Pattern recognition based digital relaying for advanced series compensated line

This paper presents a new approach for fault analysis of advanced series compensated (thyristor controlled series compensated) line using pattern recognition approach. Here, S-transform (ST) is used to process the post fault current signal samples and features are extracted to identify the faulty phase and the faulty section involved in the fault process. The S-transform is an extension of wavelet transform, which possesses superior property over the latter as the moving functions are fixed with respect to time axis while the localizing scalable Gaussian window dilates and translates. Such a transform with moving and scalable localizing Gaussian window, therefore, provides excellent time localization property for different signals. Utilizing the ST, the time-frequency contours (ST coefficients) are generated for various fault conditions in advanced series compensated transmission system. Also an automatic fault recognition system is designed using probabilistic neural network (PNN) to provide the faulty phase and faulty section involved in the TCSC based line. The PNN is tested and trained with the features extracted from the ST contours for different fault situations. The proposed method is tested for all 11 types of shunt faults with wide range of operating conditions and provides accurate results.