METHOD OF MONITORING THE CONDITION OF A WIND TURBINE

• US 20150322924A1
• Filed: 12/18/2013
• Published: 11/12/2015
• Est. Priority Date: 12/18/2012
• Status: Abandoned Application

• Current Assignee: GE Renewable Technologies Wind B.V. (Alstom SA)
• Original Assignee: Alstom Renewable Technologies
• Inventors: MENASANCH DE TOBARUELA, Jorge
• Application Number:
  US14/650,556
• Publication Number:
  US 20150322924A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: F03D 1/06 Rotors
• 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: F03D 3/06 Rotors
• CPC Class Codes: G05B 2219/2619 Wind turbines
• CPC Class Codes: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
• CPC Class Codes: Y02E 10/72 Wind turbines with rotation axis in wind direction
• CPC Class Codes: Y02E 10/74 Wind turbines with rotation axis perpendicular to the wind direction

Abstract

A method of monitoring the condition of a wind turbine. The method includes the steps of selecting a plurality of measurable parameters indicative of the operational state of the wind turbine; recording measures of a variable indicative of the condition of the wind turbine during a period of normal operation thereof, and deriving corresponding values of a characteristic quantity from said measures; recording measures of the parameters during the same period; identifying a correlation between the characteristic quantity and at least one correlated parameter; and from said correlation, defining the expected value of the characteristic quantity as a target function that is a function of said at least one correlated parameter.

Claims

1. A method of monitoring the condition of a wind turbine, comprising the steps of:

selecting a plurality of measurable parameters indicative of the operational state of the wind turbine;

recording measures of a variable indicative of the condition of the wind turbine during a period of normal operation thereof, and deriving corresponding values of a characteristic quantity from the measures;

recording measures of the parameters during the same period;

identifying a correlation between the characteristic quantity and at least one correlated parameter;

from the correlation, defining an expected value of the characteristic quantity as a target function that is a function of the at least one correlated parameter.

2. The method according to claim 1, wherein the recording steps comprise recording a time series of the variable, determining a corresponding time series of the characteristic quantity, and recording a corresponding time series of the parameters, the method further comprising the steps of:

defining a normalized time series of the characteristic quantity as a time function that is a function of the characteristic quantity as represented by the time series of the characteristic quantity, and is also a function of the target function as applied to the corresponding time series of the at least one correlated parameter; and

defining an alarm function which is a function of the time function.

3. The method according to claim 2, wherein the time function is either a ratio, a difference or a relative difference between the characteristic quantity and the target function.

4. The method according to claim 2, comprising the step of approximating the time function with a probability distribution.

5. The method according to claim 4, wherein the time function is approximated with a normal probability distribution.

6. The method according to claim 4, wherein the alarm function is a constant equal to a sum of a mean and a standard deviation for the probability distribution.

7. The method according to claim 2, comprising the steps of:

measuring the variable and determining the corresponding characteristic quantity;

measuring the at least one correlated parameter;

applying the target function to the measure of the at least one correlated parameter to obtain an expected value of the characteristic quantity;

applying the time function to the characteristic quantity and the expected value thereof;

applying the alarm function to a previous result of the time function;

computing the difference between the previous result said results of the time function and the alarm function applied to the previous result of the time function; and

triggering a first alarm if the difference is bigger than a first predetermined amount.

8. The method according to claim 7, comprising the step of triggering a second alarm if the difference is bigger than a second predetermined amount.

11. The method according to claim 1, wherein the recording steps comprise selecting a range for a rotational speed of the rotor of the wind turbine and performing measurements within the range.

12. The method according to claim 1, wherein the indicative variable is any of an amplitude, speed or acceleration indicative of a vibration of an element of the wind turbine.

13. The method according to claim 12, wherein a frequency of the vibration is filtered by a predetermined range of frequencies.

14. The method according to claim 1, wherein the parameters are selected from a rotational speed of a rotor of the wind turbine, a power output, a rotational speed of a high-speed shaft, an ambient temperature, a wind speed, an atmospheric pressure, a humidity level or a corrosion of a surface of wind turbine blades.

15. The method according to claim 1, wherein correlations between the characteristic quantity and the at least one correlated parameter are identified by employing a self-learning algorithm.

16. The method according to claim 6, comprising the steps of:

measuring the variable and determining the corresponding characteristic quantity;

measuring the at least one correlated parameter;

applying the target function to the measure of the at least one correlated parameter to obtain an expected value of the characteristic quantity;

applying the time function to the characteristic quantity and the expected value thereof;

applying the alarm function to a previous result of the time function;

computing the difference between the previous result of the time function and the alarm function applied to the previous result of the time function;

triggering a first alarm if the difference is bigger than four standard deviations.

17. The method according to claim 16, wherein a second alarm is triggered when the difference is bigger than seven standard deviations.

18. The method according to claim 6, comprising the steps of:

measuring the variable and determining the corresponding characteristic quantity;

measuring the at least one correlated parameter;

applying the target function to the measure of the at least one correlated parameter to obtain an expected value of the characteristic quantity;

applying the time function to the characteristic quantity and the expected value thereof;

applying the alarm function to a previous result of the time function;

computing the difference between the previous result of the time function and the alarm function applied to the previous result of the time function;

triggering a first alarm if the difference is bigger than a first predetermined amount;

triggering a second alarm if the difference is bigger than seven standard deviations.

19. The method according to claim 2, wherein the recording steps comprise selecting a range for a rotational speed of a rotor of the wind turbine and performing measurements within the range.

20. The method according to claim 2, wherein the indicative variable is any of an amplitude, speed or acceleration indicative of a vibration of an element of the wind turbine, and a frequency of the vibration is filtered by a predetermined range of frequencies.

21. The method according to claim 2, wherein the parameters are selected from a rotational speed of a rotor of the wind turbine, a power output, a rotational speed of a high-speed shaft, an ambient temperature, a wind speed, an atmospheric pressure, a humidity level or a corrosion of a surface of blades of the wind turbine.

22. The method according to claim 2, wherein correlations between the characteristic quantity and the at least one correlated parameter are identified by employing a self-learning algorithm.

9-10. -10. (canceled)