Lin, W.-M., Yang, C.-D., Lin, J.-H. and Tsay, M.-T. (2001) A Fault Classification Method by RBF Neural Network with OLS Learning Procedures. IEEE Transaction Power Delivery, 16, 473-477.
http://dx.doi.org/10.1109/61.956723

has been cited by the following article:

• TITLE: Adaline and FIS Techniques for Fault Identification in HV Transmission Line

  AUTHORS: K. Elango, G. Geetha

  KEYWORDS: Symmetrical Components, Fuzzy, Fuzzy Interference System, Transmission-Line Protection

  JOURNAL NAME: Circuits and Systems, Vol.7 No.10, August 23, 2016

  ABSTRACT: 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.