基于解析冗余式的风机传动系统故障诊断  

Fault diagnosis of driven train system of wind turbine based on analytical redundancy relations

作　　者：王俊年[1] 钱瞻 陈茂林[1] 王磊[1] 

机构地区：[1]湖南科技大学信息与电气工程学院,湖南湘潭411201

出　　处：《电力系统保护与控制》2017年第3期41-47,共7页Power System Protection and Control

基　　金：国家自然科学基金项目(60974048);湖南省高校创新平台开放基金项目(11K027);湖南科技大学研究生创新基金(S140022)~~

摘　　要：为了以系统有序的方法对风机的具体元件参数进行故障诊断,给出一种基于键合图模型的故障诊断方法。键合图模型是一种跨能域的元件级模型,因此能够定位到具体的故障元件。同时键合图模型能够清晰地表明各元件之间的关系,因此适用于推导解析冗余式。通过解析冗余式可以对系统进行故障检测和隔离。为了能够系统地得到尽量多的解析冗余式,提高故障的可隔离性,该方法首先建立风机的键合图模型,然后由键合图模型导出时间因果图,由时间因果图导出变量关系图,最后由变量关系图消除键合图中结点方程的未知变量得到解析冗余式。实验结果验证了采用该方法推导的解析冗余式能够用于风机参数故障的诊断。A diagnosis method based on bond graph model is proposed for fault detection and isolation（FDI） of wind turbine components. Bond graph model can be used to locate fault components for it is a multidisciplinary component-level model, and to derive analytical redundancy relations（ARRs） for it can represent clearly the relationships between components. ARRs are often used to fault detection and isolation. In order to obtain as much ARRs as possible to improve the isolability, first of all, this paper builds the bond graph model of wind turbine, then uses the model to obtain temporal causal graph（TCG）, according to TCG to obtain relations graph of variables. Finally, it eliminates unknown variables of junction functions of bond graph by relations graph of variables. The simulation results show the ARRs of this method can be used to parameter fault diagnosis of the wind turbine system.

关 键 词：风机 键合图 解析冗余式 时间因果图 基于模型的故障诊断 

分 类 号：TM614[电气工程—电力系统及自动化]