摘 要
在现代电力系统中，电力变压器的稳定运行是保障电网安全和高效的关键。考虑到其故障可能带来的严重后果，本文全面综述了电力变压器故障类型、特征、影响因素以及在线监测与故障诊断技术的最新进展。首先，文章详细阐述了电力变压器常见的故障类型，包括短路、放电、绝缘、绕组和机械故障，并深入分析了导致这些故障的多方面因素，如设计缺陷、制造质量、外部环境、操作维护不当及负载过载等。接着，文章介绍了变压器在线监测技术，重点讨论了电磁参数、温度湿度以及油中溶解气体的监测方法，这些方法为及时发现变压器潜在问题提供了重要手段。随后，文章详细探讨了变压器故障诊断方法，从传统方法如视觉检查、声音分析、温度监测、油液分析和电气测试，到现代人工智能诊断技术，如机器学习、深度学习、数据挖掘、专家系统和模糊逻辑等，均进行了系统阐述，并对比了不同诊断方法的优缺点及适用场景。最后，文章展望了电力变压器在线监测与故障诊断技术的未来发展趋势，强调了传感器技术的微型化与智能化、实时数据分析与云计算的深度融合、预测性维护与故障预警系统的发展，以及多物理场耦合建模与仿真技术在提升诊断精度和效率方面的潜力。本文的研究成果对于保障电力变压器安全稳定运行、提高电网可靠性和经济效益具有重要意义。
关键词：电力变压器 在线监测 故障诊断 人工智能
Abstract
In modern power systems, the stable operation of power transformers is the key to ensuring the safety and efficiency of the power grid. Considering the serious consequences that its failure may bring, this article comprehensively reviews the types, characteristics, influencing factors, and latest advances in online monitoring and fault diagnosis technology of power transformer faults. Firstly, the article elaborates on the common types of faults in power transformers, including short circuit, discharge, insulation, winding, and mechanical faults, and deeply analyzes the various factors that cause these faults, such as design defects, manufacturing quality, external environment, improper operation and maintenance, and load overload. Next, the article introduces the online monitoring technology for transformers, with a focus on the monitoring methods for electromagnetic parameters, temperature and humidity, and dissolved gases in oil. These methods provide important means for timely detection of potential problems in transformers. Subsequently, the article discussed in detail the diagnostic methods for transformer faults, ranging from traditional methods such as visual inspection, sound analysis, temperature monitoring, oil analysis, and electrical testing, to modern artificial intelligence diagnostic technologies such as machine learning, deep learning, data mining, expert systems, and fuzzy logic. The advantages, disadvantages, and applicable scenarios of different diagnostic methods were systematically explained and compared. Finally, the article looks forward to the future development trends of online monitoring and fault diagnosis technology for power transformers, emphasizing the miniaturization and intelligence of sensor technology, the deep integration of real-time data analysis and cloud computing, the development of predictive maintenance and fault warning systems, and the potential of multi physics field coupling modeling and simulation technology in improving diagnostic accuracy and efficiency. The research results of this article are of great significance for ensuring the safe and stable operation of power transformers, improving the reliability and economic benefits of the power grid.
key words:Power transformer; Online monitoring; Fault diagnosis; artificial intelligence
目 录
摘 要 I
Abstract II
第1章 绪论 1
1.1 研究背景与意义 1
1.2 国内外研究现状 1
1.3 研究目的及方法 1
第2章 电力变压器故障类型与特征 2
2.1 常见故障类型 2
2.1.1 短路故障 2
2.1.2 放电故障 2
2.1.3 绝缘故障 3
2.1.4 绕组故障 3
2.1.5 机械故障 4
2.2 故障影响因素 4
2.2.1 设计缺陷 4
2.2.2 制造质量问题 4
2.2.3 外部环境因素 5
2.2.4 操作和维护不当 5
2.2.5 负载和过载影响 5
第3章 变压器在线监测技术 7
3.1 电磁参数监测 7
3.2 温度与湿度监测 7
3.3 油中溶解气体监测 7
第4章 变压器故障诊断方法 9
4.1 传统诊断方法 9
4.2 人工智能诊断方法 10
4.3 诊断方法的比较与选择 11
第5章 电力变压器在线监测与故障诊断技术未来发展趋势 13
5.1 传感器技术的微型化与智能化 13
5.2 实时数据分析与云计算集成 13
5.3 预测性维护与故障预警系统的发展 13
5.4 多物理场耦合建模与仿真技术 14
第6章 结论 15
参考文献 16
致 谢 17
