Adaline and FIS Techniques for Fault Identification in HV Transmission Line  

作　　者：K. Elango G. Geetha K. Elango;G. Geetha(Department of EEE, Valliammai Engineering College, Chennai, India;Department of EEE, Annai Mira College of Engineering, Chennai, India)

机构地区：[1]Department of EEE, Valliammai Engineering College, Chennai, India [2]Department of EEE, Annai Mira College of Engineering, Chennai, India

出　　处：《Circuits and Systems》2016年第10期3183-3192,共10页电路与系统（英文）

摘　　要：This paper is to identify and classify the various types of shunt and line faults in transmission line. The faults may be an insulation failure, lightning or accidental faulty operation. In a transmission line protection important factor is identifying a fault because if any error occurs in finding fault may leads to abnormal operation of the protection system. So either a disturbance or steady state variation is called power quality variation. The proposed test system is modeled based on the neural network and fuzzy algorithm. The online symmetrical components are extracted by this above algorithm. The fuzzy is used to separate the oscillating components and average components. Here input for the fuzzy is trained by using neural network. It is based on current samples and very effective in fault classifier using rule base. This method is very much suitable for online implementation.This paper is to identify and classify the various types of shunt and line faults in transmission line. The faults may be an insulation failure, lightning or accidental faulty operation. In a transmission line protection important factor is identifying a fault because if any error occurs in finding fault may leads to abnormal operation of the protection system. So either a disturbance or steady state variation is called power quality variation. The proposed test system is modeled based on the neural network and fuzzy algorithm. The online symmetrical components are extracted by this above algorithm. The fuzzy is used to separate the oscillating components and average components. Here input for the fuzzy is trained by using neural network. It is based on current samples and very effective in fault classifier using rule base. This method is very much suitable for online implementation.

关 键 词：Symmetrical Components FUZZY Fuzzy Interference System Transmission-Line Protection 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]