An artificial neural network-based solution to locate the multilocation faults in double circuit series capacitor compensated transmission lines

During thunder storms, multilocation faults may occur at different locations in different phases of the 3 phase transmission lines at same or different time. This paper proposes an artificial neural network-based solution to locate the multilocation faults in double-circuit series capacitor compensated transmission lines (SCCTLs), unlike previous works that only locate the fault at single location. Although various fault location schemes have been proposed for normal shunt faults occurring at 1 location in SCCTL, nevertheless, finding the locations of multilocation faults in double circuit SCCTL has not been addressed so far. The proposed artificial neural network-based method determines the location of multilocation faults by using current and voltage signals of 1 end of line only, thus avoiding the need of communication link. The signals are preprocessed by using discrete wavelet transform. A comparative study of various neural networks such as feed-forward back-propagation network with Levenberg-Marquardt algorithm, Elman recurrent neural network with gradient descent algorithm, radial basis function neural network has been carried out. The proposed method is not affected by variation in different parameters viz. fault type, fault location, fault inception angle, fault resistance, degree of series compensation, and location of series capacitor. The key advantage of the method is that it correctly estimates the locations of multilocation faults as well as single-location fault, thus making it more reliable and accurate as compared to conventional fault location schemes.