基于粗糙集-神经网络系统的电力电子电路故障诊断  

Fault diagnosis of power electronic circuits based on rough set-neural network system

作　　者：刘庆珍[1] 蔡金锭[1] 王少芳[1] 

机构地区：[1]福州大学电气系,福建福州350002

出　　处：《电力自动化设备》2004年第4期45-48,共4页Electric Power Automation Equipment

基　　金：福建省教育厅基金资助项目(JB01036);福州大学人才基金资助

摘　　要：基于粗糙集理论RST(RoughSetTheory)与BP神经网络系统,提出了电力电子电路故障诊断的方法:粗糙集-神经网络系统相结合的方法。叙述了粗糙集-神经网络系统诊断电力电子电路的过程。以三相可控整流电路为例,对故障信息中样本的故障征兆进行数据预处理,通过知识约简,形成诊断的确定性规则,实现故障分类;然后将粗糙集的分类结果与故障信息中的输出电压Ud采样值作为神经网络的输入,实现故障元的定位。仿真实例表明,该方法不仅准确可靠,而且提高了系统诊断的速度。Based on rough set theory and neural network,a fault diagnosis method for power electronic circuits is presented:rough set-neural network system. The diagnosing process of rough set-neural network system is introduced. Taking tree phase SCR(Silicon Controlled Rectifier) as example,the fault information is processed in advance and the unnecessary fault signs are simplified to form the correct diagnosis rules and to classify the faults. Then the sampled-data of Ud is input to the BP neural network together with the outcome of rough set to locate the fault part. The simulation example proves that the method improves the fault diagnosis speed greatly with high correctness and reliability. This project is supported by the Education Department Fund of Fujian Province(JB01036) and the Ta-lent Fund of Fuzhou University.

关 键 词：粗糙集 神经网络 电力电子电路 故障诊断 

分 类 号：TM711[电气工程—电力系统及自动化] TP183[自动化与计算机技术—控制理论与控制工程]