摘 要
随着电力系统规模的不断扩大和复杂程度的日益提高，电气设备的安全稳定运行对整个电力系统的可靠性和经济性至关重要，而传统故障诊断技术在面对海量数据时存在诸多局限，基于大数据的电气设备故障诊断技术应运而生。本研究旨在利用大数据技术提升电气设备故障诊断的准确性与及时性，通过构建融合多种数据源的大数据平台，整合电气设备运行状态、环境因素等多维数据，采用数据挖掘算法如聚类分析、关联规则挖掘等方法深入挖掘数据潜在规律，建立故障特征库并训练故障诊断模型。结果表明该方法能够有效识别不同类型故障模式，在早期故障预警方面具有较高灵敏度，相较于传统方法误报率降低30%以上。本研究创新性地将大数据技术与电气设备故障诊断深度融合，为实现智能化故障诊断提供了新思路，不仅提高了故障诊断效率，还为电力系统的预防性维护提供了有力支持，有助于推动电力系统向智能化方向发展。
关键词：电气设备故障诊断 大数据技术 数据挖掘算法
Abstract
As the scale and complexity of power systems continue to expand, the safe and stable operation of electrical equipment has become critical for ensuring the reliability and economic efficiency of the entire power system. Traditional fault diagnosis techniques face numerous limitations when dealing with massive amounts of data, leading to the emergence of big data-based fault diagnosis technologies for electrical equipment. This study aims to enhance the accuracy and timeliness of fault diagnosis by leveraging big data technology. A big data platform integrating multiple data sources is constructed to consolidate multidimensional data including operational status of electrical equipment and environmental factors. Data mining algorithms such as clustering analysis and association rule mining are employed to uncover underlying patterns in the data, establishing a fault feature library and training fault diagnosis models. The results demonstrate that this approach can effectively identify different types of fault patterns and exhibits high sensitivity in early fault warning, reducing false alarm rates by more than 30% compared to traditional methods. Innovatively integrating big data technology with electrical equipment fault diagnosis, this research provides new insights into intelligent fault diagnosis, not only improving diagnostic efficiency but also offering strong support for preventive maintenance of power systems, thereby facilitating the development of power systems towards intelligence.
Keyword:Electrical Equipment Fault Diagnosis Big Data Technology Data Mining Algorithm
目 录
引言 1
1大数据与电气设备故障诊断概述 1
1.1电气设备故障诊断的重要性 1
1.2大数据技术的基本原理 2
1.3大数据在故障诊断中的应用现状 2
2数据采集与预处理技术 3
2.1传感器网络与数据获取 3
2.2数据清洗与质量控制 3
2.3特征提取与降维方法 4
3故障诊断模型与算法 5
3.1基于机器学习的诊断模型 5
3.2深度学习在故障诊断中的应用 5
3.3模型评估与优化策略 6
4实时监测与预测维护系统 6
4.1实时监测系统的架构设计 6
4.2预测性维护的关键技术 7
4.3系统实施与案例分析 7
结论 8
参考文献 10
致谢 11
随着电力系统规模的不断扩大和复杂程度的日益提高，电气设备的安全稳定运行对整个电力系统的可靠性和经济性至关重要，而传统故障诊断技术在面对海量数据时存在诸多局限，基于大数据的电气设备故障诊断技术应运而生。本研究旨在利用大数据技术提升电气设备故障诊断的准确性与及时性，通过构建融合多种数据源的大数据平台，整合电气设备运行状态、环境因素等多维数据，采用数据挖掘算法如聚类分析、关联规则挖掘等方法深入挖掘数据潜在规律，建立故障特征库并训练故障诊断模型。结果表明该方法能够有效识别不同类型故障模式，在早期故障预警方面具有较高灵敏度，相较于传统方法误报率降低30%以上。本研究创新性地将大数据技术与电气设备故障诊断深度融合，为实现智能化故障诊断提供了新思路，不仅提高了故障诊断效率，还为电力系统的预防性维护提供了有力支持，有助于推动电力系统向智能化方向发展。
关键词：电气设备故障诊断 大数据技术 数据挖掘算法
Abstract
As the scale and complexity of power systems continue to expand, the safe and stable operation of electrical equipment has become critical for ensuring the reliability and economic efficiency of the entire power system. Traditional fault diagnosis techniques face numerous limitations when dealing with massive amounts of data, leading to the emergence of big data-based fault diagnosis technologies for electrical equipment. This study aims to enhance the accuracy and timeliness of fault diagnosis by leveraging big data technology. A big data platform integrating multiple data sources is constructed to consolidate multidimensional data including operational status of electrical equipment and environmental factors. Data mining algorithms such as clustering analysis and association rule mining are employed to uncover underlying patterns in the data, establishing a fault feature library and training fault diagnosis models. The results demonstrate that this approach can effectively identify different types of fault patterns and exhibits high sensitivity in early fault warning, reducing false alarm rates by more than 30% compared to traditional methods. Innovatively integrating big data technology with electrical equipment fault diagnosis, this research provides new insights into intelligent fault diagnosis, not only improving diagnostic efficiency but also offering strong support for preventive maintenance of power systems, thereby facilitating the development of power systems towards intelligence.
Keyword:Electrical Equipment Fault Diagnosis Big Data Technology Data Mining Algorithm
目 录
引言 1
1大数据与电气设备故障诊断概述 1
1.1电气设备故障诊断的重要性 1
1.2大数据技术的基本原理 2
1.3大数据在故障诊断中的应用现状 2
2数据采集与预处理技术 3
2.1传感器网络与数据获取 3
2.2数据清洗与质量控制 3
2.3特征提取与降维方法 4
3故障诊断模型与算法 5
3.1基于机器学习的诊断模型 5
3.2深度学习在故障诊断中的应用 5
3.3模型评估与优化策略 6
4实时监测与预测维护系统 6
4.1实时监测系统的架构设计 6
4.2预测性维护的关键技术 7
4.3系统实施与案例分析 7
结论 8
参考文献 10
致谢 11
