Identification of overhead line fault traveling wave and interference clutter based on convolution neural network and random forest fusion

Application of a Centroid Frequency-Based Back Propagation Neural Network Fault Location Method for a Distribution Network Considering Renewable Energy Assessment

“…If, at this time, Equation ( 15) was valid, indicating that the centroid frequency curve was monotonic, then the average value of x j and x k provided the final location results. If Equation ( 14) did not hold, we continued determining whether the difference between x j and x k was less than 1 km, we still used the average value of x j and x k for the final location of the results; otherwise, the use of λ neural network gave the λ-fault distance curve for this fault condition, and then gave the λ curve location results x p and x m according to (15), which were then combined with (16) to determine the final localization results. The transient fault current collected at both ends of the line was transformed by FFT (Fast Fourier Transform) to extract the frequency components, and the centroid frequency fG and auxiliary criterion indicators were calculated, according to the current spectrum.…”

“…The traveling wave method cannot be affected by the distribution network operation mode and high impedance grounding easily. Recently, scholars have proposed fault location methods based on the traveling wave method, such as in References [14][15][16][17][18]. The traveling wave method is based on the time-frequency characteristic.…”

“…The traveling wave method is based on the time-frequency characteristic. The methods proposed in References [14][15][16][17][18] have to obtain the arrival time of the wave head and the speed; this is called the time-domain traveling wave method. This method solves the fault distance by measuring the arrival time difference of each wave head and establishing the equations about the fault distance, propagation time, and wave speed.…”

“…Reference [14] gave the sources of the first three incoming wave heads for different fault distances. Reference [15] classified the traveling waves into four categories and analyzed them to obtain the categories of refracted waves to which the initial antipodal waves of the near-end fault and the far-end fault belong. Reference [16] was based on the arrival time differences between different frequency components of the traveling wave for location, using the wavelet transforms to carry out the time-frequency analysis.…”

“…Existing fault analysis methods in References [14][15][16][17][18][19][20][21][22][23][24][25] cannot be applied to both hybrid lines and feeders with branches in distribution networks and have significant limitations. Distribution network fault characteristics are complex, and there are differences between various distribution network fault characteristics.…”