摘要
本文聚焦于高压电器设备的绝缘特性分析、在线监测技术及故障诊断方法。首先，深入研究了高压电器设备的绝缘材料特性，分析了典型绝缘缺陷的形成机理，并探讨了绝缘老化的特征及其演化规律，为设备的绝缘性能评估和维护提供了理论基础。在在线监测技术方面，文章研究了局部放电在线监测方法，以实现对设备绝缘状态的实时监测；同时，探讨了介质损耗因数的在线测量技术，以及温度场分布的在线监测系统设计，为设备的状态监测和预警提供了技术支持。针对高压电器设备的故障诊断，本文提出了基于多源信息融合的故障诊断模型，以提高诊断的准确性和可靠性；并研究了智能算法在故障诊断中的应用，进一步提升了诊断的智能化水平。此外，还通过典型故障案例的分析与验证，验证了所提方法的有效性和实用性。本研究不仅为高压电器设备的绝缘性能评估、状态监测和故障诊断提供了全面的视角和方法，也为电力系统的安全稳定运行提供了重要的技术保障。
关键词：高压电器设备；绝缘监测；多源信息融合；深度学习；迁移学习
Abstract
This article focuses on the analysis of insulation characteristics, online monitoring technology, and fault diagnosis methods of high-voltage electrical equipment. Firstly, the insulation material characteristics of high-voltage electrical equipment were deeply studied, the formation mechanism of typical insulation defects was analyzed, and the characteristics and evolution laws of insulation aging were explored, providing a theoretical basis for the evaluation and maintenance of equipment insulation performance. In terms of online monitoring technology, the article studied the method of partial discharge online monitoring to achieve real-time monitoring of equipment insulation status; At the same time, the online measurement technology of dielectric loss factor and the design of online monitoring system for temperature field distribution were discussed, providing technical support for equipment status monitoring and early warning. This paper proposes a fault diagnosis model based on multi-source information fusion for high-voltage electrical equipment to improve the accuracy and reliability of diagnosis; And the application of intelligent algorithms in fault diagnosis was studied, further improving the level of intelligent diagnosis. In addition, the effectiveness and practicality of the proposed method were verified through the analysis and validation of typical fault cases. This study not only provides a comprehensive perspective and method for the insulation performance evaluation, status monitoring, and fault diagnosis of high-voltage electrical equipment, but also provides important technical support for the safe and stable operation of power systems.
Keywords：High-Voltage Electrical Equipment; Insulation Monitoring; Multi-Source Information Fusion; Deep Learning; Transfer Learning
目 录
摘要 I
Abstract II
一、绪论 1
（一）研究背景 1
（二）研究意义 1
（三）研究现状 1
二、高压电器设备绝缘特性分析 3
（一）高压电器设备绝缘材料特性研究 3
（二）典型绝缘缺陷的形成机理分析 3
（三）绝缘老化特征及其演化规律 4
三、高压电器设备在线监测技术 6
（一）局部放电在线监测方法研究 6
（二）介质损耗因数在线测量技术 6
（三）温度场分布在线监测系统设计 7
四、高压电器设备故障诊断方法 9
（一）基于多源信息融合的故障诊断模型 9
（二）智能算法在故障诊断中的应用研究 9
（三）典型故障案例分析与验证 10
结 论 11
本文聚焦于高压电器设备的绝缘特性分析、在线监测技术及故障诊断方法。首先，深入研究了高压电器设备的绝缘材料特性，分析了典型绝缘缺陷的形成机理，并探讨了绝缘老化的特征及其演化规律，为设备的绝缘性能评估和维护提供了理论基础。在在线监测技术方面，文章研究了局部放电在线监测方法，以实现对设备绝缘状态的实时监测；同时，探讨了介质损耗因数的在线测量技术，以及温度场分布的在线监测系统设计，为设备的状态监测和预警提供了技术支持。针对高压电器设备的故障诊断，本文提出了基于多源信息融合的故障诊断模型，以提高诊断的准确性和可靠性；并研究了智能算法在故障诊断中的应用，进一步提升了诊断的智能化水平。此外，还通过典型故障案例的分析与验证，验证了所提方法的有效性和实用性。本研究不仅为高压电器设备的绝缘性能评估、状态监测和故障诊断提供了全面的视角和方法，也为电力系统的安全稳定运行提供了重要的技术保障。
关键词：高压电器设备；绝缘监测；多源信息融合；深度学习；迁移学习
Abstract
This article focuses on the analysis of insulation characteristics, online monitoring technology, and fault diagnosis methods of high-voltage electrical equipment. Firstly, the insulation material characteristics of high-voltage electrical equipment were deeply studied, the formation mechanism of typical insulation defects was analyzed, and the characteristics and evolution laws of insulation aging were explored, providing a theoretical basis for the evaluation and maintenance of equipment insulation performance. In terms of online monitoring technology, the article studied the method of partial discharge online monitoring to achieve real-time monitoring of equipment insulation status; At the same time, the online measurement technology of dielectric loss factor and the design of online monitoring system for temperature field distribution were discussed, providing technical support for equipment status monitoring and early warning. This paper proposes a fault diagnosis model based on multi-source information fusion for high-voltage electrical equipment to improve the accuracy and reliability of diagnosis; And the application of intelligent algorithms in fault diagnosis was studied, further improving the level of intelligent diagnosis. In addition, the effectiveness and practicality of the proposed method were verified through the analysis and validation of typical fault cases. This study not only provides a comprehensive perspective and method for the insulation performance evaluation, status monitoring, and fault diagnosis of high-voltage electrical equipment, but also provides important technical support for the safe and stable operation of power systems.
Keywords：High-Voltage Electrical Equipment; Insulation Monitoring; Multi-Source Information Fusion; Deep Learning; Transfer Learning
目 录
摘要 I
Abstract II
一、绪论 1
（一）研究背景 1
（二）研究意义 1
（三）研究现状 1
二、高压电器设备绝缘特性分析 3
（一）高压电器设备绝缘材料特性研究 3
（二）典型绝缘缺陷的形成机理分析 3
（三）绝缘老化特征及其演化规律 4
三、高压电器设备在线监测技术 6
（一）局部放电在线监测方法研究 6
（二）介质损耗因数在线测量技术 6
（三）温度场分布在线监测系统设计 7
四、高压电器设备故障诊断方法 9
（一）基于多源信息融合的故障诊断模型 9
（二）智能算法在故障诊断中的应用研究 9
（三）典型故障案例分析与验证 10
结 论 11
参考文献 12
