Fault Diagnosis Method for Distribution Network Based on Rough Set and Adaptive Neutral Fuzzy Inference System
Zhang Lian · Journal of Chongqing Institute of Technology · 2009
In order to improve the accuracy and efficiency of fault diagnosis in distribution network,this paper puts forward an intelligent hybrid diagnosis system,which combines rough set theory with adaptive neutral fuzzy inference system theory,to make the best use of the rule reduction in rough set theory and the capabilities of fault-tolerance and learning in neutral network.Through the reasonable selection of input variables in the diagnostic system in such a way that more correlative parameters with fault diagnosis information are chosen as input variables to form the simplified rule sets,with ANFIS(Adaptive Neuro-Fuzzy Inference System) the most simplified rule sets are called to make modeling and parameter identification,then learning training is done by training samples.So it not only reduces the learning training time,but also improves the accuracy of diagnosis.Compared with the results of fault diagnosis for a certain distribution network in conventional rough set theory,it shows that with this method it will compute faster,have better fault-tolerance and potential of on-line fault diagnosis.