wind turbine generator (12).

17. A method of operating (500) a wind turbine generator (12) by an act of controlling (600) the wind turbine generator (12) as a function of detected abnormal conditions (100,120) according to the methods (100) of any one or more of claims 1 to 16.

18. The method (500) according to claim 17, wherein the act of controlling (600) in volves at least decreasing the rotational speed (610) below a certain limit (612), pitch ing (620), or yawing (630) during the abnormal condition (120).

19. The method (500) according to claim 18, wherein the act of controlling (600) is performed temporally during detected abnormal conditions and at lower power gener ation level.

20. The method (500) according to claim 19, wherein the detected abnormal condi tions are heavy rain conditions.

21. The method (500) according to claim 20, wherein the rotational speed temporarily is reduced to reduce the tip speed to below a speed of 200 km/h.

22. A system for detecting abnormal conditions (70) on a wind turbine generator (12), the system comprising

- sensory means (41) providing a sensory input (140) indicative of vibrations;

- means adapted for executing the acts according the method (100) of any one or more of claims 1 to 16.

23. The system (70) according to claim 22, wherein the sensory means (41) comprise one or more vibration sensors (50) and/or acoustic sensors (60) configured to be placed in a blade (22) of a wind turbine generator (12) and configured to measure vi brations and/or noise indicative of abnormal conditions (120).

24. The system (70) according to claim 22 or 23, wherein the sensory means (41, 45) comprise one or more means adapted for execution of the method acts according to one or more of claims 1 to 16.

25. A system for operating a wind turbine generator (90) comprising:

- the system for detecting abnormal conditions (70) according to any one or more of claims 22 to 24;

- means for controlling a wind turbine generator (12) as a function of detected abnor mal conditions (100, 120);

- means adapted for executing the method acts according to any one or more of claims 1 to 16.