Electric Power Systems Research 81 (2011) 751–766 Contents lists available at ScienceDirect Electric Power Systems Research journal homepage: www.elsevier.com/locate/epsr Experimental validation of doubly fed induction machine electrical faults diagnosis under time-varying conditions Yasser Gritli a,b , Andrea Stefani b , Claudio Rossi b , Fiorenzo Filippetti b,∗ , Abderrazak Chatti a a National Institute of Applied Sciences and Technology, Tunis, Tunisia b University of Bologna, Department of Electrical Engineering, Bologna, Italy article info Article history: Received 3 May 2010 Received in revised form 9 October 2010 Accepted 9 November 2010 Keywords: Doubly fed induction machine Time-varying conditions Frequency sliding Wavelet transforms abstract This paper investigates a new diagnosis technique for incipient electrical faults in doubly fed induction machine for wind power systems under time-varying conditions. The proposed method is based on currents frequency sliding pre-processing, and discrete wavelet transform thereby. The mean power calculation of wavelet signals, at different resolution levels, is introduced as a dynamic fault indicator for quantifying the fault extents. The approach effectiveness is proved for both stator and rotor faults under speed and fault varying conditions. Simulation and experimental results show the validity of the developed method, leading to an effective diagnosis procedure for stator and rotor faults in doubly fed induction machines. © 2010 Published by Elsevier B.V. 1. Introduction For many modern large wind farms, wind turbines equipped with doubly fed induction machine (DFIM) are a well established technology. Different diagnosis methods have been proposed for wind turbines using DFIM [1–4]. Investigations on different failure modes in variable speed induction motors done by industrials and experts have revealed that 45% of motor failures are related to the stator and rotor parts [5]. A detailed analysis of this type of faults can be found in [6]. More concretely, each electrical fault that occurs in the stator/rotor side of a DFIM (short circuits or increasing resis- tance) give rise to a phase dissymmetry because the impedances of the windings are not longer equal or because of a distortion in the airgap flux. Thus the simplest way to emulate a phase unbalance in order to test the effectiveness of diagnosis methods is to insert an additional resistance in series to one phase stator/rotor winding [4] to provoke a phase unbalance. Increasing resistance, or as commonly known in the literature “High-Resistance Connections”, is a common problem that can occurs in any power connections of industrial motor [4,7]. This failure mode can be initiated by gradual abrasion, corrosion and ∗ Corresponding author. Tel.: +39 051 20 93 564; fax: +39 051 209 3588. E-mail addresses: yasser.gritli@esti.rnu.tn (Y. Gritli), andrea.stefani@mail.ing.unibo.it (A. Stefani), claudio.rossi@mail.ing.unibo.it (C. Rossi), fiorenzo.filippetti@mail.ing.unibo.it (F. Filippetti), abderrazak.chatti@insat.rnu.tn (A. Chatti). fretting, leading generally to a local heating which in turn leads to insulation damage. Consequently if the evolution of this type of faults is not detected at an incipient stage, its propagation can lead to more serious failure modes. Several diagnostic methods, such as motor current signature analysis (MCSA), and more recently, flux signature analysis (FSA) and rotor modulation signature analysis (RMSA) have been proposed to detect stator and rotor faults [8–13]. Depending on wind speed, the induction machine operates con- tinuously in time-varying condition. In this context, the classical application of Fourier analysis (FA) for processing the above sig- nals fails as slip and speed vary. Thus the fault components are spread in a bandwidth proportional to the variation. Among differ- ent solutions, high resolution frequency estimation [12] and more recently signal demodulation (SD) technique [13] have been devel- oped to reduce the effect of the non periodicity on the analyzed signals. These techniques, based on FA gives high quality discrim- ination between healthy and faulty conditions but don’t provide time-domain information. This shortcoming in the Fourier analy- sis can be overcome to some extent by analyzing a small section of the signal at a time by means of short-time Fourier transform (STFT). This method was widely used to detect stator and rotor fail- ures in induction motor. As an advanced use of the FFT algorithm, it assumes local periodicity within continuously translated time win- dow. However the fixed size of the chosen window, the difficulties in quantifying the faults extent and the high computational cost required to obtain a good resolution still remain the major draw- backs of this technique [14–16]. wavelet transform (WT), on the other hand, provides greater resolution in time for high frequency 0378-7796/$ – see front matter © 2010 Published by Elsevier B.V. doi:10.1016/j.epsr.2010.11.004