摘要
随着电力系统规模的不断扩大和复杂性的持续提升，电力设备故障诊断技术已成为保障电网安全稳定运行的关键环节之一然而传统故障诊断方法在面对高维、非线性数据时存在局限性，难以满足现代电力系统对精准性和实时性的要求基于此，本文聚焦于深度学习技术在电力设备故障诊断中的应用，旨在通过构建高效智能的诊断模型，提升故障识别的准确率与效率研究中采用卷积神经网络（CNN）与长短时记忆网络（LSTM）相结合的方法，充分利用CNN对空间特征的提取能力和LSTM对时间序列信息的捕捉优势，设计了一种混合深度学习框架该框架能够同时处理多源异构数据，并有效挖掘电力设备运行状态中的潜在模式实验结果表明，所提方法在多种典型电力设备故障场景下均展现出优异的诊断性能，其准确率较传统方法提升了15%以上此外，本文还提出一种基于迁移学习的优化策略，显著降低了对大规模标注数据的依赖，增强了模型的泛化能力综上所述，本研究不仅为电力设备故障诊断提供了新的技术路径，也为深度学习在工业领域的应用拓展了思路
关键词：电力设备故障诊断；深度学习；卷积神经网络
Abstract
With the continuous expansion of power system scale and increasing complexity, fault diagnosis technology for power equipment has become one of the key components to ensure the safe and stable operation of the power grid. However, traditional fault diagnosis methods exhibit limitations when dealing with high-dimensional and nonlinear data, making it difficult to meet the modern power system's requirements for precision and real-time performance. In response to this challenge, this study focuses on the application of deep learning techniques in power equipment fault diagnosis, aiming to construct an efficient and intelligent diagnostic model to improve the accuracy and efficiency of fault identification. A hybrid deep learning fr amework is proposed by integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM), leveraging CNN's capability in spatial feature extraction and LSTM's advantage in capturing temporal sequence information. This fr amework is capable of processing multi-source heterogeneous data while effectively mining potential patterns in the operational status of power equipment. Experimental results demonstrate that the proposed method exhibits superior diagnostic performance across various typical fault scenarios of power equipment, with an accuracy improvement of over 15% compared to traditional methods. Furthermore, a transfer learning-based optimization strategy is introduced, which significantly reduces the dependency on large-scale labeled data and enhances the model's generalization ability. In summary, this research not only provides a new technical approach for power equipment fault diagnosis but also broadens the application prospects of deep learning in industrial domains.
Keywords：Fault Diagnosis Of Power Equipment; Deep Learning; Convolutional Neural Network
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法概述 2
二、深度学习基础理论与技术 2
(一) 深度学习基本原理 2
(二) 常用深度学习模型介绍 3
(三) 深度学习在故障诊断中的应用 3
三、电力设备故障数据处理与特征提取 4
(一) 故障数据采集与预处理 4
(二) 数据特征提取方法研究 4
(三) 特征选择与优化策略 5
四、基于深度学习的故障诊断模型构建 6
(一) 模型架构设计与选择 6
(二) 模型训练与参数调优 6
(三) 模型性能评估与验证 7
结 论 9
参考文献 10
随着电力系统规模的不断扩大和复杂性的持续提升，电力设备故障诊断技术已成为保障电网安全稳定运行的关键环节之一然而传统故障诊断方法在面对高维、非线性数据时存在局限性，难以满足现代电力系统对精准性和实时性的要求基于此，本文聚焦于深度学习技术在电力设备故障诊断中的应用，旨在通过构建高效智能的诊断模型，提升故障识别的准确率与效率研究中采用卷积神经网络（CNN）与长短时记忆网络（LSTM）相结合的方法，充分利用CNN对空间特征的提取能力和LSTM对时间序列信息的捕捉优势，设计了一种混合深度学习框架该框架能够同时处理多源异构数据，并有效挖掘电力设备运行状态中的潜在模式实验结果表明，所提方法在多种典型电力设备故障场景下均展现出优异的诊断性能，其准确率较传统方法提升了15%以上此外，本文还提出一种基于迁移学习的优化策略，显著降低了对大规模标注数据的依赖，增强了模型的泛化能力综上所述，本研究不仅为电力设备故障诊断提供了新的技术路径，也为深度学习在工业领域的应用拓展了思路
关键词：电力设备故障诊断；深度学习；卷积神经网络
Abstract
With the continuous expansion of power system scale and increasing complexity, fault diagnosis technology for power equipment has become one of the key components to ensure the safe and stable operation of the power grid. However, traditional fault diagnosis methods exhibit limitations when dealing with high-dimensional and nonlinear data, making it difficult to meet the modern power system's requirements for precision and real-time performance. In response to this challenge, this study focuses on the application of deep learning techniques in power equipment fault diagnosis, aiming to construct an efficient and intelligent diagnostic model to improve the accuracy and efficiency of fault identification. A hybrid deep learning fr amework is proposed by integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM), leveraging CNN's capability in spatial feature extraction and LSTM's advantage in capturing temporal sequence information. This fr amework is capable of processing multi-source heterogeneous data while effectively mining potential patterns in the operational status of power equipment. Experimental results demonstrate that the proposed method exhibits superior diagnostic performance across various typical fault scenarios of power equipment, with an accuracy improvement of over 15% compared to traditional methods. Furthermore, a transfer learning-based optimization strategy is introduced, which significantly reduces the dependency on large-scale labeled data and enhances the model's generalization ability. In summary, this research not only provides a new technical approach for power equipment fault diagnosis but also broadens the application prospects of deep learning in industrial domains.
Keywords：Fault Diagnosis Of Power Equipment; Deep Learning; Convolutional Neural Network
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法概述 2
二、深度学习基础理论与技术 2
(一) 深度学习基本原理 2
(二) 常用深度学习模型介绍 3
(三) 深度学习在故障诊断中的应用 3
三、电力设备故障数据处理与特征提取 4
(一) 故障数据采集与预处理 4
(二) 数据特征提取方法研究 4
(三) 特征选择与优化策略 5
四、基于深度学习的故障诊断模型构建 6
(一) 模型架构设计与选择 6
(二) 模型训练与参数调优 6
(三) 模型性能评估与验证 7
结 论 9
参考文献 10
