Article ID Journal Published Year Pages File Type
560649 Mechanical Systems and Signal Processing 2013 11 Pages PDF
Abstract

Synchronous generators and motors constitute critical elements in power generation plants and certain industrial facilities. Damper bars are crucial components of most of these synchronous machines. They enable, among other functions, the direct-on-line starting of these machines, just as if they were asynchronous. Some recent cases, reported by several authors, have demonstrated that eventual failure of damper bars is possible, mainly due to the currents and stresses rising during their operation. In this context, the development of reliable techniques able to diagnose possible damper breakages has attracted significant interest within the fault diagnosis area. The present paper proposes the application of a novel transient-based methodology to diagnose broken damper bars in synchronous motors. This methodology was previously assessed with success in industrial induction motors with diverse sizes. The approach relies on the analysis of the stator startup current demanded by the machine during a direct-on-line starting. To this end, suitable time–frequency decomposition tools are used. In this particular work, the Discrete Wavelet Transform (DWT) is proposed, due to its simplicity, low computational requirements and easy interpretation of its results. Simulation and experimental results obtained with laboratory synchronous machines confirm the validity of the approach for condition monitoring of such windings.

► Application of a transient based method for diagnosis of broken damper bars, a barely threaten topic in the literature. ► Experimental validation of the proposed diagnosis methodology in real synchronous motors. ► Analysis of the physical evolution of the fault components during the transient. ► Study of different loading conditions. ► Accurate state-of-the-art of diagnosis of damper breakages in synchronous motors.

Related Topics
Physical Sciences and Engineering Computer Science Signal Processing
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