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环节。传统的检测方法往往依赖接触式测量或定期维护，存在效率低、成本高及难以实时监测的问题。为解决上述不足，本研究聚焦于红外热成像技术在电气设备检测中的应用，旨在通过非接触式手段实现对设备运行状态的高效评估。研究采用先进的红外热成像仪结合图像处理算法，对电气设备的温度分布进行精确测量与分析，并开发了一套基于热特征提取的故障诊断模型。通过对多种典型电气设备（如变压器、断路器和电缆接头）的实际测试，验证了该技术在早期故障识别中的有效性。结果表明，红外热成像技术能够准确捕捉设备异常热点，其检测精度可达±2℃，显著优于传统方法。此外，本研究创新性地引入了机器学习算法优化热图像分析流程，大幅提高了故障诊断的自动化水平和准确性。最终结论显示，红外热成像技术不仅能够有效降低人工巡检的工作量，还能提前发现潜在隐患，从而减少因设备故障引发的停电事故。本研究的主要贡献在于提出了一种集成化、智能化的检测方案，为电气设备的状态监测提供了新思路，同时为电力行业的数字化转型奠定了基础。关键词：红外热成像技术；电气设备检测；故障诊断模型；机器学习算法；状态监测
Abstract
With the rapid development of power systems, the safe operation of electrical equipment has become a critical factor in ensuring grid stability. Traditional inspection methods often rely on contact measurements or scheduled maintenance, which are associated with low efficiency, high costs, and difficulties in real-time monitoring. To address these limitations, this study focuses on the application of infrared thermography in the inspection of electrical equipment, aiming to achieve efficient assessment of operational conditions through non-contact means. Advanced infrared thermal imagers combined with image processing algorithms were employed to accurately measure and analyze the temperature distribution of electrical equipment, and a fault diagnosis model based on thermal feature extraction was developed. Practical tests conducted on various typical electrical devices, including transformers, circuit breakers, and cable joints, validated the effectiveness of this technology in early fault identification. The results indicate that infrared thermography can precisely capture abnormal hotspots in equipment with a detection accuracy of ±2℃, significantly outperforming conventional methods. Furthermore, this study innovatively incorporates machine learning algorithms to optimize the thermal image analysis process, substantially enhancing the automation and accuracy of fault diagnosis. The final conclusion demonstrates that infrared thermography not only effectively reduces the workload of manual inspections but also enables the early detection of potential hazards, thereby minimizing power outages caused by equipment failures. The primary contribution of this research lies in proposing an integrated and intelligent inspection solution, offering new insights into the condition monitoring of electrical equipment and laying a foundation for the digital transformation of the power industry..
Key Words：Infrared Thermography Technology；Electrical Equipment Inspection；Fault Diagnosis Model；Machine Learning Algorithm；Condition Monitoring
目 录
摘 要 I
Abstract II
第1章 绪论 1
1.1 电气设备红外热成像检测的背景与意义 1
1.2 国内外研究现状分析 1
1.3 本文研究方法与技术路线 2
第2章 红外热成像检测技术原理及应用基础 3
2.1 红外热成像技术的基本原理 3
2.2 电气设备热故障机理分析 3
2.3 热成像检测在电气设备中的适用性 4
2.4 数据采集与图像处理关键技术 4
第3章 红外热成像检测技术在电气设备中的具体应用 6
3.1 高压设备的热故障诊断方法 6
3.2 变压器红外检测技术与案例分析 6
3.3 开关柜热状态监测与评估 7
3.4 电缆接头温度异常检测技术 7
第4章 红外热成像检测技术优化与未来发展 9
4.1 提高检测精度的关键因素分析 9
4.2 智能化检测技术的发展趋势 9
4.3 数据分析与故障预测模型构建 10
4.4 技术推广中的挑战与对策 10
结 论 12
参考文献 13
致 谢 14
