Advanced fault diagnosis techniques and their role in preventing cascading blackouts

Advanced Fault Diagnosis Techniques and Their Role in Preventing Cascading Blackouts. (December 2006) Nan Zhang, B.S., Tsinghua University, China; M.S., Tsinghua University, China Chair of Advisory Committee: Dr. Mladen Kezunovic This dissertation studied new transmission line fault diagnosis approaches using new technologies and proposed a scheme to apply those techniques in preventing and mitigating cascading blackouts. The new fault diagnosis approaches are based on two time-domain techniques: neural network based, and synchronized sampling based. For a neural network based fault diagnosis approach, a specially designed fuzzy Adaptive Resonance Theory (ART) neural network algorithm was used. Several application issues were solved by coordinating multiple neural networks and improving the feature extraction method. A new boundary protection scheme was designed by using a wavelet transform and fuzzy ART neural network. By extracting the fault generated high frequency signal, the new scheme can solve the difficulty of the traditional method to differentiate the internal faults from the external using one end transmission line data only. The fault diagnosis based on synchronized sampling utilizes the Global Positioning System of satellites to synchronize data samples from the two ends of the transmission line. The effort has been made to extend the fault location scheme to a complete fault detection, classification and location scheme. Without an extra data requirement, the new approach enhances the functions of fault diagnosis and improves the performance. Two fault diagnosis techniques using neural network and synchronized sampling are combined as an integrated real time fault analysis tool to be used as a reference of