摘 要
智能电网作为现代电力系统的重要组成部分，其安全稳定运行对社会经济发展具有重要意义。然而，随着电网规模的不断扩大和复杂性的增加，传统故障定位与保护方法在精度和实时性方面逐渐难以满足需求。为此，本研究基于深度学习技术提出了一种新型智能电网故障定位与保护方法，旨在提升故障诊断的准确性及响应速度。研究首先构建了包含多种故障类型和运行工况的仿真数据集，并采用卷积神经网络（CNN）对电流、电压信号进行特征提取与模式识别，从而实现对故障位置的精准定位。同时，引入长短期记忆网络（LSTM）以捕捉时序特性，进一步增强模型对动态故障过程的适应能力。实验结果表明，所提方法在不同噪声水平和复杂故障场景下均表现出较高的定位精度和鲁棒性，相较于传统方法平均误差降低了约30%。
关键词：智能电网 深度学习 故障定位
Abstract
Smart grids, as a crucial component of modern power systems, play a vital role in the safe and stable operation, which is essential for socioeconomic development. However, with the continuous expansion and increasing complexity of grid scales, traditional fault location and protection methods are gradually failing to meet the demands in terms of accuracy and real-time performance. To address this, this study proposes a novel smart grid fault location and protection method based on deep learning technology, aiming to enhance the accuracy and response speed of fault diagnosis. The research first constructs a simulation dataset that includes various fault types and operating conditions, using Convolutional Neural Networks (CNNs) to extract features and recognize patterns from current and voltage signals, thereby achieving precise fault location. Additionally, Long Short-Term Memory (LSTM) networks are introduced to capture temporal characteristics, further enhancing the model's adaptability to dynamic fault processes. Experimental results show that the proposed method demonstrates high positioning accuracy and robustness across different noise levels and complex fault scenarios, with an average error reduction of about 30% compared to traditional methods.
Keyword:Smart Grid Deep Learning Fault Location
目 录
1绪论 1
1.1智能电网故障定位的研究背景 1
1.2基于深度学习的保护技术意义 1
1.3国内外研究现状与挑战 1
1.4本文研究方法概述 2
2深度学习在智能电网中的应用基础 2
2.1深度学习算法的基本原理 2
2.2智能电网数据特征分析 3
2.3故障信号处理的关键技术 3
2.4数据驱动模型的设计思路 4
2.5应用基础总结与展望 4
3深度学习驱动的故障定位方法研究 4
3.1故障定位的核心问题分析 5
3.2基于卷积神经网络的定位模型 5
3.3长短时记忆网络的应用探索 5
3.4模型优化与性能评估 6
3.5方法的实际适用性讨论 6
4智能电网保护策略的深度学习实现 7
4.1保护策略的需求分析 7
4.2深度学习在继电保护中的应用 7
4.3实时保护系统的架构设计 8
4.4系统可靠性与安全性评估 8
4.5未来发展方向与改进路径 9
结论 9
参考文献 11
致谢 12
