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

Moosavi, Seyed Mohammad Mahdi; Faiz, Jawad; Abadi, Mohsen Bandar; Cruz, Sérgio M. A.

doi:10.1049/iet-epa.2018.5528

Cited by 20 publications

(15 citation statements)

References 29 publications

(41 reference statements)

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“…The DFIG is controlled by vector control strategy which has been discussed in ref. [14]. Based on this control system, the value of injected active and reactive power to the grid could be adjusted.…”

Section: Experimental Testsmentioning

confidence: 99%

“…This can be done in time or/and frequency domains by signal processing tools such as Discrete Fourier Transform and its short-time version known as the Short Time Fourier Transform, Discrete Wavelet Transform, Hilbert-Huang transform, and Empirical mode Decomposition (EMD). Among numerous signal-based FDD methods proposed in the literature, a small number addressed the ITF detection problem in DFIGs [13][14][15][16]. One of the most popular techniques in this category that has been widely used for ITF detection is the Motor Current Signature Analysis [17].…”

Section: Introductionmentioning

confidence: 99%

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Incipient detection of stator inter‐turn short‐circuit faults in a Doubly‐Fed Induction Generator using deep learning

Alipoor

Mirbagheri

Moosavi

et al. 2022

IET Electric Power Appl

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Wind turbines are increasingly expanding worldwide and Doubly‐Fed Induction Generator (DFIG) is a key component of most of them. Stator winding fault is a major fault in this equipment and its incipient detection is of vital importance. However, there is a paucity of research in this field. In this study, a novel machine learning‐based method is proposed for incipient detection of inter‐turn short‐circuit fault (ITF) in the DFIG stator based on the current signals of the stator. The proposed method makes use of state‐of‐the‐art deep learning methods along with conventional signal processing tools and general machine learning techniques. More specifically, the incipient fault detection problem is regarded as a multi‐class classification problem and a Long Short‐Term Memory network, which is more appropriate for time‐series data is utilised for inference. Furthermore, a variant of the celebrated Empirical mode Decomposition analysis tool is used to extract some well‐known statistical features among which the most informative ones are selected using a new feature selection method. Our tests using experimental data in steady‐state conditions show that the proposed method can accurately detect ITF fault at its initial stage when only one turn is shorted. Moreover, its performance is considerably higher than that of a variety of machine learning‐based methods.

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Section: Experimental Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Incipient detection of stator inter‐turn short‐circuit faults in a Doubly‐Fed Induction Generator using deep learning

Alipoor

Mirbagheri

Moosavi

et al. 2022

IET Electric Power Appl

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show abstract

“…The proposed technique was validated with experimental results on a 7.5 kW WRIM. In [82], the effects of rotor inter-turn shorts and imbalanced rotor resistance on fault indicators were evaluated. The diagnosis of these anomalies was carried out by means of suitable signals present in the DFIG control system (stator current, reactive power and rotor modulating voltage).…”

Section: Wound Rotormentioning

confidence: 99%

Novel Diagnostic Techniques for Rotating Electrical Machines—A Review

Frosini

2020

Energies

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This paper aims to update the review of diagnostic techniques for rotating electrical machines of different type and size. Each of the main sections of the paper is focused on a specific component of the machine (stator and rotor windings, magnets, bearings, airgap, load and auxiliaries, stator and rotor laminated core) and divided into subsections when the characteristics of the component are different according to the type or size of the machine. The review considers both the techniques currently applied on field for the diagnostics of the electrical machines and the novel methodologies recently proposed by the researchers in the literature.

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“…When the stator winding interturn short-circuit fault occurs in a generator, it is assumed that a current in the same direction as the original current is superimposed on the short-circuit turn, and the magnetic field created in the air gap by the current is superimposed with the magnetic field of the generator under normal condition; in other words, the synthetic air-gap magnetic field of the generator with the stator winding interturn short-circuit fault [24].…”

Section: A Negative Sequence Current Mapped To the Shorted Position Of The Switsc Faultmentioning

confidence: 99%

Detection and Location of Stator Winding Interturn Fault at Different Slots of DFIG

Shen

Jiang

2019

IEEE Access

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Generators are critical components for the wind power generation system and process industries. Generating units or production lines may be shut down by generator failure. Most of the methods used in previous research have analyzed the rotor winding interturn fault in the slot location or the stator winding interturn fault in the phase location, but the doubly fed induction generator (DFIG) stator fault in the slot location still needs to be studied further because of the special topological structure and the real factors of the generator model. To solve this problem, this paper determines the pulsating magnetomotive force (MMF) expressions mapped to the shorted position in two forms. Based on 2D discretization and piecewise interpolation, by taking the short-circuit number and the position of short-circuit slot as model parameters, the modified fault model is developed. The simulations show that as an interturn short-circuit fault occurs, the stator's three-phase current is no longer symmetric, where the fault-phase current is bigger than that in healthy operation. In addition, the results show that the interturn short-circuit faults induce negative sequence current that increases with the gravity of fault in the DFIG stator and that the phase difference of the fault-phase and its lagging phase is more than 120 • . Moreover, commonality and heterogeneity existing in the current amplitude and negative sequence features for detecting stator interturn faults at different slots of the DFIG are proposed and analyzed. The results obtained in this paper are important complements to the stator interturn short-circuit analysis and help the practical monitoring and diagnosis of this fault.

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

Cited by 20 publications

References 29 publications

Incipient detection of stator inter‐turn short‐circuit faults in a Doubly‐Fed Induction Generator using deep learning

Incipient detection of stator inter‐turn short‐circuit faults in a Doubly‐Fed Induction Generator using deep learning

Novel Diagnostic Techniques for Rotating Electrical Machines—A Review

Detection and Location of Stator Winding Interturn Fault at Different Slots of DFIG

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