A data-driven multivariable fault prediction method for wind turbines
A wind turbine and fault prediction technology, applied in data processing applications, electrical digital data processing, special data processing applications, etc., can solve the problem of low prediction accuracy and achieve the effect of improving prediction accuracy
Active Publication Date: 2019-05-17
SHANXI UNIV +1
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[0005] In order to solve the problem of low prediction accuracy of existing data-driven fault prediction methods, the present invention provides a data-driven multivariable fault prediction method for wind turbines
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[0058] Based on a data-driven multivariable fault prediction method for wind turbines, this method is implemented by the following steps:
[0059] Step 1: Collect state data of the monitored wind turbine components, and extract the following feature quantities from the state data: time-domain feature quantities, frequency-domain feature quantities, and complexity feature quantities;
[0060] Step 2: Use the five-point moving average method to perform noise reduction processing on the feature quantities, thereby eliminating the random influence between the feature quantities; the noise reduction processing formula is expressed as follows:
[0061]
[0062] In the formula (1): x(k) represents the feature quantity data sequence before noise reduction; x'(k) represents the feature quantity data sequence after noise reduction; k=1,2,...,n;
[0063] During the stable operation period of the wind turbine components, calculate the mean value of the state data corresponding to the r...
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The invention relates to a wind turbine generator failure prediction method, in particular to a wind turbine generator multivariate failure prediction method based on data driving. The problem that an existing failure prediction method based on data driving is low in accuracy is solved. The wind turbine generator multivariate failure prediction method based on data driving comprises the following steps that 1, state data collecting is conducted on monitored wind turbine generator parts; 2, denoising processing is conducted on the characteristic quantity by adopting a five-point sliding average method; 3, a correlation degree R of the characteristic quantity and remaining life prediction is calculated; 4, a multivariate least square support vector machine prediction model is built; 5, optimization is conducted on a regularization parameter gamma and a nuclear parameter sigma<2> of the multivariate least square support vector machine prediction model; 6, the effectiveness of the multivariate least square support vector machine prediction model is verified; 7, the remaining effective life of the wind turbine generator parts is predicted. The wind turbine generator multivariate failure prediction method based on data driving is suitable for wind turbine generator failure prediction.
Description
technical field [0001] The invention relates to a fault prediction method for wind turbines, in particular to a data-driven multivariable fault prediction method for wind turbines. Background technique [0002] Failure prediction refers to predicting future failure trends or remaining useful life based on historical and current data. Wind turbine failure prediction refers to the use of fault reasoning methods to predict future failures of wind turbines when the wind turbine has not yet failed. Compared with fault monitoring and diagnosis, the biggest advantage of fault prediction is: if the time advance of fault prediction is long enough, and the cause and location of the fault are accurate enough, on-site operators can take corresponding measures to prevent the occurrence of faults, which improves the reliability of wind turbines. stability. However, most of the operating states of wind turbines have the characteristics of severe nonlinearity, time-varying, structural and...
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IPC IPC(8): G06F17/50G06Q50/06
CPCG06F30/367G06Q50/06Y02E60/00
Inventor 王灵梅李其龙孟恩隆孟秉贵申杰兵苏华
Owner SHANXI UNIV