基于贝叶斯参数学习优化的海上风电机组动态可靠性预测

黄玲玲; 王全德; 应飞祥; 苗育植; 符杨; 刘璐洁

doi:doi:10.19912/j.0254-0096.tynxb.2024-1055

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基于贝叶斯参数学习优化的海上风电机组动态可靠性预测

更新时间：2025-12-17

- 基于贝叶斯参数学习优化的海上风电机组动态可靠性预测
- Issue 10, Pages: 703-713(2025)
- 作者机构：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1055
  CLC： M614
- Published Online：31 October 2025，
  
  Published：31 October 2025
- 稿件说明：
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黄玲玲, 王全德, 应飞祥, et al. 基于贝叶斯参数学习优化的海上风电机组动态可靠性预测[J]. 2025, (10): 703-713.
DOI：

黄玲玲, 王全德, 应飞祥, et al. 基于贝叶斯参数学习优化的海上风电机组动态可靠性预测[J]. 2025, (10): 703-713. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1055.

摘要

为提升风电机组可靠性预测的准确性

构建一种基于贝叶斯参数学习优化的动态可靠性预测模型。首先

提出融合单纯可靠性贝叶斯网络与卷积神经网络的状态信息数据处理方法

以解决“多元异质”状态信息的参数不确定性和提取状态数据特征的问题。其次

提出基于参数学习优化的贝叶斯改进方法处理参数不确定性

提高模型对未来可靠性水平的预测准确性。最终

构建的基于贝叶斯参数学习优化的动态可靠性预测模型能精确预测风电机组在一定时间尺度内的可靠性变化趋势。算例结果表明

与其他基准模型相比

所提的预测模型在预测风电机组可靠性变化趋势方面表现出更高的优越性

进一步验证其有效性。

Abstract

To improve the accuracy of reliability prediction for wind turbines

a dynamic reliability prediction model optimized through Bayesian parameter learning is developed. Firstly

a data processing method that integrates a pure reliability Bayesian network with a convolutional neural network was proposed to address the parameter uncertainty and feature extraction of "multivariate heterogeneous" state information. Secondly

a Bayesian improvement method based on parameter learning optimization was introduced to handle parameter uncertainty

enhancing the model’s accuracy in predicting future reliability levels. Finally

the constructed dynamic reliability prediction model

based on Bayesian parameter learning optimization

can precisely predict the reliability trend of wind turbine units over a certain time scale. Case study results show that

compared to other benchmark models

the proposed prediction model exhibits superior performance in predicting the reliability trend of wind turbine units

further validating its effectiveness.

关键词

Keywords

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