Online Fault Detection for Power System using Wavelet and PNN

This paper presents the approach to the problem of fast fault detection in transmission line. The idea is to use concepts from signal processing and wavelet theory to create fast and sensitive fault detection. Then, the artificial neural network was used to classify the fault location in the transmission line. In this study, the output signal of the speed deviations of generator are taken as the input for wavelet analysis. The "oscillation signatures" are recorded using Multi Resolution Analysis (MRA) Wavelet Transform. The MRA decomposes the signal where the components are analyzed for their energy content and characteristic and then used as a feature for different classes and locations of the fault. The same features are also fed to the Probabilistic Neural Network (PNN) to give the location and classification of the fault.