DFIG Wind Generators Fault Diagnosis Considering Parameter and Measurement Uncertainties

Mojallal, Aslan; Lotfifard, Saeed

doi:10.1109/tste.2017.2761842

Cited by 13 publications

(7 citation statements)

References 30 publications

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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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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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“…Since accurate and rapid fault detection and isolation/identification (FDI) of wind turbines can better prevent further damage to wind power systems, it is necessary to minimize unit downtime. In Mojallal and Lotfifard (2018), a robust FDI method for doubly-fed wind turbines based on the bond graph theory is proposed to detect and locate faults of different combinations. In Cheng et al (2018), a new method for fault diagnosis of wind turbine drive train gearbox using DFIG rotor current signal analysis is proposed.…”

Section: Introductionmentioning

confidence: 99%

Rotor-current-based fault detection for doubly-fed induction generator using new sliding mode observer

Jiang

Wang

et al. 2020

Transactions of the Institute of Measurement and Control

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To detect the fault of the doubly-fed induction generator (DFIG), in this paper, a fault detection method of novel sliding mode observer is proposed, without a velocity sensor. In addition to better elimination of chattering for the new sliding mode observer, at the same time, it has better stability and faster convergence speed than the traditional sliding mode observer. Firstly, the sliding mode observer is built according to the mathematical model of the DFIG. Then, the rotor current and the rotational speed are estimated. After comparing the actual rotor current value with the observed value, the self-detection of the fault for the DFIG is realized. Secondly, three faults of grid voltage sags failure, DFIG inter-turn stator fault and rotor current sensor fault are given respectively. After that the Simulink simulation model is built under different fault conditions. It is proved by simulation that this sliding mode observer can well detect faults occurring at different positions. Additionally, it can also be proved that the sliding mode observer has the characteristics of fast response and good stability.

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“…Voltage fluctuations, current harmonics, unbalance current and change in flux density create overheating [6] in the generator windings and other control problems are common with integrated system. Due to this, the parameters of the generator and associated devices are varying, the variation in parameters is considered as a parametric uncertainty which affects the control signals of the studied system [7].…”

Section: Introductionmentioning

confidence: 99%

Stability assessment and robust controller design of grid interactive offshore wind farm and marine current farm with STATCOM and BFCL

2020

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DFIG Wind Generators Fault Diagnosis Considering Parameter and Measurement Uncertainties

Cited by 13 publications

References 30 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

Rotor-current-based fault detection for doubly-fed induction generator using new sliding mode observer

Stability assessment and robust controller design of grid interactive offshore wind farm and marine current farm with STATCOM and BFCL

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