摘 要
在电力系统中，高压局部放电信号的准确检测对于预防设备故障、确保系统稳定运行至关重要。然而，实际检测过程中，放电信号常受到各种噪声的干扰，严重影响了信号的准确识别与分析。因此，本研究旨在开发一种有效的去噪技术及模式识别方法，以提升高压局部放电信号的检测精度。通过对比实验，验证了该方法在去除噪声、保留信号特征方面的优越性。在模式识别方面，我们提出了一种基于深度学习的识别模型，该模型能够自动学习和提取放电信号中的特征，进而实现高精度的分类识别。本研究的主要贡献在于提出了一种新颖的去噪与模式识别方法，该方法结合了现代信号处理技术与深度学习算法，有效提升了高压局部放电信号的检测与识别精度。这一创新性的研究为电力系统的智能化监测与诊断奠定了坚实基础，具有重要的理论意义和实践价值。
关键词：高压局部放电信号 去噪技术 模式识别 小波变换
Abstract
This paper takes the design and implementation of campus second-hand goods trading system as the research ob ject, aiming to solve the problems existing in the current campus second-hand goods trading, such as high express costs, many intermediaries, and imperfect credit system, and provide students with a convenient and efficient second-hand goods trading platform. The system adopts B/S architecture, combines content-based recommendation algorithm, collaborative filtering recommendation algorithm and hybrid recommendation technology to realize personalized product recommendation. The system is divided into foreground user module and background management module. The front desk module includes user registration, login, product browsing, details viewing, idle items management, personal information management, product collection, order management and personalized recommendation functions; The background module covers the functions of administrator login, user management, commodity classification management and commodity audit. Data storage and management are realized by MySQL database, and offline analysis and recommendation of recommendation algorithm are realized by Python language. The black box test method is adopted in the system test to verify the effectiveness and stability of each functional module. The research results show that the system can effectively meet the demand of campus second-hand transaction, improve the efficiency of resource utilization, enhance the students' environmental awareness, and has a good application prospect and promotion value.
Keyword:Campus second-hand transaction System design Recommendation algorithm B/S architecture
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高压局部放电信号特性分析 1
2.1局部放电信号的产生机制 2
2.2放电信号的传播与衰减特性 2
2.3噪声来源及其对放电信号的影响 2
2.4放电信号的特征提取方法 3
3高压局部放电信号去噪技术研究 3
3.1传统去噪方法及其局限性 3
3.2基于小波变换的去噪方法 4
3.3基于经验模态分解的去噪技术 4
3.4新型去噪算法的探索与实践 5
3.5去噪效果的评价指标与方法 5
4高压局部放电信号模式识别研究 6
4.1模式识别的基本原理与方法 6
4.2基于统计学习的模式识别技术 6
4.3深度学习在放电信号模式识别中的应用 7
4.4多分类器融合策略的研究 7
4.5模式识别性能的评估与优化 8
5结论 8
参考文献 10
致谢 11
