Performance Evaluation of Signal Processing Tools Used for Fault Detection of Hydrogenerators Operating in Noisy Environments

Abstract

Signal processing plays a crucial role in addressing failures in electrical machines. Experimental data are never perfect due to the intrusion of undesirable fluctuations unrelated to the investigated phenomenon, namely so-called noise. Noise has disturbing effects on the measurement data and, in the same way, could diminish or mask the fault patterns in feature extraction using different signal processors. This article introduces various types of noise occurring in an industrial environment. Several measurements are performed in the laboratory and power plants to identify the dominant type of noise. Fault detection in a custom-made 100-kVA synchronous generator under an interturn short-circuit fault is also studied using measurements of the air-gap magnetic field. Signal processing tools such as fast Fourier transform, short-time Fourier transform (STFT), discrete wavelet transform, continuous wavelet transform (CWT), and time-series data mining are used to diagnose the faults, with a central focus on additive noise impacts on processed data. Two novel patterns are introduced based on STFT and CWT for interturn short-circuit fault detection of synchronous generators that do not need a priori knowledge of a healthy machine. Useful methods are presented for hardware noise rejection.