摘要
随着智能电网规模的不断扩大和复杂度的增加，传统故障诊断方法在面对海量数据和复杂工况时逐渐暴露出局限性。为此，本研究提出一种基于深度学习的智能电网故障诊断方法，旨在通过深度神经网络模型对电网运行状态进行实时监测与故障识别。该方法利用卷积神经网络（CNN）提取多源传感器数据中的特征信息，并结合长短期记忆网络（LSTM）实现对时序数据的有效建模，从而提高故障检测的准确性和时效性。实验结果表明，在多种典型故障场景下，所提方法能够以95%以上的准确率完成故障定位与分类，相比传统方法具有显著优势。特别是在处理非线性、强噪声环境下的故障信号时，该方法展现出更强的鲁棒性和适应性。此外，本研究还引入迁移学习机制，解决了小样本条件下模型训练不足的问题，进一步提升了诊断系统的泛化能力。通过对实际电网系统中不同类型故障的测试验证，证明了该方法不仅能够快速响应瞬态故障，还能有效识别隐性故障，为智能电网的安全稳定运行提供了可靠的技术保障。
关键词：智能电网故障诊断；深度学习；卷积神经网络
Abstract
As the scale and complexity of smart grids continue to expand, traditional fault diagnosis methods have increasingly revealed limitations when dealing with massive data volumes and complex operating conditions. To address these challenges, this study proposes a deep learning-based fault diagnosis method for smart grids, aiming to achieve real-time monitoring and fault identification through deep neural network models. This approach employs Convolutional Neural Networks (CNN) to extract feature information from multi-source sensor data and combines Long Short-Term Memory networks (LSTM) to effectively model time-series data, thereby enhancing the accuracy and timeliness of fault detection. Experimental results demonstrate that the proposed method can achieve fault localization and classification with an accuracy rate exceeding 95% across various typical fault scenarios, showing significant advantages over traditional methods. Notably, in handling fault signals under nonlinear and high-noise environments, this method exhibits superior robustness and adaptability. Additionally, this research introduces transfer learning mechanisms to resolve issues of insufficient model training under small sample conditions, further improving the generalization capability of the diagnostic system. Through testing and validation on different types of faults in actual smart grid systems, it is proven that this method not only responds rapidly to transient faults but also effectively identifies latent faults, providing reliable technical support for the safe and stable operation of smart grids.
Keywords：Intelligent Grid Fault Diagnosis; 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 1
(三) 本文研究方法 2
二、深度学习算法在智能电网中的应用 2
(一) 深度学习基础理论 2
(二) 常用深度学习模型 3
(三) 智能电网数据预处理 3
(四) 模型选择与优化策略 4
三、故障诊断特征提取与表示 4
(一) 电网故障特征分析 5
(二) 特征提取方法研究 5
(三) 特征降维技术应用 6
(四) 特征表示优化方案 6
四、基于深度学习的故障诊断模型构建 7
(一) 模型架构设计原则 7
(二) 关键参数设置方法 8
(三) 模型训练与验证过程 8
(四) 实验结果与性能评估 9
结 论 11
参考文献 12
随着智能电网规模的不断扩大和复杂度的增加，传统故障诊断方法在面对海量数据和复杂工况时逐渐暴露出局限性。为此，本研究提出一种基于深度学习的智能电网故障诊断方法，旨在通过深度神经网络模型对电网运行状态进行实时监测与故障识别。该方法利用卷积神经网络（CNN）提取多源传感器数据中的特征信息，并结合长短期记忆网络（LSTM）实现对时序数据的有效建模，从而提高故障检测的准确性和时效性。实验结果表明，在多种典型故障场景下，所提方法能够以95%以上的准确率完成故障定位与分类，相比传统方法具有显著优势。特别是在处理非线性、强噪声环境下的故障信号时，该方法展现出更强的鲁棒性和适应性。此外，本研究还引入迁移学习机制，解决了小样本条件下模型训练不足的问题，进一步提升了诊断系统的泛化能力。通过对实际电网系统中不同类型故障的测试验证，证明了该方法不仅能够快速响应瞬态故障，还能有效识别隐性故障，为智能电网的安全稳定运行提供了可靠的技术保障。
关键词：智能电网故障诊断；深度学习；卷积神经网络
Abstract
As the scale and complexity of smart grids continue to expand, traditional fault diagnosis methods have increasingly revealed limitations when dealing with massive data volumes and complex operating conditions. To address these challenges, this study proposes a deep learning-based fault diagnosis method for smart grids, aiming to achieve real-time monitoring and fault identification through deep neural network models. This approach employs Convolutional Neural Networks (CNN) to extract feature information from multi-source sensor data and combines Long Short-Term Memory networks (LSTM) to effectively model time-series data, thereby enhancing the accuracy and timeliness of fault detection. Experimental results demonstrate that the proposed method can achieve fault localization and classification with an accuracy rate exceeding 95% across various typical fault scenarios, showing significant advantages over traditional methods. Notably, in handling fault signals under nonlinear and high-noise environments, this method exhibits superior robustness and adaptability. Additionally, this research introduces transfer learning mechanisms to resolve issues of insufficient model training under small sample conditions, further improving the generalization capability of the diagnostic system. Through testing and validation on different types of faults in actual smart grid systems, it is proven that this method not only responds rapidly to transient faults but also effectively identifies latent faults, providing reliable technical support for the safe and stable operation of smart grids.
Keywords：Intelligent Grid Fault Diagnosis; 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 1
(三) 本文研究方法 2
二、深度学习算法在智能电网中的应用 2
(一) 深度学习基础理论 2
(二) 常用深度学习模型 3
(三) 智能电网数据预处理 3
(四) 模型选择与优化策略 4
三、故障诊断特征提取与表示 4
(一) 电网故障特征分析 5
(二) 特征提取方法研究 5
(三) 特征降维技术应用 6
(四) 特征表示优化方案 6
四、基于深度学习的故障诊断模型构建 7
(一) 模型架构设计原则 7
(二) 关键参数设置方法 8
(三) 模型训练与验证过程 8
(四) 实验结果与性能评估 9
结 论 11
参考文献 12
