LAUSR

Many methods used for precise fault detection in salient pole synchronous generators (SPSGs) often require a priori knowledge of the healthy case, but this requirement impedes application of the methods since an accurate analysis of the different machine quantity waveforms is not usually carried out during commissioning. The inspection and maintenance processes in SPSGs are also costly and time-consuming; therefore, reliable methods that can detect and discriminate between different faults without comparison with the healthy condition are highly desirable. This paper proposes a precise method for detection and discrimination between different fault types in SPSG. The method does not require healthy machine data and is applied to diagnose both inter-turn short circuits (ITSC) in the field winding and dynamic eccentricities (DE). The proposed non-intrusive detection algorithm is based on advanced signal analysis of stray magnetic field data and can be applied during SPSG operation. Moreover, a distinctive pattern for both ITSC and DE fault enables the discrimination between both considered failures, even if they are present at the same time. The proposed method is validated through finite element modeling and experimentally on a 100 kVA and a 22 MVA SPSG to demonstrate its applicability in real power plants.