摘 要
随着智能电网的快速发展，电能质量监测技术在保障电力系统稳定运行和提升用户用电体验方面的重要性日益凸显。本研究旨在针对智能电网中复杂多变的电能质量问题，提出一种基于先进信号处理与人工智能算法的综合监测方案。研究首先分析了传统电能质量监测方法在数据采集精度、实时性和智能化水平上的不足，进而设计了一种融合小波变换和深度学习模型的监测框架，用于准确识别和分类多种电能质量扰动。通过构建大规模实际电网数据集并进行实验验证，结果表明该方法在检测速度和准确性上较现有技术均有显著提升，特别是在低信噪比环境下的表现更为突出。此外，本研究还开发了一套适用于分布式部署的监测系统，能够实现对电网全域范围内的电能质量状态进行实时监控与评估。最终结论显示，所提出的监测技术不仅有效提升了智能电网的电能质量管理能力，还为未来电力系统的智能化升级提供了重要技术支持，其创新点在于将人工智能技术与传统信号处理方法有机结合，从而实现了更高水平的自动化和精确性。
关键词：智能电网，电能质量监测，小波变换，深度学习，分布式部署
Research on Power Quality Monitoring Technology in Smart Grids
Abstract
With the rapid development of smart grids, the importance of power quality monitoring technology in ensuring the stable operation of power systems and enhancing user experience has become increasingly prominent. This study aims to address the complex and dynamic power quality issues in smart grids by proposing an integrated monitoring solution based on advanced signal processing and artificial intelligence algorithms. It first analyzes the inadequacies of traditional power quality monitoring methods in terms of data acquisition accuracy, real-time performance, and level of intelligence, followed by the design of a monitoring fr amework that integrates wavelet transform with deep learning models for precise identification and classification of various power quality disturbances. By constructing a large-scale real-world power grid dataset and conducting experimental validations, the results demonstrate significant improvements in detection speed and accuracy compared to existing technologies, particularly under low signal-to-noise ratio conditions. Additionally, this research develops a monitoring system suitable for distributed deployment, enabling real-time monitoring and evaluation of power quality across the entire power grid. The final conclusion indicates that the proposed monitoring technology not only effectively enhances the power quality management capability of smart grids but also provides crucial technical support for the intelligent upgrading of future power systems. Its innovation lies in the organic combination of artificial intelligence techniques with traditional signal processing methods, thereby achieving a higher level of automation and precision.
Key Words：Smart Grid, Power Quality Monitoring, Wavelet Transform, Deep Learning, Distributed Deployment
目 录
一、绪论 1
（一）智能电网与电能质量监测背景 1
（二）电能质量监测技术的研究现状 2
（三）本文研究方法与技术路线 2
二、智能电网中电能质量问题分析 3
（一）电能质量问题的分类与特征 3
（二）智能电网环境下的典型电能质量问题 5
（三）电能质量问题对系统运行的影响 6
三、电能质量监测关键技术研究 7
（一）数据采集与信号处理技术 7
（二）实时监测算法的设计与优化 8
（三）监测系统的硬件架构与实现 9
四、智能电网中电能质量监测系统设计 10
（一）系统功能需求分析 11
（二）基于大数据的监测平台构建 12
（三）监测系统的性能评估与验证 13
结论 14
参考文献 15
致 谢 17
随着智能电网的快速发展，电能质量监测技术在保障电力系统稳定运行和提升用户用电体验方面的重要性日益凸显。本研究旨在针对智能电网中复杂多变的电能质量问题，提出一种基于先进信号处理与人工智能算法的综合监测方案。研究首先分析了传统电能质量监测方法在数据采集精度、实时性和智能化水平上的不足，进而设计了一种融合小波变换和深度学习模型的监测框架，用于准确识别和分类多种电能质量扰动。通过构建大规模实际电网数据集并进行实验验证，结果表明该方法在检测速度和准确性上较现有技术均有显著提升，特别是在低信噪比环境下的表现更为突出。此外，本研究还开发了一套适用于分布式部署的监测系统，能够实现对电网全域范围内的电能质量状态进行实时监控与评估。最终结论显示，所提出的监测技术不仅有效提升了智能电网的电能质量管理能力，还为未来电力系统的智能化升级提供了重要技术支持，其创新点在于将人工智能技术与传统信号处理方法有机结合，从而实现了更高水平的自动化和精确性。
关键词：智能电网，电能质量监测，小波变换，深度学习，分布式部署
Research on Power Quality Monitoring Technology in Smart Grids
Abstract
With the rapid development of smart grids, the importance of power quality monitoring technology in ensuring the stable operation of power systems and enhancing user experience has become increasingly prominent. This study aims to address the complex and dynamic power quality issues in smart grids by proposing an integrated monitoring solution based on advanced signal processing and artificial intelligence algorithms. It first analyzes the inadequacies of traditional power quality monitoring methods in terms of data acquisition accuracy, real-time performance, and level of intelligence, followed by the design of a monitoring fr amework that integrates wavelet transform with deep learning models for precise identification and classification of various power quality disturbances. By constructing a large-scale real-world power grid dataset and conducting experimental validations, the results demonstrate significant improvements in detection speed and accuracy compared to existing technologies, particularly under low signal-to-noise ratio conditions. Additionally, this research develops a monitoring system suitable for distributed deployment, enabling real-time monitoring and evaluation of power quality across the entire power grid. The final conclusion indicates that the proposed monitoring technology not only effectively enhances the power quality management capability of smart grids but also provides crucial technical support for the intelligent upgrading of future power systems. Its innovation lies in the organic combination of artificial intelligence techniques with traditional signal processing methods, thereby achieving a higher level of automation and precision.
Key Words：Smart Grid, Power Quality Monitoring, Wavelet Transform, Deep Learning, Distributed Deployment
目 录
一、绪论 1
（一）智能电网与电能质量监测背景 1
（二）电能质量监测技术的研究现状 2
（三）本文研究方法与技术路线 2
二、智能电网中电能质量问题分析 3
（一）电能质量问题的分类与特征 3
（二）智能电网环境下的典型电能质量问题 5
（三）电能质量问题对系统运行的影响 6
三、电能质量监测关键技术研究 7
（一）数据采集与信号处理技术 7
（二）实时监测算法的设计与优化 8
（三）监测系统的硬件架构与实现 9
四、智能电网中电能质量监测系统设计 10
（一）系统功能需求分析 11
（二）基于大数据的监测平台构建 12
（三）监测系统的性能评估与验证 13
结论 14
参考文献 15
致 谢 17
