Method and system for predicting wind machine state based on Bayesian reasoning mode

A wind turbine and state technology, which is applied in the field of predicting the state of wind turbines based on Bayesian reasoning, can solve problems such as the prediction of difficult wind turbine fault operating states.

Active Publication Date: 2014-08-27
瀛能科技(上海)有限公司 +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0007] In view of this, the technical problem to be solved by this application is to provide a method and system for pr

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  • Method and system for predicting wind machine state based on Bayesian reasoning mode
  • Method and system for predicting wind machine state based on Bayesian reasoning mode
  • Method and system for predicting wind machine state based on Bayesian reasoning mode

Examples

Experimental program
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Example Embodiment

[0041] Example one

[0042] Such as figure 1 As shown, the method for predicting the fault state of a wind turbine based on Bayesian inference described in Embodiment 1 of this application includes:

[0043] Step 101: Obtain the operating data of all the wind turbines to be tested in the wind farm within a certain period of time before a specified time point, and classify the operating data according to the preset operating state type of the wind turbine, and then The operation state type corresponding to each of the wind turbines in each time period is obtained according to the same time period in the certain period of time.

[0044] In step 101, the specified time point may specifically be the time when a certain failure of the wind turbine actually causes shutdown as the time point, or it may be a certain time during the normal operation of the wind turbine. As the time point, no limitation is made here. The operating data is specifically the correlation data between the operati...

Example Embodiment

[0051] Example two

[0052] Combine figure 2 As shown in the second embodiment of this application, a method for predicting the fault state of a wind turbine based on Bayesian inference, the method includes:

[0053] Step 201: Obtain the operating data of the wind turbine to be tested within a certain period of time, divide the operating data into multiple operating feature items according to preset dividing conditions, and obtain the wind power for the same period of time within the certain period of time. The operating characteristic items corresponding to the machine in each time period.

[0054] For step 201, the operation data of the wind turbine to be tested in a certain period of time is obtained, specifically: the wind speed data of the wind turbine to be tested in a certain period of time and the power generation, blade speed, and pitch angle of the wind turbine to be tested Wait for the operating parameters to perform a correlation test, and obtain the operating data gene...

Example Embodiment

[0064] Example three

[0065] The specific application of the method for predicting the fault state of wind turbines based on Bayesian inference will be described in detail below in conjunction with the figure:

[0066] It should be noted that when the operating data is obtained, the more types of correlation tests, the more accurate the operating status of the wind turbine reflected. Therefore, in this embodiment, the The data of three correlation tests are obtained as operating data: the correlation test between wind speed data and the power generation, blade speed, and pitch angle of the wind turbine under test.

[0067] Step 1: Take the time when the wind turbine has a certain failure and cause the shutdown as the specified time point, and obtain the three types of operating data generated by the three types of correlation tests 25 days before the specified time point, according to the following table The preset division conditions shown divide the three types of operating data ...

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Abstract

The invention discloses a method and system for predicting a wind machine state based on a Bayesian reasoning mode. The method comprises the steps that operation data of a wind machine in a certain time are obtained and are classified; the wind machine operation state type of each time period is subjected to probability statistics processing, probability values are generated, and overall operation state types in the time periods are determined; the overall operation state types are subjected to Bayesian network modeling, and the priori condition probability of the overall operation state types is generated; and the priori condition probabilities of the time periods and the probability values of the overall operation state types are subjected to probability processing in a Bayesian reasoning mode, a joint probability value is generated, the state distribution curve of the wind machine is established, and according to the state distribution curve, the wind machine is subjected to fault state prediction. The problem that the fault operation state of a wind power generator cannot be predicted easily is solved.

Description

technical field [0001] The present application relates to the field of state monitoring of wind turbines, and more specifically relates to a method and system for predicting the state of wind turbines based on Bayesian reasoning. Background technique [0002] Driven by natural wind, wind turbines operate and generate electricity. During the operation of wind turbines, they will be affected by external environmental factors and their own working conditions. For example, due to real-time changes in natural wind, sometimes the wind speed is lower than that of wind turbines. The cut-in wind speed makes it difficult for the wind turbine to start; sometimes the wind speed is greater than the limit wind speed that the wind turbine can withstand, causing damage to the wind turbine, causing the wind turbine to fail or even shut down; another example: the wind turbine runs for a long time, cause wind turbines to malfunction. [0003] For the operation failure of the wind turbine, usu...

Claims

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Application Information

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IPC IPC(8): F03D11/00
CPCY02E10/722
Inventor 叶翔
Owner 瀛能科技(上海)有限公司
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