This paper describes a digital technique for detecting internal faults in stator windings of synchronous generators. The technique uses positive- and negative-sequence models of the synchronous generator, and voltages and currents measured at the generato

Since the complex variation of line impedance is accentuated as the capacitorʹs own protection equipment operates randomly under fault conditions in series compensated transmission systems, conventional distance protection schemes are limited to certain applications. This paper proposes an adaptive protection scheme based on neural networks with special emphasis on analysis of the first-zone performance. The paper describes in detail the feature extraction, sampling rate, data window and structures of the neural networks. The basic idea of the method is to design a protection scheme using a neural network approach by catching the feature signals in a certain frequency range under fault conditions. This is different from conventional schemes that are based on deriving implicit mathematical equations based on the information obtained by complex filtering techniques. System simulation and test results presented and analysed in the paper demonstrate the feasibility of the proposed scheme.