A Novel Substation Fault Diagnosis Approach Based on RS and ANN and ES
With the aid of rough set (RS) and artificial neural networks (ANN), expert system (ES) can extend its capability in knowledge representation and acquisition as well as parallel reasoning. However, ANN still cant completely replace ES due to its inherent flaws such as learning difficulty and interpreting disability, etc. Hence, in the paper we would incorporate ANN with ES to overcome each deficiency and exert each excellence. In addition, rough set is applied to serve for pretreatment unit of ANN so as to simplify networks structure and improve learning quality. Thus, on the one hand, the problems such as inference complexity and time lengthiness of conventional ES are overcome. On the other hand, the flaws such as the incompleteness or error of ANN input data are also resolved well. In the end, a simulation trial in substation fault diagnosis shows the availability of the method.
Hongsheng Su Feng Zhao
School of Information and Electrical Engineering Lanzhou Jiaotong University Lanzhou, Gansu, China
国际会议
2006 International Conference on Communications,Circuits and Systems(第四届国际通信、电路与系统学术会议)
广西桂林
英文
2124-2127
2006-06-25（万方平台首次上网日期，不代表论文的发表时间）