摘 要
随着风力发电技术的快速发展，风电机组的规模和复杂性不断增加，传统的故障诊断方法已难以满足高效、准确的需求。因此，本研究旨在通过结合谐波特征和深度学习技术，提升风电机组故障诊断的精确度和效率。为实现这一目标，我们采集了风电机组运行过程中的谐波数据，利用谐波分析提取故障特征。在模型改进方面，我们主要优化了卷积层的结构和参数，增强了网络对细微特征变化的敏感性。通过实验验证，本研究提出的方法在风电机组故障诊断中表现出了显著的优势。与传统方法相比，我们的方法在故障识别准确率和诊断速度上均有了显著提升。这一创新性的研究不仅为风电机组的稳定运行提供了有力保障，也为其他复杂系统的故障诊断提供了新的思路和方法。总体而言，本研究通过深度融合谐波分析和深度学习技术，为风电机组故障诊断领域做出了重要贡献。
关键词：风电机组故障诊断 谐波分析 改进卷积网络 深度学习
Abstract
With the rapid development of wind power generation technology, the scale and complexity of wind turbines are increasing, and traditional fault diagnosis methods are difficult to meet the needs of high efficiency and accuracy. Therefore, this study aims to improve the accuracy and efficiency of wind turbine fault diagnosis by combining harmonic characteristics and deep learning technology. In order to achieve this goal, we collect harmonic data during the operation of wind turbines and extract fault characteristics by harmonic analysis. In terms of model improvement, we mainly optimized the structure and parameters of the convolutional layer to enhance the sensitivity of the network to subtle feature changes. The experimental results show that the proposed method has significant advantages in the fault diagnosis of wind turbines. Compared with traditional methods, our method has significantly improved the accuracy of fault identification and the speed of diagnosis. This innovative research not only provides a strong guarantee for the stable operation of wind turbines, but also provides a new idea and method for the fault diagnosis of other complex systems. Overall, this study has made an important contribution to the field of wind turbine fault diagnosis through the deep fusion of harmonic analysis and deep learning technology.
Keyword:Wind turbine fault diagnosis Harmonic analysis Improved convolutional networks Deep learning
目 录
1绪论 1
1.1研究背景及意义 1
1.2国内外研究现状 1
2风电机组故障与谐波分析基础 1
2.1风电机组基本结构与工作原理 2
2.2风电机组常见故障类型及特征 2
2.3谐波产生机理及其在故障诊断中的应用 3
2.4谐波分析方法与技术手段 3
3改进卷积网络模型构建与优化 3
3.1传统卷积网络模型概述及局限性分析 3
3.2改进卷积网络模型设计思路与实现方法 4
3.3模型参数优化策略及实验验证 4
3.4改进卷积网络在故障诊断中的优势分析 5
4基于谐波和改进卷积网络的风电机组故障诊断方法 5
4.1故障诊断流程框架搭建 5
4.2谐波特征提取与处理方法研究 6
4.3改进卷积网络在故障诊断中的应用策略 6
4.4实验验证与结果分析 7
5结论 7
参考文献 9
致谢 10
