摘 要
电力设备作为现代电力系统的核心组成部分，其运行状态直接关系到整个电力系统的安全稳定。随着智能电网建设的推进，在线监测技术成为保障电力设备可靠性的关键技术之一。本研究聚焦于电力设备在线监测与数据处理技术，旨在通过构建高效的数据采集、传输及分析体系，实现对电力设备运行状态的实时精准评估。采用物联网、大数据分析、机器学习等多学科交叉方法，建立了一套完整的在线监测系统架构，实现了从数据采集到故障预警的全流程自动化处理。创新性地提出了基于深度学习的故障诊断模型，有效提高了故障识别准确率。通过对实际电力设备运行数据的测试验证，该系统能够及时发现潜在故障隐患，显著提升了电力设备运维效率和可靠性，为电力系统的智能化发展提供了有力支持。研究成果不仅为电力设备状态检修提供了理论依据和技术手段，也为相关领域的研究开拓了新的思路。
关键词：电力设备在线监测；故障诊断模型；数据处理技术
Abstract
As the core component of modern power system, the operation state of electric power equipment is directly related to the safety and stability of the whole power system. With the advancement of smart grid construction, online monitoring technology has become one of the key technologies to ensure the reliability of power equipment. This research focuses on the online monitoring and data processing technology of power equipment, aiming to realize the real-time and accurate evaluation of the operation status of power equipment by building an efficient data collection, transmission and analysis system. Using interdisciplinary methods such as the Internet of Things, big data analysis and machine learning, a complete set of online monitoring system architecture has been established, realizing the automatic processing of the whole process from data collection to fault warning. A fault diagnosis model based on deep learning is innovatively proposed to effectively improve the fault identification accuracy. Through the test and verification of the operation data of the actual power equipment, the system can find the potential faults in time, significantly improve the efficiency and reliability of the operation and maintenance of the power equipment, and provide strong support for the intelligent development of the power system. The research results not only provide the theoretical basis and technical means for the state maintenance of power equipment, but also open up new ideas for the research in related fields.
Keywords: Online monitoring of electric power equipment; fault diagnosis model; data processing technology
目 录
1 引言 1
2 电力设备在线监测技术基础 1
2.1 在线监测技术概述 1
2.2 关键监测参数分析 1
2.3 监测系统架构设计 2
3 数据采集与预处理方法 2
3.1 数据采集技术选择 2
3.2 数据清洗与校验 3
3.3 数据压缩与存储 3
4 数据分析与故障诊断 4
4.1 特征提取与模式识别 4
4.2 故障诊断模型构建 4
4.3 预警机制与评估 5
5 系统集成与应用实践 5
5.1 系统集成方案设计 5
5.2 实时监控平台开发 6
5.3 应用案例分析研究 6
6 结论 7
致 谢 8
参考文献 9
电力设备作为现代电力系统的核心组成部分，其运行状态直接关系到整个电力系统的安全稳定。随着智能电网建设的推进，在线监测技术成为保障电力设备可靠性的关键技术之一。本研究聚焦于电力设备在线监测与数据处理技术，旨在通过构建高效的数据采集、传输及分析体系，实现对电力设备运行状态的实时精准评估。采用物联网、大数据分析、机器学习等多学科交叉方法，建立了一套完整的在线监测系统架构，实现了从数据采集到故障预警的全流程自动化处理。创新性地提出了基于深度学习的故障诊断模型，有效提高了故障识别准确率。通过对实际电力设备运行数据的测试验证，该系统能够及时发现潜在故障隐患，显著提升了电力设备运维效率和可靠性，为电力系统的智能化发展提供了有力支持。研究成果不仅为电力设备状态检修提供了理论依据和技术手段，也为相关领域的研究开拓了新的思路。
关键词：电力设备在线监测；故障诊断模型；数据处理技术
Abstract
As the core component of modern power system, the operation state of electric power equipment is directly related to the safety and stability of the whole power system. With the advancement of smart grid construction, online monitoring technology has become one of the key technologies to ensure the reliability of power equipment. This research focuses on the online monitoring and data processing technology of power equipment, aiming to realize the real-time and accurate evaluation of the operation status of power equipment by building an efficient data collection, transmission and analysis system. Using interdisciplinary methods such as the Internet of Things, big data analysis and machine learning, a complete set of online monitoring system architecture has been established, realizing the automatic processing of the whole process from data collection to fault warning. A fault diagnosis model based on deep learning is innovatively proposed to effectively improve the fault identification accuracy. Through the test and verification of the operation data of the actual power equipment, the system can find the potential faults in time, significantly improve the efficiency and reliability of the operation and maintenance of the power equipment, and provide strong support for the intelligent development of the power system. The research results not only provide the theoretical basis and technical means for the state maintenance of power equipment, but also open up new ideas for the research in related fields.
Keywords: Online monitoring of electric power equipment; fault diagnosis model; data processing technology
目 录
1 引言 1
2 电力设备在线监测技术基础 1
2.1 在线监测技术概述 1
2.2 关键监测参数分析 1
2.3 监测系统架构设计 2
3 数据采集与预处理方法 2
3.1 数据采集技术选择 2
3.2 数据清洗与校验 3
3.3 数据压缩与存储 3
4 数据分析与故障诊断 4
4.1 特征提取与模式识别 4
4.2 故障诊断模型构建 4
4.3 预警机制与评估 5
5 系统集成与应用实践 5
5.1 系统集成方案设计 5
5.2 实时监控平台开发 6
5.3 应用案例分析研究 6
6 结论 7
致 谢 8
参考文献 9
