Detecting rotor faults of SCIG based wind turbine using PSD estimation methods

Noureddine, Lahcene; Hafaifa, Ahmed; Kouzou, Abdellah; Guemana, Mouloud; Salam, Abudura

doi:10.1109/icmic.2016.7804176

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…For the special situation of the studied stator current data in this paper, the sampling frequency of 10 kHz has demonstrated to be very low for the estimation of the frequency bandwidths of the data that were most sensitive to damage, as shown in Figures 2 and 3. Through the Hilbert transform study that has been previously presented in [1], [17], [18], [16], [20], [34], [35], this transform is fully not local, and all the samples are required to be calculated with Hilbert transform at each point. The calculation of the Hilbert transform of a 15 defects presented on the rotor, and also appropriate for real-time implementations.…”

Section: Resultsmentioning

confidence: 99%

Rotor Fault Detection of Wind Turbine Squirrel Cage Induction Generator Using Teager–kaiser Energy Operator

2017

Control and Intelligent Systems

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This paper proposes the use of the Teager-Kaiser energy operator (TKEO) for the detection of a broken rotor bar (BRB) in a squirrel cage induction generator, which is used usually in wind energy systems. The TKEO is investigated using the non-stationary signals representation in the time/frequency domain by sensing the estimation of the instantaneous amplitudes and frequencies present on the stator of wind turbine. This situation of the rotor bar breaks is caused mainly by combinations of mechanical and electrical stresses, including vibration, pendulum, thermal and centrifugal and rarely it can appear due to the fabrication and the quality of the used material's origins. Consequently, the case of a BRB may occur under these circumstances and it is a common fault which can occur in the rotor of the squirrel cage induction generator, it can be estimated to be around 20% among the eventual faults of this kind of machine. The necessary properties for the development of this representation based on the TKEO in time and frequency are presented, which can improve the diagnosis of the studied wind energy system. The obtained results of the presented method have been experimentally validated by robustness tests, which raise the effectiveness of the proposed approach, including a 4-kW induction generator for online rotor bar fault detection using the spectrum analyser and stator current sensors board designed especially for this experimental purpose.

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Section: Resultsmentioning

confidence: 99%

Rotor Fault Detection of Wind Turbine Squirrel Cage Induction Generator Using Teager–kaiser Energy Operator

2017

Control and Intelligent Systems

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“…Therefore, the challenge is to improve reliability and sustainability of WECS under faulty conditions with fault tolerant control (FTC). Several research work in literature use diagnostic methods to detect the defect rotor bars in SCIG, experimental results in (Lahcène et al, 2016) demonstrate the effectiveness of diagnostic method by analyzing stator current signature in broken rotor bar SCIG, in (Park et al, 2012) a Fast Fourier Transform (FFT) of stator current to detect the faults in SCIG, in (Obaid et al, 2016) a model-based Support Vector Classification (SVC) method to test experimentally the sanity of rotor bars at full load conditions, a rotor torque monitoring method with finite element to detect the failures in rotor bars of SCIG, in (Lešić et al, 2013) fault tolerant control scheme of variable pitch variable speed wind turbine to detect faults in rotor bars in order to avoid wind turbine shutdown.…”

Section: Introductionmentioning

confidence: 99%

Advanced Fault-tolerant Control Strategy of Wind Turbine Based on Squirrel Cage Induction Generator with Rotor Bar Defects

Roubah

Toubakh²,

Sayed‐Mouchaweh

2019

PHM_CONF

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Several research works in Wind Energy Conversion Systems (WECS) based on Squirrel Cage Induction Generator (SCIG) are developed for isolated site applications. Where, WECS are usually controlled by the Maximum Power Point Trucking (MPPT) algorithm in order to extract the maximum power from wind with constant operating point of SCIG. However, this WECS based on SCIG generator suffers from the problem of breaking rotor bars, therefore a failure in rotor bars is particularly a challenging task because: 1) it can change the operating point generated by MPPT algorithm, 2) increase mechanical vibrations, 3) affect the operation of gearbox, 4) inject a harmonic current in the grid. Therefore, Fault Tolerant Control (FTC) scheme is necessary to allow an optimum energy production, to reduce the maintenance costs and to increase the WECS availability. In this work, a new fault diagnosis approach based on frequency analysis of stator currents is used to detect the failure in bars of SCIG, then FTC algorithm is developed to adjust the pitch angle in max wind speed zone by determining a new operating point according to the wind speed, which leads to extend the lifetime of WECS. Simulation results using MATLAB SIMULINK showed a nominal current in healthy rotor bars and the lifetime of WECS was increased.

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“…The eigenvector method allows the calculation of a desired noise subspace vector from noise or signal subspace eigenvectors by forcing spurious zeros into the unit circle in order to differentiate spurious zeros from real zeros. The power spectral density is obtained from the statement in Equation 11 with the eigenvector method [23,24]. There was a curve peaking at approximately 0.001 units at 45 Hz and descending to zero at 125 Hz at the normal signal PSD in Figure 11.…”

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confidence: 99%

Analysis of Heart Sound Signals in Ebstein's Anomaly

Akgün

2022

The Journal of Cognitive Systems

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Ebstein's anomaly is an abnormality in the pediatric heart disease group. The anomaly is described as a structural defect by considering the whole heart. It can be manifested by typical symptoms in auscultation and can be detected with other diagnostic methods such as ECG. Systolic ejection click and murmur are the most important symptoms in the diagnosis of disease. In this study, heart sound signal recorded from a 13-year-old patient was analyzed with different numerical methods along with a normal heart sound signal. The signals were first examined in the time plane and findings in auscultation were observed. The frequency components of the signals were then obtained. Additional frequency components emerged in findings of disease in this plane compared to the normal one. Spectrograms enable to observe the differences in time frequency and amplitude components. Bispectral analysis was performed as a high order spectral analysis method by diversifying the analysis. In bispectral analysis of the anomaly, click and murmurs are manifested by equiphase surfaces distributed at high frequencies.Lastly, the power spectrum density of the signals were examined. The decrease in the additional power peak and power rating of the diseased signal was remarkable.

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Detecting rotor faults of SCIG based wind turbine using PSD estimation methods

Cited by 5 publications

References 10 publications

Rotor Fault Detection of Wind Turbine Squirrel Cage Induction Generator Using Teager–kaiser Energy Operator

Rotor Fault Detection of Wind Turbine Squirrel Cage Induction Generator Using Teager–kaiser Energy Operator

Advanced Fault-tolerant Control Strategy of Wind Turbine Based on Squirrel Cage Induction Generator with Rotor Bar Defects

Analysis of Heart Sound Signals in Ebstein's Anomaly

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