Variable pitch bearing fault identification method based on improved hidden Markov model

A Hidden Markov, pitch bearing technology, applied in character and pattern recognition, mechanical bearing testing, mechanical component testing, etc., can solve problems such as difficulty in online monitoring of pitch bearing status, achieve effective fault identification, reduce false positives, etc. Report rate, the effect of solving dynamic

Inactive Publication Date: 2020-01-17
ZHEJIANG WINDEY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that it is very difficult to realize the online monitoring of the state of the pitch bearing, and to provide a fault identification method for the pitch bearing of the wind power ge

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  • Variable pitch bearing fault identification method based on improved hidden Markov model
  • Variable pitch bearing fault identification method based on improved hidden Markov model
  • Variable pitch bearing fault identification method based on improved hidden Markov model

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Embodiment

[0043] combine figure 1 Flow chart, a kind of improved Hidden Markov Model-based wind turbine pitch bearing fault identification method proposed by the present invention comprises the following steps:

[0044] Step 1: Offline modeling, collect training sample set data, including wind turbine normal operating condition data and pitch bearing fault condition data, and use the collected training set data to train the hidden Markov model;

[0045] Step 2: Use the trained hidden Markov model to calculate the threshold for identifying unknown faults;

[0046] Step 3: Online identification. For the test data collected online, it includes the normal operating condition data of the wind turbine, the fault condition data of the pitch bearing, and another fault condition data of the pitch system during the operation of the wind turbine. As a monitoring sample, calculate the variance of the posterior probability of the test data;

[0047] Step 4: Comparing the posterior probability vari...

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Abstract

According to the invention, considering that unknown faults which are not considered in the operation process of a wind turbine generator are also likely to occur in addition to known faults, a wind generating set variable pitch bearing fault identification method based on an improved hidden Markov model is disclosed, comprises offline modeling and online identification, in the offline modeling step, a threshold statistic is defined based on the hidden Markov model, and the threshold statistic is used for identifying unknown faults. According to the method, a new threshold statistic is introduced, so that the probability that other unknown faults are mistakenly recognized as the variable-pitch bearing faults can be greatly reduced when online recognition is carried out on the variable-pitch bearing faults of the wind generating set. Therefore, compared with other existing methods, due to the fact that the time sequence of the data and the occurrence of unknown faults are fully considered, the method has higher accuracy for identifying the variable pitch bearing faults of the wind generating set.

Description

technical field [0001] The invention belongs to the field of wind power generation, in particular to a pitch bearing fault identification method based on an improved hidden Markov model. Background technique [0002] As one of the core components of the control system of modern large-scale variable-speed constant-frequency wind turbines, the pitch bearing plays an important role in the safe and stable operation of the unit. However, due to the randomness and uncertainty of wind speed, and the pitch bearing works with the blades in the hub, the harsh operating environment makes it one of the components with a high failure rate in wind turbines. Once the pitch bearing fails Or abnormal, it is likely to lead to serious accidents of wind turbines such as blade breakage and fan collapse. In addition, there are many online monitoring parameters of the pitch bearing itself, and the operating parameters are closely related to the complex operating conditions of the wind turbine, ma...

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Application Information

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IPC IPC(8): G06K9/62G01M13/04G01M13/045
CPCG01M13/04G01M13/045G06F18/214G06F18/295
Inventor 王琳陈棋孙勇傅凌焜
Owner ZHEJIANG WINDEY
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