ملخص الدراسة:

This research handles detecting, classifying and locating of faults on high voltage direct current (HVDC) transmission line (TL) using backpropagation feedforward artificial neural network (ANN). An overhead bipolar HVDC TL model of 940-km long and ±500-kV is chosen to be studied. The HVDC TL post-fault measurements of ac and dc voltages and currents at the rectifier and inverter stations related to pre-fault measurements are used as inputs to the neural networks. In this research, most frequent kinds of bipolar HVDC TL power faults that may occur can be precisely detected and classified while the location of these faults can be determined with an acceptable percentage of error. Analysis of neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. Simulation results have been provided to demonstrate that artificial neural network based methods are efficient in detecting, classifying and locating faults on HVDC transmission lines and achieve acceptable performances.

توثيق المرجعي (APA)