Gaussian process modeling based wind turbine shafting state monitoring method

A wind turbine, Gaussian process technology, applied in mechanical bearing testing, sustainable buildings, climate sustainability, etc., can solve the problem of poor condition monitoring effect, inability to adapt to random changes in wind speed of wind turbines, and time-varying shaft speed and load, etc. problems, to achieve the effect of avoiding the expansion of bearing damage, avoiding damage and failure

Inactive Publication Date: 2013-08-07
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

[0006] Aiming at the problem that the existing wind turbine shafting state monitoring and fault diagnosis technology cannot adapt to the random change of wind speed, the time-varying characteristics of shafting s

Method used

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  • Gaussian process modeling based wind turbine shafting state monitoring method
  • Gaussian process modeling based wind turbine shafting state monitoring method
  • Gaussian process modeling based wind turbine shafting state monitoring method

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specific Embodiment approach

[0046] The specific implementation of the present invention is described by taking the generator bearing in the shafting as an example, which specifically includes:

[0047] (1) Collect historical data of generator bearings during normal operation to form a historical data set (X,Y)={X t ,y t |t=1,2,...,n}. Among them, the input vector X is a four-dimensional vector, It is composed of four variables: wind speed, power, ambient temperature, and the temperature of the generator bearing at the moment; the output is the generator temperature y. There are n input and output data pairs in the historical data set, that is, training samples.

[0048] (2) Using the Gaussian process regression method and the generator bearing temperature historical data set (X,Y)={X i ,y i |i=1,2,…,n} to establish the generator bearing temperature model. First determine the covariance function as:

[0049] k ( X p ...

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Abstract

The invention discloses a Gaussian process modeling based wind turbine shafting state monitoring method in the field of wind turbine state monitoring. The technical scheme includes: collecting values of normal temperature of a bearing to be monitored and of correlated variables of the bearing temperature from historical data of a wind turbine SCADA system to form a bearing temperature vector set; building a bearing temperature model by the aid of a Gaussian process regression method; using the bearing temperature model for monitoring the bearing in real time, and using difference between the measured bearing temperature and the predicated temperature outputted by the model as predicated model residual; comparing the predicated model residual with a set residual threshold, and when the predicated model residual is larger than the residual threshold, judging the bearing to be abnormal; and otherwise, judging the bearing to be in a normal state. The method has the advantages that under the operation conditions of random changing of wind speed and time varying of rotating speed of a wind turbine shafting, states of bearings on the wind turbine shafting are analyzed and judged accurately, bearing fault alarm is sent timely, and maintenance complexity and cost are lowered.

Description

technical field [0001] The invention belongs to the field of wind turbine state monitoring, and in particular relates to a wind turbine shaft state monitoring method based on Gaussian process modeling. Background technique [0002] According to the "2012 China Wind Power Installed Capacity Statistics" report released by the China Wind Energy Association in March 2013, by the end of 2012, my country's installed capacity of wind turbines had ranked first in the world, with a total of 53,764 installed wind turbines and an installed capacity of 75,324MW. The development of my country's offshore wind power has also started. my country's first offshore wind farm, the Shanghai Donghai Bridge Wind Farm, was connected to the grid in 2010 for power generation. According to the national "Twelfth Five-Year" renewable energy plan, the scale of my country's offshore wind power will reach 5 million kilowatts in 2015; by 2020, the scale of offshore wind power will reach 30 million kilowatts...

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Application Information

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IPC IPC(8): G01M13/04
CPCY02B10/30
Inventor 郭鹏
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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