Stator Inter-turn Fault Analysis in Doubly-Fed Induction Generators using rotor current based on Finite Element Analysis

Cheng, Jemmie; Ma, Hongzhong; Song, Shuping; Xie, Zaipeng

doi:10.1109/pic.2018.8706293

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…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%

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: 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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Internal electrical fault detection techniques in DFIG-based wind turbines: a review

Bebars

Eladl

Abdulsalam

et al. 2022

Prot Control Mod Power Syst

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The keys factor in making wind power one of the main power sources to meet the world's growing energy demands is the reliability improvement of wind turbines (WTs). However, the eventuality of fault occurrence on WT components cannot be avoided, especially for doubly-fed induction generator (DFIG) based WTs, which are operating in severe environments. The maintenance need increases due to unexpected faults, which in turn leads to higher operating cost and poor reliability. Extensive investigation into DFIG internal fault detection techniques has been carried out in the last decade. This paper presents a detailed review of these techniques. It discusses the methods that can be used to detect internal electrical faults in a DFIG stator, rotor, or both. A novel sorting technique is presented which takes into consideration different parameters such as fault location, detection technique, and DFIG modelling. The main mathematical representation used to detect these faults is presented to allow an easier and faster understanding of each method. In addition, a comparison is carried out in every section to illustrate the main differences, advantages, and disadvantages of every method and/or model. Some real monitoring systems available in the market are presented. Finally, recommendations for the challenges, future work, and main gaps in the field of internal faults in a DFIG are presented. This review is organized in a tutorial manner, to be an effective guide for future research for enhancing the reliability of DFIG-based WTs.

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The Effect of Stator Inter-Turn Short-Circuit Fault on DFIG Performance Using FEM

Mellah

Arslan²,

Sahraoui³

et al. 2022

Eng. Technol. Appl. Sci. Res.

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Doubly-Fed Induction Generators (DFIG) are operated for wind energy production, and as their capacity is increasing, their safety and reliability become more important. Several faults affect the performance of DFIG. The stator winding Inter-Turn Short-Circuit Fault (ITSCF) is one of the most prevalent electric machine failures. This study examined the stator ITSCF effects on DFIG performance for different cases. The DFIG was designed and engineered using the Finite Element Method (FEM) and the Maxwell software, and was examined in healthy operation and four defective cases with various Numbers of Inter-Turn Short Circuit Faults (NITSCF): 4, 9, 19, and 29. These models allowed the examination of the effects and the comparison of each case separately from the healthy state. The comparison was plotted in Matlab to show the effects of the faults. The novelty of this study was that it investigated the effects of different NITSCF on the performance of DFIG and not only on their effect on the stator current and distribution of magnetic flux density. A better understanding of the short circuit effects on the performance of the DFIG can be exploited for subsequent implementations of early fault detection systems.

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Stator Inter-turn Fault Analysis in Doubly-Fed Induction Generators using rotor current based on Finite Element Analysis

Cited by 3 publications

References 2 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

Internal electrical fault detection techniques in DFIG-based wind turbines: a review

The Effect of Stator Inter-Turn Short-Circuit Fault on DFIG Performance Using FEM

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