Diagnosis of the Doubly-Fed Induction Generator of a Wind Turbine

Bennouna, Ouadie; Héraud, Nicolas; Camblong, Haritza; Rodrı́guez, Miguel A.

doi:10.1260/030952405775992607

Cited by 18 publications

(8 citation statements)

References 21 publications

(24 reference statements)

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“…Stator and rotor currents and stator voltages are measured to monitor the health condition of the generator [53]. A stator and rotor current based data technique is proposed to identify faults within the doubly fed induction generator (DFIG) [54]. Power signals calculated from voltage and current signals is used to detect rotor electrical imbalance as reported in [55].…”

Section: Eelectrical Signal-based Methodsmentioning

confidence: 99%

Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review

2018

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Condition monitoring and early fault diagnosis for wind turbines have become essential industry practice as they help improve wind farm reliability, overall performance and productivity. If not detected and rectified at early stages, some faults can be catastrophic with significant loss or revenue along with interruption to the business relying mainly on wind energy. The failure of Wind turbine results in system downtime and repairing or replacement expenses that significantly reduce the annual income. Such failures call for more systematized operation and maintenance schemes to ensure the reliability of wind energy conversion systems. Condition monitoring and fault diagnosis systems of wind turbine play an important role in reducing maintenance and operational costs and increase system reliability. This paper is aimed at providing the reader with the overall feature for wind turbine condition monitoring and fault diagnosis which includes various potential fault types and locations along with the signals to be analyzed with different signal processing methods.

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Section: Eelectrical Signal-based Methodsmentioning

confidence: 99%

Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review

2018

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“…In the model-based CMFD, accurate mathematical models were constructed to simulate the dynamic behaviors of a WT and its critical subsystems and components [30], [74]- [76] or extract the trend of a signal acquired from the WT [21] using physical principles or data-driven approaches. For example, in [77], [78], WT models were built based on the stochastic recordings of the structural responses of the WTs in the IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS healthy conditions described by vibration, AE, and other signals.…”

Section: H Model-based Methodsmentioning

confidence: 99%

“…It was also found that the rotor line current spectrum offered more information of the rotor phase unbalance than the stator line current spectrum. To verify whether a fault has occurred in a doubly-fed induction generator (DFIG) WT, Bennouna et al [30] proposed a stator and rotor current-based data recondition technique using the equations of a model representing the DFIG WT. The detection of rotor electrical imbalance of an induction generator using power signals (calculated using voltage and current signals) was studied in [31].…”

Section: G Electrical Signalsmentioning

confidence: 99%

A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part II: Signals and Signal Processing Methods

Wang

2015

IEEE Trans. Ind. Electron.

322

147

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This paper provides a comprehensive survey on the state-of-the-art condition monitoring and fault diagnostic technologies for wind turbines. The Part II of this survey focuses on the signals and signal processing methods used for wind turbine condition monitoring and fault diagnosis. ). respectively. A. VibrationMany WT faults induce vibrations of the corresponding WT subsystems, which can be detected by using the signals acquired from vibration sensors. Vibration monitoring is the dominant technique used in almost all commercially available WT condition monitoring systems (CMSs), in which the vibration sensors are usually installed on the casing of the gearbox, generator, main shaft and bearing, and blade surface.The major types of vibration sensors include accelerometers, velocity sensors, and displacement sensors. Accelerometers have the widest working frequency range from 1Hz to 30 kHz, In contrast, velocity sensors have a working frequency range from 10 Hz to 1 kHz and displacement sensors have a working frequency range of 1-100 Hz. Accelerometers are the most widely used vibration sensors in CMFD of WTs for their wide working frequency range. The signals acquired from accelerometers contain the information of accelerations of WT components caused by faults [1]. Displacement sensors have also been used in WT CMFD systems for diagnosing the faults leading to lowfrequency vibrations of WT components. Vibration monitoring has been used for CMFD of WT gearbox [2], [3], bearing [4], rotor and blade [5], generator, tower, main shaft, etc. The amplitude of the vibration signal can indicate the severity of a fault [6]. For example, the amplitude of the 1P frequency components in vibration signals provides a measure of rotor asymmetries [5].Through years of applications, the vibration-based CMFD technologies have been mature and standardized by ISO10816. However, this approach requires the installation of the capital cost and wiring complexity of the WT system. The vibration sensors are usually mounted on the surface or are buried in the body of WT components, making them difficult to access during WT operation. Moreover, the sensors and data acquisition devices are also inevitably subject to failure. Sensor failure may further cause the failure of WT control, mechanical, and electrical subsystems. These could cause additional problems with system reliability and additional operation and maintenance (O&M) costs. In addition, vibration signals usually have a low signal-to-noise ratio (SNR) when used to diagnose an incipient fault. B. AEMaterials that are subjected to stress or strain may emit sound waves, which is called AE [7]. The sources of AE can be located to detect possible defects of a structure using one or more AE sensors. The AE monitoring technology has been used for CMFD of WT blades [1], gearboxes [8], [9], and bearings [10]. As a blade is usually made of various materials and components, damages often grow over critical areas and the interfaces of different internal components inside the blade. Therefore...

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“…Therefore, condition monitoring systems and fault detection systems are required to identify and diagnose wind turbine faults to achieve satisfactory performance and avoid catastrophic disasters. Till date, numerous studies have measured different sensor signals such as vibration [2,3], current [4], acoustic emission (AE) [5], torque [6], and temperature [7] to detect faults in wind turbines. The general scheme of wind turbine fault detection is illustrated in Figure 1 [8,9].…”

Section: Introductionmentioning

confidence: 99%

Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region

Tran

Lan

et al. 2020

Energies

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A novel concept of wind farm fault detection by monitoring the wind speed in the wake region is proposed in this study. A wind energy dissipation model was coupled with a computational fluid dynamics solver to simulate the fluid field of a wind turbine array, and the wind velocity and direction in the simulation were exported for identifying wind turbine faults. The 3D steady Navier–Stokes equations were solved by using the cell center finite volume method with a second order upwind scheme and a k−ε turbulence model. In addition, the wind energy dissipation model, derived from energy balance and Betz’s law, was added to the Navier–Stokes equations’ source term. The simulation results indicate that the wind speed distribution in the wake region contains significant information regarding multiple wind turbine faults. A feature selection algorithm specifically designed for the analysis of wind flow was proposed to reduce the number of features. This algorithm proved to have better performance than fuzzy entropy measures and recursive feature elimination methods under a limited number of features. As a result, faults in the wind turbine array could be detected and identified by machine learning algorithms.

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Diagnosis of the Doubly-Fed Induction Generator of a Wind Turbine

Cited by 18 publications

References 21 publications

Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review

Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review

A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part II: Signals and Signal Processing Methods

Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region

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