摘要
为保证水电机组运行的可靠性,通常采用基于振动频率分析的故障诊断技术。但是水电机组故障类型间存在重叠的频率特征,仅凭频率分析不易确定故障类型。因此,文中采用信息融合技术,引入开机过程中的时间和空间特征信息,在特征层采用支持向量机作为信息融合手段,在决策层采用D-S证据理论进行信息融合。实验结果表明,信息融合增加了故障诊断的特征信息,提高了故障诊断系统的诊断能力。
Generally, fault diagnosis based on vibration frequency analysis is used for high running reliability of the hydropower generating unit （HGU）. However, it is difficult to diagnose a fault correctly with only the spectrum feature because there is the same spectrum feature between different faults. In this paper, spatiotemporal characteristics in starting up a HGU are used in fault diagnosis by information fusion. The support vector machine （SVM） is adopted as information fusion algorithm at the feature level and D-S evidence theory is proposed as information fusion tool at the decision level. The experiment result shows that feature information is increased and reliability of fault diagnosis is improved by information fusion.
出处
《电力系统自动化》
EI
CSCD
北大核心
2008年第13期76-80,共5页
Automation of Electric Power Systems
基金
国家自然科学基金重点项目(50539140)
国家自然科学基金资助项目(50579022)
科技部水利部公益性行业专项科研基金(200701008)~~
关键词
水电机组
故障诊断
信息融合
支持向量机
hydropower generating unit
fault diagnosis
information fusion
support vector machine
