Ultrafast Transmission Line Fault Detection Using a DWT-Based ANN

Digital impedance protection of transmission lines suffers from known shortcomings not only as a principle but also as an application. This necessitates developing a new relaying principle that overcomes those shortcomings. Such a principle is offered in this paper and is currently being validated using field data. The principle is a new application of wavelet-based artificial neural networks (ANNs). The application uses high-frequency content of a subset of local currents of one end of a protected line to classify transients on the line protected and its adjacent lines. The scheme can classify transients-including faults-occurring on a protected line, categorize transients on adjacent lines, and pinpoint the line causing the transient event. It is shown that the feature vector of the event can be determined from a subset of local currents without using any voltages altogether. The subset of local currents consists of the two aerial modes of the local current. Modal transformation is used to transform phase currents to modal quantities. Discrete wavelet transform (DWT) is used to extract high-frequency components of the two aerial modal currents. A feature vector is built using the wavelets details coefficients of one level of the aerial modes and is used to train an ANN. Results show that the classes corres-ponding to each transient event type on the protected line and its adjacent lines are almost linearly separable from each other. Results demonstrate that very accurate classification using one-eighth of a cycle of postevent data is possible.