摘 要
随着电力系统的快速发展，电气设备的安全运行成为保障电网稳定的关键因素之一，而温度场分布异常往往是电气设备故障的重要前兆。为此，本文旨在研究电气设备的温度场分布监测与故障预警技术，以实现对潜在故障的早期发现和有效预防。研究通过结合红外热成像技术和多传感器数据融合方法，构建了一套高精度的温度场监测系统，并引入机器学习算法对采集的数据进行分析与建模，从而实现对设备健康状态的实时评估和故障预警。具体而言，该研究首先设计了基于分布式光纤测温和红外热成像的复合监测方案，能够全面获取电气设备的温度分布特征；其次，提出了一种改进的深度学习模型，用于识别温度场中的异常模式并预测可能的故障类型；最后，通过实验验证了所提方法在实际应用场景中的有效性。结果表明，该系统能够以较高的灵敏度和准确性检测出温度场的微小变化，并提前预警潜在故障，为运维人员提供决策支持。本文的主要创新点在于将多源异构数据融合与智能诊断算法相结合，显著提升了监测系统的可靠性和智能化水平，为电气设备的状态检修和健康管理提供了新的技术手段。研究成果对于提高电力系统的安全性和经济性具有重要意义。关键词：电气设备；温度场监测；故障预警；多传感器数据融合；深度学习模型；红外热成像；分布式光纤测温
Abstract
With the rapid development of power systems, the safe operation of electrical equipment has become one of the key factors in ensuring grid stability, while abnormal temperature field distribution is often an important precursor to equipment failure. To address this issue, this study focuses on the monitoring of temperature field distribution and fault early warning technology for electrical equipment, aiming to achieve early detection and effective prevention of potential faults. By integrating infrared thermography with multi-sensor data fusion methods, a high-precision temperature field monitoring system was developed. Machine learning algorithms were introduced to analyze and model the collected data, enabling real-time assessment of equipment health status and fault prediction. Specifically, a hybrid monitoring scheme based on distributed optical fiber temperature measurement and infrared thermography was designed to comprehensively capture the temperature distribution characteristics of electrical equipment. Furthermore, an improved deep learning model was proposed to identify abnormal patterns in the temperature field and predict possible fault types. The effectiveness of the proposed method in practical applications was validated through experiments. The results demonstrate that the system can detect subtle changes in the temperature field with high sensitivity and accuracy, providing advance warnings of potential faults and supporting decision-making for maintenance personnel. The main innovation of this study lies in the combination of multi-source heterogeneous data fusion and intelligent diagnostic algorithms, which significantly enhances the reliability and intelligence level of the monitoring system, offering new technical means for condition-based maintenance and health management of electrical equipment. The research findings are of great significance for improving the safety and economic efficiency of power systems..
Key Words：Electrical Equipment；Temperature Field Monitoring；Fault Early Warning；Multi-Sensor Data Fusion；Deep Learning Model；Infrared Thermography；Distributed Optical Fiber Temperature Measurement
目 录
摘 要 I
Abstract II
第1章 绪论 1
1.1 电气设备温度场监测的研究背景 1
1.2 温度场分布与故障预警的意义分析 1
1.3 国内外研究现状与技术发展 2
1.4 本文研究方法与技术路线 2
第2章 温度场分布监测的关键技术 3
2.1 温度场分布的物理特性分析 3
2.2 监测技术的原理与实现方式 3
2.3 数据采集与处理方法研究 4
2.4 监测系统的误差分析与校正 4
2.5 关键技术的应用案例探讨 5
第3章 故障预警模型的构建与优化 6
3.1 故障预警的基本理论框架 6
3.2 基于温度场数据的特征提取方法 6
3.3 预警模型的算法设计与实现 7
3.4 模型性能评估与优化策略 7
3.5 实际应用中的问题与解决方案 8
第4章 系统集成与实际应用验证 9
4.1 监测与预警系统的整体架构设计 9
4.2 系统软硬件集成的技术实现 9
4.3 实验验证与数据分析方法 10
4.4 应用场景下的效果评估 10
4.5 系统改进方向与未来展望 11
结 论 12
参考文献 13
致 谢 14
