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

Hossain, S; Abu-Siada, A.; Muyeen, S. M.

doi:10.3390/en11051309

Cited by 118 publications

(83 citation statements)

References 63 publications

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“…The spectral peak frequencies were chosen to reflect amplitude gains expected for a bearing fault such as this causing relative movement within the internal assembly of the generator. 12,15,19,20 Although all WTs in the dataset were the same size and topology, they did not all use the same bearing manufacturer and model. This meant that the ball passing frequency for the inner and outer races (BPFI and BPFO), as well as the rolling element deterioration frequency (BSF), could not be calculated consistently for each WT.…”

Section: Fourier and Order Analysismentioning

confidence: 99%

“…Previous studies have shown that operating parameters at the time of vibration measurement can have a substantial effect on vibration spectra and hence the features that are extracted to detect the fault. 11,12 To help mitigate these issues, vibration samples can be binned based on power output at the time of measurement, a technique widely adopted by developers of condition monitoring software today. Expanding on this idea of grouping vibration data, this paper presents a two-stage methodology to predict generator bearing failure 1 to 2 months before occurrence.…”

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confidence: 99%

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Prediction of wind turbine generator failure using two‐stage cluster‐classification methodology

et al. 2019

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Reducing wind turbine downtime through innovations surrounding asset management has the potential to greatly influence the levelised cost of energy (LCoE) for large wind farm developments. Focusing on generator bearing failure and vibration data, this paper presents a two‐stage methodology to predict failure within 1 to 2 months of occurrence. Results are obtained by building up a database of failures and training machine learning algorithms to classify the bearing as healthy or unhealthy. This is achieved by first using clustering techniques to produce subpopulations of data based on operating conditions, which this paper demonstrates can greatly influence the ability to diagnose a fault. Secondly, this work classifies individual clusters as healthy or unhealthy from vibration‐based condition monitoring systems by applying order analysis techniques to extract features. Using the methodology explained in the report, an accuracy of up to 81.6% correct failure prediction was achieved.

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Section: Fourier and Order Analysismentioning

confidence: 99%

mentioning

confidence: 99%

Prediction of wind turbine generator failure using two‐stage cluster‐classification methodology

et al. 2019

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“…In [19], a critical analysis of the synchrosqueezing transform for the representation of non-stationary signals is proposed: for this aim, the synchrosqueezing transform is improved using iterative generalized demodulation and the proposed method is validated using both numerically simulated and experimental vibration signals of wind turbines planetary gearboxes. Finally, for comprehensive and recent reviews about wind turbine drive-train condition monitoring techniques, refer to [12,20,21].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements

et al. 2020

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Condition monitoring of gear-based mechanical systems in non-stationary operation conditions is in general very challenging. This issue is particularly important for wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of their unavailability time. In this work, a new method for the diagnosis of drive-train bearings damages is proposed: the general idea is that vibrations are measured at the tower instead of at the gearbox. This implies that measurements can be performed without impacting the wind turbine operation. The test case considered in this work is a wind farm owned by the Renvico company, featuring six wind turbines with 2 MW of rated power each. A measurement campaign has been conducted in winter 2019 and vibration measurements have been acquired at five wind turbines in the farm. The rationale for this choice is that, when the measurements have been acquired, three wind turbines were healthy, one wind turbine had recently recovered from a planetary bearing fault, and one wind turbine was undergoing a high speed shaft bearing fault. The healthy wind turbines are selected as references and the damaged and recovered are selected as targets: vibration measurements are processed through a multivariate Novelty Detection algorithm in the feature space, with the objective of distinguishing the target wind turbines with respect to the reference ones. The application of this algorithm is justified by univariate statistical tests on the selected time-domain features and by a visual inspection of the data set via Principal Component Analysis. Finally, a novelty index based on the Mahalanobis distance is used to detect the anomalous conditions at the damaged wind turbine. The main result of the study is that the statistical novelty of the damaged wind turbine data set arises clearly, and this supports that the proposed measurement and processing methods are promising for wind turbine condition monitoring.

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“…The most commonly-adopted model-based FDI techniques for wind turbines are the parity relation method and observer design [10]. However, these approaches require accurate mathematical models to simulate the dynamic behaviors of the process under diagnosis [11].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

Simani

Castaldi

2019

Applied Sciences

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Fault diagnosis of wind turbine systems is a challenging process, especially for offshore plants, and the search for solutions motivates the research discussed in this paper. In fact, these systems must have a high degree of reliability and availability to remain functional in specified operating conditions without needing expensive maintenance works. Especially for offshore plants, a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance. Therefore, this paper presents viable fault detection and isolation techniques applied to a wind turbine system. The design of the so-called fault indicator relies on an estimate of the fault using data-driven methods and effective tools for managing partial knowledge of system dynamics, as well as noise and disturbance effects. In particular, the suggested data-driven strategies exploit fuzzy systems and neural networks that are used to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, which approximate dynamic relations with arbitrary accuracy. The designed fault diagnosis schemes were verified and validated using a high-fidelity simulator that describes the normal and faulty behavior of a realistic offshore wind turbine plant. Finally, by accounting for the uncertainty and disturbance in the wind turbine simulator, a hardware-in-the-loop test rig was used to assess the proposed methods for robustness and reliability. These aspects are fundamental when the developed fault diagnosis methods are applied to real offshore wind turbines.

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

Cited by 118 publications

References 63 publications

Prediction of wind turbine generator failure using two‐stage cluster‐classification methodology

Prediction of wind turbine generator failure using two‐stage cluster‐classification methodology

Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements

Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

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