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Offshore wind turbine generator operation state evaluation method, device and system

A technology for wind turbines and operating status, applied in instruments, biological neural network models, data processing applications, etc., can solve problems such as strong volatility and non-stationary SCADA data of wind turbines, and achieve high accuracy

Pending Publication Date: 2022-02-15
中国绿发投资集团有限公司 +3
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, due to the complex and changeable offshore environment, the obtained SCADA data of wind turbines has the characteristics of non-stationarity and strong fluctuation, which makes the status assessment of offshore wind turbines much more difficult than that of onshore units. Therefore, the status assessment of offshore wind turbines It has always been a research hotspot in offshore wind power operation and maintenance. How to improve the accuracy of offshore wind turbine status assessment is a key issue to be solved urgently

Method used

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  • Offshore wind turbine generator operation state evaluation method, device and system
  • Offshore wind turbine generator operation state evaluation method, device and system
  • Offshore wind turbine generator operation state evaluation method, device and system

Examples

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Embodiment 1

[0059] An embodiment of the present invention provides a method for evaluating the operating state of an offshore wind turbine, including:

[0060] Utilize the preset wind turbine state parameter prediction model to predict the wind turbine state parameter, obtain the wind turbine state prediction parameter, and the wind turbine state parameter prediction model is established and obtained in conjunction with attention mechanism and GRU neural network;

[0061] Comparing the residual error of the wind turbine state prediction parameters and real parameters with the adaptive threshold calculated based on the wind turbine state parameters, the wind turbine state prediction is completed.

[0062] In a specific implementation of the embodiment of the present invention, the wind turbine state parameter prediction model includes a connected attention layer and a GRU layer (ie Attention+GRU model), and the GRU layer is a GRU neural network, that is, in the GRU An attention mechanism i...

Embodiment 2

[0101] Based on the same inventive concept as in Embodiment 1, the embodiment of the present invention provides a device for evaluating the operating state of offshore wind turbines, including:

[0102] The first prediction module is used to predict the state parameters of the wind turbine using the preset wind turbine state parameter prediction model to obtain the wind turbine state prediction parameter. The wind turbine state parameter prediction model is established by combining the attention mechanism and the GRU neural network acquired;

[0103] The second prediction module is used to compare the residual error of the wind turbine state prediction parameter and the real parameter with the adaptive threshold calculated based on the wind turbine state parameter to complete the wind turbine state prediction.

[0104] All the other parts are the same as in Example 1.

Embodiment 3

[0106] Based on the same inventive concept as in Embodiment 1, the embodiment of the present invention provides a system for evaluating the operating state of offshore wind turbines, including a storage medium and a processor;

[0107] The storage medium is used to store instructions;

[0108] The processor is configured to operate according to the instructions to execute the method according to any one of Embodiment 1.

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Abstract

The invention discloses an offshore wind turbine generator operation state evaluation method, device and system. The method comprises the steps: predicting a wind turbine generator state parameter through a preset wind turbine generator state parameter prediction model, and obtaining a wind turbine generator state prediction parameter, wherein the wind turbine generator state parameter prediction model is established and obtained by combining an attention mechanism and a GRU neural network; and comparing a residual error of the wind turbine generator state prediction parameter and the real parameter with a self-adaptive threshold value calculated based on the wind turbine generator state parameter to complete wind turbine generator state prediction. According to the method, false alarm triggering can be effectively avoided on the premise that the state prediction precision is guaranteed, and the accuracy of the operation state evaluation of the offshore wind turbine generator is remarkably improved.

Description

technical field [0001] The present invention belongs to the field of fault diagnosis, and specifically relates to a method, device and system for evaluating the operating state of an offshore wind turbine, in particular to a method, device and system for evaluating the operating state of an offshore wind turbine based on deep learning and an attention mechanism. Background technique [0002] In recent years, my country's offshore wind power has developed rapidly, gradually showing a trend from near sea to far sea, from shallow sea to deep sea. The development of offshore wind power in my country's coastal economically developed areas can effectively alleviate the contradictions of energy shortage and single power structure in the area, and also facilitate the large-scale local consumption and utilization of wind power. However, due to the complex and changeable offshore environment, the obtained SCADA data of wind turbines has the characteristics of non-stationarity and stro...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/06G06Q10/04G06N3/04
CPCG06Q10/0639G06Q10/04G06Q50/06G06N3/044
Inventor 丁显冯涛宫永立汤海宁
Owner 中国绿发投资集团有限公司
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