A turn fault in the stator winding of a generator causes a large circulating current to flow in the shorted turns. If left undetected, turn faults can propagate, leading to phaseground or phase-phase faults. Incipient detection of turn’s faults is essential to avoid hazardous operating conditions and reduce down time. At present the synchronous generators are protected against almost all kind of faults using differential methods of protection. All kind of faults develops into inter winding fault by damaging inter winding insulation. So it is necessary to protect the synchronous generator from inter winding faults which represents the protection against all kind of faults. There are different method based techniques for analyzing generator incipient/inter turn faults on stator side. They are circuit based, field based, wavelet based, artificial intelligence based, fuzzy based, artificial neural networks based. Machine performance characteristics that could be monitored to diagnose the stator inter-turn fault in generator include line current, terminal voltage, torque pulsations, temperature rise due to excessive losses, shaft vibrations, air-gap flux and speed ripples. So in this we are developing a mathematical model or method based on online/offline condition monitoring system by analyzing various conditions and collecting various samples of voltage and current (i.e. normal and abnormal) for protection of generators against faults (i.e. means incipient/inter turn faults) on stator side. The main Objective is to develop a mathematical model or method based on online/offline condition monitoring system by analyzing various conditions (i.e. normal and abnormal) for protection of generators against faults (i.e. means incipient/inter turn faults)
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