Wind turbine generator fault prediction method based on deep learning

A technology of fault prediction and wind turbines, applied in the direction of neural learning methods, prediction, computer components, etc., can solve the problem of fault data feature learning ability, long-term high real-time state data processing ability, prediction efficiency and accuracy limitations, massive time-series data Effectively deal with issues such as means to achieve the effect of improving the time span

Inactive Publication Date: 2019-10-18
BEIJING HUADIAN TIANREN ELECTRIC POWER CONTROL TECH
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Problems solved by technology

However, due to the lack of effective processing methods for massive time-series data in the current wind turbine prediction method, there are limitations in the feature learning ability of fault data, the processin

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  • Wind turbine generator fault prediction method based on deep learning
  • Wind turbine generator fault prediction method based on deep learning
  • Wind turbine generator fault prediction method based on deep learning

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[0058] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0059] A fault prediction method for wind turbines based on deep learning of the present invention, the fault prediction method is aimed at the SCADA data of the operating state of the wind turbines, and the fault prediction method performs fault prediction based on a fault prediction model;

[0060] Such as figure 1 As shown, the fault prediction model includes a feature extraction module, a fault prediction module and a fully connected layer;

[0061] The feature extraction module is constructed by a multi-layer convolutional neural network structure (i.e. a deep convolutional neural network), and is used to extract fault features of multi-frame operating data graphs;

[0062] In the embodim...

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Abstract

The invention discloses a wind turbine generator fault prediction method based on deep learning, and the method aims at the SCADA data of the operation state of a wind turbine generator, and carries out the fault prediction based on a fault prediction model. The fault prediction method comprises the following steps: recombining SCADA data of the running state of the wind turbine generator into a running data graph; extracting fault features in the operation data graph by using a CNN; taking LSTM as a fault prediction model main body, and converting the single-frame fault feature into a time sequence fault feature; and completing the integration of time sequence fault features through a full connection layer, and obtaining a fault prediction result. For massive SCADA operation data, the CNN-LSTM framework is used for completing long-period prediction of the wind turbine generator faults, multi-dimensional high-sampling-frequency long-period time sequence SCADA data in the wind turbine generator can be processed, and accurate prediction of the long-period wind turbine generator faults can be effectively achieved.

Description

technical field [0001] The invention belongs to the technical field of fault prediction of wind power generators, and relates to a deep learning-based fault prediction method for wind turbines, in particular to a deep learning-based wind turbine fault prediction method for SCADA data of wind turbines. Background technique [0002] With the increasingly prominent problems of environmental pollution and energy crisis, wind energy, as a clean and renewable energy, has attracted more and more attention from all over the world, and has developed rapidly in recent years. While the installed capacity of the wind power industry continues to grow, it is also facing some new problems. Due to the limitation of the distribution of wind resources, the natural environment, the size and working mode of wind turbines themselves, wind turbines are mostly distributed in areas where there are few people and the environment is relatively changeable. had a great impact. According to statistics...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08G06Q10/04G06Q50/06
CPCG06N3/08G06Q10/04G06Q50/06G06N3/045G06F18/241
Inventor 赵计生范婧强保华莫烨
Owner BEIJING HUADIAN TIANREN ELECTRIC POWER CONTROL TECH
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