METHOD AND APPARATUS FOR MONITORING WIND TURBINE BLADES DURING OPERATION

• US 20140260634A1
• Filed: 03/15/2013
• Published: 09/18/2014
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: GE Infrastructure Technology, LLC (General Electric Company)
• Original Assignee: Digital Wind Systems Inc
• Inventors: Newman, John W.
• Granted Patent: US 9,194,843 B2
• Time in Patent Office: Days
• US Class Current: 73/645
• CPC Class Codes: F03D 17/00 Monitoring or testing of wind motors, e.g. diagnostics testing during commissioning of wind motors F03D13/30
• CPC Class Codes: G01N 29/22 Details , e.g. general constructional or apparatus details
• CPC Class Codes: Y02E 10/72 Wind turbines with rotation axis in wind direction

Abstract

A wind power turbine blade inspection system includes a sensor positioned on the blade root end bulkhead to receive airborne acoustic signals emanating from anomalies in rotating turbine blades during cyclic stress loading, a three axis accelerometer to determine the gravity vector and other sources of cyclic acceleration with respect to the acoustic signals and a signal analysis system configured to analyze the sensor and accelerometer signals to provide data for wind power asset management.

Claims

1. A method of detecting defects in a wind turbine blade, comprising steps of:

detecting a motion of a wind turbine blade;

detecting acoustic information that is emitted from the wind turbine blade; and

analyzing the acoustic information in conjunction with the motion of the wind turbine blade to identify a potential defect in the wind turbine blade.

2. A method of detecting defects in a wind turbine blade according to claim 1, wherein the step of detecting the motion of a wind turbine blade is performed using an accelerometer.

3. A method of detecting defects in a wind turbine blade according to claim 2, wherein the accelerometer is a three axis accelerometer.

4. A method of detecting defects in a wind turbine blade according to claim 2, wherein the accelerometer is mounted on the wind turbine blade.

5. A method of detecting defects in a wind turbine blade according to claim 4, wherein the accelerometer is constructed and arranged to provide identification data that permits the specific wind turbine blade on which it is mounted to be identified.

6. A method of detecting defects in a wind turbine blade according to claim 1, further comprising a step of transmitting data related to the motion of the wind turbine blade wirelessly.

7. A method of detecting defects in a wind turbine blade according to claim 1, wherein the step of detecting acoustic information is performed remotely from a remote distance.

8. A method of detecting defects in a wind turbine blade according to claim 1, wherein the step of detecting acoustic information comprises detecting an acoustic signal within an internal space of the wind turbine blade.

9. A method of detecting defects in a wind turbine blade according to claim 1, wherein the step of analyzing acoustic information in conjunction with the motion of the wind turbine blade is performed using a computer.

10. A method of detecting defects in a wind turbine blade according to claim 9, wherein the computer is positioned on the wind turbine blade and powered using a parasitic electrical generator that is also positioned on the wind turbine blade.

11. A method of detecting defects in a wind turbine blade according to claim 1, wherein the step of analyzing the acoustic information in conjunction with the motion of the wind turbine blade comprises correlating the acoustic information with the position of the wind turbine blade at a specific time.

12. A method of detecting defects in a wind turbine blade according to claim 11, further comprising a step of obtaining time information from a remote source.

13. A method of detecting defects in a wind turbine blade according to claim 12, wherein the remote source is a GPS satellite.

14. A method of detecting defects in a wind turbine blade according to claim 1, further comprising steps of obtaining acoustic and motion information with respect to at least one additional wind turbine blade, and comparing information from the respective blades to identify an outlier blade.