Comparative assessments of binned and support vector regression-based blade pitch curve of a wind turbine for the purpose of condition monitoring

Pandit, Ravi; Infield, David

doi:10.1007/s40095-018-0287-3

Cited by 38 publications

(20 citation statements)

References 34 publications

(38 reference statements)

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“…For these reasons, in order to compare reliably the performance of the same model of wind turbine placed in different sites, it is more appropriate to compare operation curves that are not based on the nacelle wind speed measurements: in this study, the same curves as in [25] are selected, which are the generator speed-power curve and the blade pitchpower curve. In studies like [34][35][36], it was observed that operation variables as the rotor speed, generator speed, and blade pitch are important for a reliable multivariate analysis of the power of a wind turbine [37,38]. In this study, following [38], a step forward with respect to the literature is made because the selected curves involve couples of operation variables and do not involve the nacelle wind speed.…”

Section: Operation Curve Analysismentioning

confidence: 99%

Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis

Astolfi¹,

Byrne²,

Castellani³

2021

Energies

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It is a common sense expectation that the efficiency of wind turbines should decline with age, similarly to what happens with most technical systems. Due to the complexity of this kind of machine and the environmental conditions to which it is subjected, it is far from obvious how to reliably estimate the impact of aging. In this work, the aging of five Vestas V52 wind turbines is analyzed. The test cases belong to two different sites: one is at the Dundalk Institute of Technology in Ireland, and four are sited in an industrial wind farm in a mountainous area in Italy. Innovative data analysis techniques are employed: the general idea consists of considering appropriate operation curves depending on the working control region of the wind turbines. When the wind turbine operates at fixed pitch and variable rotational speed, the generator speed-power curve is studied; for higher wind speed, when the rotational speed has saturated and the blade pitch is variable, the blade pitch-power curve is considered. The operation curves of interest are studied through the binning method and through a support vector regression with a Gaussian kernel. The wind turbine test cases are analyzed vertically (each in its own history) and horizontally, by comparing the behavior at the two sites for the given wind turbine age. The main result of this study is that an evident effect of aging is the worsening of generator efficiency: progressively, less power is extracted for the given generator rotational speed. Nevertheless, this effect is observed to be lower for the wind turbines in Italy (order of −1.5% at 12 years of age with respect to seven years of age) with respect to the Dundalk wind turbine, which shows a sharp decline at 12 years of age (−8.8%). One wind turbine sited in Italy underwent a generator replacement in 2018: through the use of the same kind of data analysis methods, it was possible to observe that an average performance recovery of the order of 2% occurs after the component replacement. It also arises that for all the test cases, a slight aging effect is visible for higher wind speed, which can likely be interpreted as due to declining gearbox efficiency. In general, it is confirmed that the aging of wind turbines is strongly dependent on the history of each machine, and it is likely confirmed that the technology development mitigates the effect of aging.

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Section: Operation Curve Analysismentioning

confidence: 99%

Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis

Astolfi¹,

Byrne²,

Castellani³

2021

Energies

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“…For this reason, therefore, in the wind energy literature, the idea of employing the rotor as wind speed measurement probe has been recently gaining interest: this is the concept of rotor equivalent wind speed [23,24]. Thus, the performance monitoring task can translate into the monitoring of appropriate operation curves (alternative to the power curve), like the rotor speed-power curve [25] and the blade pitch-power curve [25,26].…”

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confidence: 99%

A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis

et al. 2020

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Ageing of technical systems and machines is a matter of fact. It therefore does not come as a surprise that an energy conversion system such as a wind turbine, which in particular operates under non-stationary conditions, is subjected to performance decline with age. The present study presents an analysis of the performance deterioration with age of a Vestas V52 wind turbine, installed in 2005 at the Dundalk Institute of Technology campus in Ireland. The wind turbine has operated from October 2005 to October 2018 with its original gearbox, that has subsequently been replaced in 2019. Therefore, a key point of the present study is that operation data spanning over thirteen years have been analysed for estimating how the performance degrades in time. To this end, one of the most innovative approaches for wind turbine performance control and monitoring has been employed: a multivariate Support Vector Regression with Gaussian Kernel, whose target is the power output of the wind turbine. Once the model has been trained with a reference data set, the performance degradation is assessed by studying how the residuals between model estimates and measurements evolve. Furthermore, a power curve analysis through the binning method has been performed to estimate the Annual Energy Production variations and suggests that the most convenient strategy for the test case wind turbine (running the gearbox until its end of life) has indeed been adopted. Summarizing, the main results of the present study are as follows: over a ten-year period, the performance of the wind turbine has declined of the order of 5%; the performance deterioration seems to be nonlinear as years pass by; after the gearbox replacement, a fraction of performance deterioration has been recovered, though not all because the rest of the turbine system has been operating for thirteen years from its original state. Finally, it should be noted that the estimate of performance decline is basically consistent with the few results available in the literature.

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“…Support vector regression has been vastly employed in general for renewable power generation applications [30] and in particular for several kinds of problem in wind energy, such as, for example, operation curve analysis [31,32], wind farm layout optimization [33], and forecasting [34,35].…”

Section: Support Vector Regressionmentioning

confidence: 99%

SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

2021

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The electric generator is estimated to be among the top three contributors to the failure rates and downtime of wind turbines. For this reason, in the general context of increasing interest towards effective wind turbine condition monitoring techniques, fault diagnosis of electric generators is particularly important. The objective of this study is contributing to the techniques for wind turbine generator fault diagnosis through a supervisory control and data acquisition (SCADA) analysis method. The work is organized as a real-world test-case discussion, involving electric damage to the generator of a Vestas V52 wind turbine sited in southern Italy. SCADA data before and after the generator damage have been analyzed for the target wind turbine and for reference healthy wind turbines from the same site. By doing this, it has been possible to formulate a normal behavior model, based on principal component analysis and support vector regression, for the power and for the voltages and currents of the wind turbine. It is shown that the incipience of the fault can be individuated as a change in the behavior of the residuals between model estimates and measurements. This phenomenon was clearly visible approximately two weeks before the fault. Considering the fast evolution of electrical damage, this result is promising as regards the perspectives of exploiting SCADA data for individuating electric damage with an advance that can be useful for applications in wind energy practice.

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Comparative assessments of binned and support vector regression-based blade pitch curve of a wind turbine for the purpose of condition monitoring

Cited by 38 publications

References 34 publications

Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis

Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis

A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis

SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

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