This paper presents an equipment for monitoring synchronous generators condition through characteristics of the time derivative of the external magnetic field. The developed monitoring methodology allows the identification of established or incipient faults, by detecting changes in the magnetic signature of the synchronous generator. In this methodology, the measurement of signals outside the machine gives this equipment a non-invasive characteristic, allowing its monitoring without interfering or disturbing its operation. The developed system includes the specification of magnetic field sensors, signal measurement and processing equipment, as well as software for analysis and monitoring. The validation of the methodology used in this system was carried out through the analysis of experimental data, presenting efficient results in the detection of electrical and mechanical faults in synchronous generators of an experimental test bench and a hydroelectric power plant. As a result, the commercial specification of this equipment was obtained and two units were implemented in a hydroelectric power plant to monitor 305 MVA synchronous generators.
Condition monitoring of synchronous generators through non-invasive methods is widely requested by maintenance teams for not interfering the machine operation. Among the techniques used, external magnetic field monitoring is a recent strategy with great potential for detecting incipient faults. In this context, this paper proposes the application of a simple strategy with low computational cost to process data of external magnetic field time derivative signals for the purposes of condition monitoring and fault detection in synchronous machines. The information of interest is extracted from changes in the magnetic signature of the synchronous generator, obtained from frequency spectra of monitored signals using induction magnetic field sensors. The process forms a set of time series that reflects constructive and operational characteristics of the machine. The Shewhart control chart method is applied for anomaly detection in these time series, allowing the detection of changes in the machine magnetic signature. This method is employed in an algorithm for continuous condition monitoring of synchronous generators, presenting as output a global change indicator for the multivariable problem associated with magnetic signature monitoring. Correlation matrices are used to improve the algorithm response, filtering series with similar variation patterns associated with detected events. The proposed method is validated through tests on an experimental bench that allows the controlled imposition of faults in a synchronous generator. The proposed global change indicator allows the automatic detection of stator and rotor faults with the machine synchronized with the commercial power grid. The proposed methodology is also applied on data obtained from an equipment installed in a 305 MVA synchronous generator of a hydroelectric power plant where the evolution of an incipient fault, i.e., a mechanical vibration fault, has been detected.
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