Detection of mixed eccentricity fault in doubly‐fed induction generator based on reactive power spectrum

Abstract

Wide application of wind energy in the world towards reduction of global warming, have made the low cost maintenance of these turbines very important. Basically, mechanical faults such as gear-box faults in these turbines lead to a long down time and consequently heavy financial loss. Generally, these faults cause eccentricity in the generator air gap, so by its early detection the fault can be largely prevented. In this paper, eccentricity fault detection is studied in doubly-fed induction generator (DFIG) which is the most established generator in wind turbines and also is more prone to mechanical faults compared to other technologies. One of frequency components of reactive power is introduced as eccentricity fault index. As the reactive power is always evaluated in the control system, there is no need to include a new hardware for fault detection. Finite elements method is used to model the wound rotor induction machine (WRIM) which is coupled with closed-loop control system. Following the validation of the model in the healthy case experimentally, eccentricity under different operating conditions is included in the modeling. Finally, a simple procedure based on simple signal processing methods is introduced for eccentricity fault diagnosis considering the operating conditions of the wind turbine.

Comparison of rotor electrical fault indices owing to inter‐turn short circuit and unbalanced resistance in doubly‐fed induction generator

“…So, λ qs is almost zero while λ ds is equal to the resultant flux-linkage of the stator. As a result, any irregularity in the magnetic flux manifests well in λ ds [7]. Since the angular speed of the synchronous reference frame is 2π f s the fault indices in the stator flux-linkage are as follows:…”

“…The generator, as the energy conversion heart of the WT, is prone to mechanical and electrical faults. Eccentricity as a major mechanical disorder of DFIG has been addressed and some fault detection methods have been proposed [2,7,8]. Electrical faults could occur in stator or rotor side windings.…”

Induction machine model with space harmonics for fault diagnosis based on the convolution theorem

“…A close inspection of (29) shows the presence of two terms in the summation, each of them corresponding to one of the coil's conductors:…”

“…Different techniques that can be used for implementing a CBM system for IMs [19], such as the analysis of currents [20][21][22][23][24], vibrations [24][25][26][27], instantaneous power [28], reactive power [29], apparent power [21], voltages [30,31], back EMFs induced tooth-coil windings [32,33], voltage injection [34], thermal images [35,36], internal flux [37,38], acoustic emissions [39,40], etc. Among these methods, the analysis of the machine current signature (MCSA) method has attracted an special interest [24,41], because it is non invasive (it requires only a current probe that can be attached to the line which feeds the machine), fast an easy to implement online (it uses a FFT to obtain the current spectrum, where the characteristic fault signatures can be detected), and is able to detect different, and possibly simultaneous [42][43][44], types of faults, and it can be operated on line.…”

“…Eccentricity is a common type of IM fault [21,28,29,[50][51][52][53][54][55], which is caused primarily because of maladjustment of bearings, load imbalance, shaft flexibility, thermal deformations, or misalignments [56]. The asymmetry of the magnetic field in the non-uniform air gap of the eccentric IM produces radial forces [53], that is, an unbalanced magnetic pull (UMP) [57][58][59][60][61], which generates abnormal vibrations, damages in the shaft bearings, destructive rotor-stator rub [62] or even sparks during the starting of the motor [63].…”