Online Stator Interturn Short Circuits Monitoring in the DFOC Induction-Motor Drive

Wolkiewicz, Marcin; Tarchała, Grzegorz; Orłowska-Kowalska, Teresa; Kowalski, C. T.

doi:10.1109/tie.2016.2520902

Cited by 84 publications

(47 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Results Of Stator and Rotor Fault Extractionmentioning

confidence: 97%

“…However, along with the increase in the degree of damage, the amplitude values of these slip frequencies increase rapidly (for one broken rotor bar it is 10 times more than in the case of an undamaged rotor). On the contrary, the components calculated in accordance with Equation (16) are too low to be extracted directly from the spectrum shown. Summarizing, the FFT analysis of the instantaneous positive-sequence symmetrical component i1, in particular the changes in the slip frequency amplitudes calculated according to Equation (15) for k = 1: fs − 2sfs and fs + 2sfs, can be used to diagnose the rotor windings of the squirrel cage motor.…”

Section: Results Of Stator and Rotor Fault Extractionmentioning

confidence: 97%

“…For ITSC faults (in low-voltage motors) intensive tests were carried out using various variables, methods and techniques, in open- [13,14] and closed-loop drives [15,16]. The symptoms of damage to stator windings occur in many signals available directly by measurements or indirectly-by calculated (estimated) quantities [13][14][15][16][17]. Most of the significant methods and related techniques are based on monitoring one or more signals and the use of advanced signal processing methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors

et al. 2019

Self Cite

View full text Add to dashboard Cite

Electrical winding faults, namely stator short-circuits and rotor bar damage, in total constitute around 50% of all faults of induction motors (IMs) applied in variable speed drives (VSD). In particular, the short circuits of stator windings are recognized as one of the most difficult failures to detect because their detection makes sense only at the initial stage of the damage. Well-known symptoms of stator and rotor winding failures can be visible in the stator current spectra; however, the detection and classification of motor windings faults usually require the knowledge of human experts. Nowadays, artificial intelligence methods are also used in fault recognition. This paper presents the results of experimental research on the application of the stator current symptoms of the converter-fed induction motor drive to electrical fault detection and classification using Kohonen neural networks. The experimental tests of a diagnostic setup based on a virtual measurement and data pre-processing system, designed in LabView, are described. It has been shown that the developed neural detectors and classifiers based on self-organizing Kohonen maps, trained with the instantaneous symmetrical components of the stator current spectra (ISCA), enable automatic distinguishing between the stator and rotor winding faults for supplying various voltage frequencies and load torque values.

show abstract

Section: Results Of Stator and Rotor Fault Extractionmentioning

confidence: 97%

Section: Results Of Stator and Rotor Fault Extractionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Essentially, these two methods are exploring the 2 nd harmonic in as fault signal [15]. In [16,17], fault detection was realized by comparing the magnitude of the 2 nd harmonics in control voltages, demonstrating that the turn fault severity is proportional to the amplitude of 2 nd harmonics. Unfortunately, the synchronous reference frame 2 nd harmonics exist in healthy conditions due to natural unbalance in a drive system.…”

Section: Introductionmentioning

confidence: 99%

Stator Turn Fault Detection by Second Harmonic in Instantaneous Power for a Triple-Redundant Fault-Tolerant PM Drive

Wang

Griffo

et al. 2018

IEEE Trans. Ind. Electron.

109

View full text Add to dashboard Cite

including the URL of the record and the reason for the withdrawal request. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICSAbstract-Fast and reliable detection of stator faults is of key importance for fail-safe and fault tolerant machine drives in order to immediately trigger appropriate fault mitigation actions. The paper presents a detailed analytical and experimental analysis of the behavior of a closed loop controlled permanent magnet machine drive under inter-turn fault conditions. It is shown that significant 2nd harmonic components in the dq voltages, currents, instantaneous active power (IAP) and reactive power (IRP) are generated during turn fault conditions. The analyses further show that the increase of the 2nd harmonic in IAP and IRP during fault conditions is comparatively higher than that of voltage and current, making them ideal candidates as turn fault indicators. A turn fault detection technique based on 2nd harmonic in IAP and IRP is implemented and demonstrated for a triple redundant, fault tolerant permanent magnet assisted synchronous reluctance machine (PMA SynRM) drive. The effectiveness of the proposed detection technique over the whole operation region is assessed, demonstrating fast and reliable detection over most of the operating region under both motoring and generating mode.Index Terms-Fault tolerant, turn fault, fault detection, fault mitigation, 2 nd harmonic, active power, reactive power.

show abstract

“…In [14], ITSC faults were detected based on stator resistance estimation by an extended Kalman filter and a Luenberger observer. The Motor Current Signature Analysis (MCSA) technique had been successfully used for detecting ITSC faults [15]. However, the method was applied for open-loop IG-based systems.…”

Section: Introductionmentioning

confidence: 99%

Short-Circuit Fault Tolerant Control of a Wind Turbine Driven Induction Generator Based on Sliding Mode Observers

et al. 2017

View full text Add to dashboard Cite

Abstract:The installed energy production capacity of wind turbines is growing intensely on a global scale, making the reliability of wind turbine subsystems of greater significance. However, many faults like Inter-Turn Short-Circuit (ITSC) may affect the turbine generator and quickly lead to a decline in supplied power quality. In this framework, this paper proposes a Sliding Mode Observer (SMO)-based Fault Tolerant Control (FTC) scheme for Induction Generator (IG)-based variable-speed grid-connected wind turbines. First, the dynamic models of the wind turbine subsystems were developed. The control schemes were elaborated based on the Maximum Power Point Tracking (MPPT) method and Indirect Rotor Flux Oriented Control (IRFOC) method. The grid control was also established by regulating the active and reactive powers. The performance of the wind turbine system and the stability of injected power to the grid were hence analyzed under both healthy and faulty conditions. The robust developed SMO-based Fault Detection and Isolation (FDI) scheme was proved to be fast and efficient for ITSC detection and localization.Afterwards, SMO were involved in scheming the FTC technique. Accordingly, simulation results assert the efficacy of the proposed ITSC FTC method for variable-speed wind turbines with faulty IG in protecting the subsystems from damage and ensuring continuous connection of the wind turbine to the grid during ITSC faults, hence maintaining power quality.

show abstract

Online Stator Interturn Short Circuits Monitoring in the DFOC Induction-Motor Drive

Cited by 84 publications

References 33 publications

Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors

Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors

Stator Turn Fault Detection by Second Harmonic in Instantaneous Power for a Triple-Redundant Fault-Tolerant PM Drive

Short-Circuit Fault Tolerant Control of a Wind Turbine Driven Induction Generator Based on Sliding Mode Observers

Contact Info

Product

Resources

About