A transmission line fault locator based on Elman recurrent networks

Two novel proposed discrete wavelet transform and filter based approaches for short-circuit faults detection in power transmission lines

“…Wavelet transform was again used for selecting distinctive features of the faulty signals, and then, ERN is utilized to determine the fault location using the features obtained by wavelet transform. The model can be only used for locating balanced shortcircuit faults [23]. A technique for fault detection which uses finite impulse response artificial neural network (FIRANN) was proposed in [24].…”

“…It was shown that the method can be used for classifying short-circuit faults in transmission lines. A fault location model of a transmission line that uses Elman recurrent network (ERN) was presented in [23]. Wavelet transform was again used for selecting distinctive features of the faulty signals, and then, ERN is utilized to determine the fault location using the features obtained by wavelet transform.…”

Unsymmetrical fault diagnosis in transmission/distribution networks

“…Knowledge based fault detection [3][4][5][6][7][8][9][10][11][12][13][14][15][16] utilises prior knowledge of the system quantities (voltages and currents and waveforms) under different fault and system operating conditions. This knowledge is then used to train a learning system to identify abnormal conditions and classify them.…”

Convergence of gradient method for Elman networks

“…The Elman network has been proven powerful for classifying time series data [4] and for modeling linear and nonlinear dynamical systems [5] . Elman networks have also been used in many different fields such as language acquisition [6] , chaos generation [7] , fault location [8] and flow estimation [9] . Elman networks are included in the popular Matlab neural network toolbox [10] .…”