Fuzzy monitoring of stator and rotor winding faults for DFIG used in wind energy conversion system

Merabet, H.; Bahi, Tahar

doi:10.1504/ijmic.2017.082485

Cited by 12 publications

(4 citation statements)

References 15 publications

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“…Zhao et al [16], discuss WT impeller mass moment computation under five different circumstances. Hichem and Tahar [17] presents fuzzy logic for fault monitoring and identification. Yang and Chai [18] is a review of fault diagnostics research for wind converters.…”

Section: Introductionmentioning

confidence: 99%

Blade imbalance fault identification in doubly fed induction generator through current signature analysis using wavelet transform

Kushwaha,

Yadav,

Maurya

2024

Bulletin EEI

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Using wind turbines (WTs) equipped with doubly fed induction generators (DFIG) is a popular technology for generating renewable energy. To ensure safe operation, prompt maintenance, and better operational reliability, the induction generator used in wind energy must be monitored. In this paper, an analysis is carried out on stator currents of the DFIG machine in a wind farm to identify any blade imbalances in the wind farm. A fault characteristics extraction analysis is carried out on the machine stator currents to detect the fault in the system. Firstly, the mathematical equation of the DFIG blade unbalanced stator current is generated using the DFIG model. Secondly, Park's Transformation is used to modify the stator's 3-phase current. Further, by evaluating the feature frequency amplitude variation in the squared signal by doing a spectral analysis on the stator current vector's squared signal. Lastly, a Simulink model for the DFIG is developed. The suggested approach analyses the fault signal of the imbalanced blade fault at various wind velocities. The outcomes show that the suggested method for diagnosing impeller imbalance faults can successfully locate the fault.

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Section: Introductionmentioning

confidence: 99%

Blade imbalance fault identification in doubly fed induction generator through current signature analysis using wavelet transform

Kushwaha,

Yadav,

Maurya

2024

Bulletin EEI

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“…Some mathematical models derived for ITF in DFIGs can be found in refs. [7][8][9][10][11][12]. Modelbased methods are susceptible to the problem characteristics and even the parameter variation and suffer from the lack of flexibility.…”

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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“…Condition monitoring of rotating machinery is increasingly important to detect problems of machines in time and minimise economic loss of product line breakdown (Zhang et al, 2015). Meanwhile, in any condition monitoring, improved reliability, effective condition monitoring and better efficiency are pursued (Hichem and Tahar, 2017). Rolling element bearing is one of the most commonly used and most critical parts in all kinds of rotary machines.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the lack of grease condition of rolling bearing using acoustic emission

Wang

Liu

Zhu

2019

IJMIC

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Rolling element bearings are the vital parts of machines, and their condition is often critical to the operation or process. Lubricant such as grease can present a film between the bearing surfaces and minimises the friction and wear. Lack of lubricant may lead to ineffective performance or malfunction of the bearing. Therefore, in order to avoid unexpected breakdowns, reliable lubrication monitoring techniques are demanded. Acoustic emission (AE) technology can detect the friction between moving parts in the machines. The object of this paper is to evaluate the grease amount in the rolling element bearing with AE signals. Four parameters of AE are studied, including event count rate, ring count per event, energy rate, and RMS. The first three parameters are derived from AE parameter analysis, and RMS is calculated directly on the continuously sampled signal. Eight amounts of the grease in the same bearing are tested. Experiments on grease consumption with running time are also carried out. According to the results, RMS and energy rate can be used to estimate the remaining amount of the lubricant. The method is also verified by field tests on articulated industrial robots.

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Fuzzy monitoring of stator and rotor winding faults for DFIG used in wind energy conversion system

Cited by 12 publications

References 15 publications

Blade imbalance fault identification in doubly fed induction generator through current signature analysis using wavelet transform

Blade imbalance fault identification in doubly fed induction generator through current signature analysis using wavelet transform

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

Monitoring the lack of grease condition of rolling bearing using acoustic emission

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