Fault Classification in a Transmission Line Using Levenberg–Marquardt Algorithm Based Artificial Neural Network

A real-time fault diagnosis system for high-speed power system protection based on machine learning algorithms

“…In order to assess its performance, we implemented the SFDS during the simulation study of the WSCC 3-machines 9-bus test system (see Figure 14) [22], and we modeled and evaluated this latter using the Matlab/Simulink software package. Since we were concerned with the 180 Km transmission line joining bus (6) and bus (9), the training and test data contained the phase-domain voltages and currents measured by CT6, VT6, CT9, and VT9, essentially. The training and test data were randomly and uniformly generated under various fault conditions.…”

“…According to [14,15], when a fault occurs in the forward direction of a relay, the modal superimposed voltages Δ [•] and currents Δ [•] have different polarities. That means that if an internal fault is assumed to happen on our line of interest, we should have the following properties at buses (6) and (9).…”

“…This length has been determined from the time needed for a refracted  Wave to go back and forth between bus (6) and bus (4) at the speed of light. The Feature Extraction Process (FEP) buffer contains the RMS values of the phase-domain superimposed currents and voltages of bus (6), the RMS value of the zero superimposed current of bus (6) and the RMS values of the phasedomain superimposed currents and voltages of bus (9). The remaining buffers are used for the fault detection process.…”

“…The kinds of fault diagnosis systems that have been suggested in the literature are mainly based on classical approaches and artificial neural networks. Those that are based on classic circuit [2,3], symmetrical theories [4][5][6], digital wavelet transform (DWT) [4][5][6], and based on artificial neural network (ANN) [8,9]. The symmetrical theories and digital wavelet transform [4][5][6] require phasor estimation whose correctness depends highly on the efficiency of non-power frequency filtering [7].…”

“…The symmetrical theories and digital wavelet transform [4][5][6] require phasor estimation whose correctness depends highly on the efficiency of non-power frequency filtering [7]. The systems based on ANN [8,9] give satisfying results when it comes to classification tasks (fault type) but bad results when it comes to regression tasks (fault location). Moreover, ANN is challenging to interpret (it is hard to understand the reasons behind the results they give), structure (there is no specific procedure to determine the architecture of artificial neural networks) and train (computational complexity increases with the number of neurons and layers).…”