基于粗糙集与神经网络的断路器状态监测  

State-Inspect of High-Voltage Circuit Breaker Based on Rough Set and Neural Network

作　　者：李俊[1] 吴江[1] 

机构地区：[1]西北大学信息科学与技术学院

出　　处：《微计算机信息》2007年第10期135-137,共3页Control & Automation

基　　金：陕西省科技攻关项目(2003K05-G32)

摘　　要：断路器的故障诊断对于事故后快速恢复具有重要意义,然而全面、准确的故障诊断仍是个难题。本文将电寿命和机械状态综合考虑,从而实现了对断路器整体健康状态的评估。其中机械状态的检测,提出了基于神经网络(Neural Network)并结合粗糙集理论(Rough Set Theory)的方法。实验数据表明,该方法提高了诊断的全面性、准确性和预测精度。The diagnosing of high-voltage circuit breaker is very important to recover the accident quickly, however it is still a diffi-culty to check out the faults comprehensively and exactly. This article concerns the two facts about electrical endurance and mechani-cal fault of high-voltage circuit breaker, so evaluating the whole healthy of high-voltage circuit breaker comes true. As for the diag-nosing the Mechanical fault of high-voltage circuit breaker, introduces a method of using the Rough Set Theory and Neural Network. The experimental data indicates the way which combines the Neural Network and Rough Set Theory to forecast the whole healthy life of high-voltage circuit breaker, which enhances the entireness, the veracity and the accuracy of the result.

关 键 词：人工神经网络 粗糙集理论 电寿命 机械故障 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]