Fault identification in an AC-DC transmission system using neural networks

The authors explore the possibility of using neural networks to identify faults that can occur in an AC-DC power system. Three types of neural network models have been studied and are compared. These networks can sense AC bus voltages either as root mean square (RMS) values (with or without phase angle information) or as sampled instantaneous values of sine waves. Depending on which method is used, some confusion can occur in distinguishing a line to line fault from a remote AC fault. A delay of 1-2 cycles in detection of faults when using RMS values is expected due to the algorithm required for determining the RMS value. This may not be too critical in practice. However, where this delay is unacceptable, instantaneous values may be used. Based on the ability of these networks to distinguish reliably between different types of faults, appropriate control measures can be taken to improve the dynamic performance of the AC-DC power system