摘 要
电力设备作为电力系统稳定运行的关键组成部分，其状态监测与故障诊断对保障电力系统的安全可靠至关重要。为此，本研究旨在研发一套先进的电力设备状态监测与故障诊断系统。该系统基于大数据分析、人工智能算法及物联网技术，整合了多种传感器采集的数据信息，通过构建深度学习模型实现对电力设备运行状态的实时监测与精准故障诊断。研究过程中，采用数据驱动的方法，从海量历史数据中挖掘特征模式，并结合专家知识库进行优化。实验结果表明，该系统能够有效识别电力设备早期潜在故障，准确率达到95%以上，且具有良好的泛化能力。与传统方法相比，本系统不仅提高了故障诊断效率和准确性，还实现了对设备全生命周期健康管理的支持，为电力设备运维提供了科学依据和技术支撑，显著提升了电力系统的智能化水平和安全保障能力。
关键词：电力设备状态监测；故障诊断系统；大数据分析
Abstract
As a key part of the stable operation of the power system, the state monitoring and fault diagnosis of the power equipment are very important to ensure the safety and reliability of the power system. To this end, this study aims to develop an advanced power equipment status monitoring and fault diagnosis system. Based on big data analysis, artificial intelligence algorithm and Internet of Things technology, the system integrates the data information collected by a variety of sensors, and realizes the real-time monitoring and accurate fault diagnosis of the running state of the power equipment through the construction of a deep learning model. During the research process, a data-driven method is adopted to mine feature patterns from massive historical data and optimize them combined with the expert knowledge base. The experimental results show that the system can effectively identify the early potential faults of power equipment, with an accuracy of more than 95% and a good generalization ability. Compared with the traditional method, the system not only improves the efficiency and accuracy of fault diagnosis, but also realizes the support for the health management of the whole life cycle of the equipment, provides scientific basis and technical support for the operation and maintenance of power equipment, and significantly improves the intelligent level and safety guarantee capability of the power system.
Keywords: State monitoring of power equipment; fault diagnosis system; big data analysis
目 录
1 引言 1
2 电力设备状态监测技术基础 1
2.1 电力设备监测现状分析 1
2.2 关键监测技术综述 1
2.3 监测系统需求评估 2
3 故障诊断算法研究与设计 3
3.1 常见故障模式识别 3
3.2 智能诊断算法选择 3
3.3 算法性能优化方案 3
4 系统架构设计与实现 4
4.1 系统总体架构规划 4
4.2 数据采集模块设计 4
4.3 故障预警机制构建 5
5 系统测试与应用验证 5
5.1 测试环境搭建方法 5
5.2 实验结果分析评价 6
5.3 应用案例效果展示 6
6 结论 7
致 谢 8
参考文献 9
电力设备作为电力系统稳定运行的关键组成部分，其状态监测与故障诊断对保障电力系统的安全可靠至关重要。为此，本研究旨在研发一套先进的电力设备状态监测与故障诊断系统。该系统基于大数据分析、人工智能算法及物联网技术，整合了多种传感器采集的数据信息，通过构建深度学习模型实现对电力设备运行状态的实时监测与精准故障诊断。研究过程中，采用数据驱动的方法，从海量历史数据中挖掘特征模式，并结合专家知识库进行优化。实验结果表明，该系统能够有效识别电力设备早期潜在故障，准确率达到95%以上，且具有良好的泛化能力。与传统方法相比，本系统不仅提高了故障诊断效率和准确性，还实现了对设备全生命周期健康管理的支持，为电力设备运维提供了科学依据和技术支撑，显著提升了电力系统的智能化水平和安全保障能力。
关键词：电力设备状态监测；故障诊断系统；大数据分析
Abstract
As a key part of the stable operation of the power system, the state monitoring and fault diagnosis of the power equipment are very important to ensure the safety and reliability of the power system. To this end, this study aims to develop an advanced power equipment status monitoring and fault diagnosis system. Based on big data analysis, artificial intelligence algorithm and Internet of Things technology, the system integrates the data information collected by a variety of sensors, and realizes the real-time monitoring and accurate fault diagnosis of the running state of the power equipment through the construction of a deep learning model. During the research process, a data-driven method is adopted to mine feature patterns from massive historical data and optimize them combined with the expert knowledge base. The experimental results show that the system can effectively identify the early potential faults of power equipment, with an accuracy of more than 95% and a good generalization ability. Compared with the traditional method, the system not only improves the efficiency and accuracy of fault diagnosis, but also realizes the support for the health management of the whole life cycle of the equipment, provides scientific basis and technical support for the operation and maintenance of power equipment, and significantly improves the intelligent level and safety guarantee capability of the power system.
Keywords: State monitoring of power equipment; fault diagnosis system; big data analysis
目 录
1 引言 1
2 电力设备状态监测技术基础 1
2.1 电力设备监测现状分析 1
2.2 关键监测技术综述 1
2.3 监测系统需求评估 2
3 故障诊断算法研究与设计 3
3.1 常见故障模式识别 3
3.2 智能诊断算法选择 3
3.3 算法性能优化方案 3
4 系统架构设计与实现 4
4.1 系统总体架构规划 4
4.2 数据采集模块设计 4
4.3 故障预警机制构建 5
5 系统测试与应用验证 5
5.1 测试环境搭建方法 5
5.2 实验结果分析评价 6
5.3 应用案例效果展示 6
6 结论 7
致 谢 8
参考文献 9
