Wind Turbine Power Curve Upgrades

Astolfi, Davide; Castellani, Francesco; Terzi, Ludovico

doi:10.3390/en11051300

Cited by 40 publications

(51 citation statements)

References 26 publications

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“…Doing this, a reference model is obtained and the performance deterioration is estimated by studying how the residuals between measurements and model estimates change from earlier to later data sets. These kinds of methods have been applied in the wind energy literature for test cases conceptually similar, despite having opposite outcomes (performance improvements, instead of ageing deterioration): wind turbine aerodynamic and-or control technology optimization [17][18][19]. The effective use of these kind of methods for detecting performance changes of the order of few percents of Annual Energy Production has inspired their application for the test case of the present study.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…In this context, as presented in the work Nehrir et al, extensive research has been conducted with the objective of discovering alternative sustainable energy resources. Additionally, many efforts are directed to the technological development and efficiency enhancement of the existing renewable energy generation systems …”

Section: Introductionmentioning

confidence: 99%

Performance enhancement of the artificial neural network–based reinforcement learning for wind turbine yaw control

et al. 2019

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The yaw angle control of a wind turbine allows maximization of the power absorbed from the wind and, thus, the increment of the system efficiency. Conventionally, classical control algorithms have been used for the yaw angle control of wind turbines. Nevertheless, in recent years, advanced control strategies have been designed and implemented for this purpose. These advanced control strategies are considered to offer improved features in comparison to classical algorithms. In this paper, an advanced yaw control strategy based on reinforcement learning (RL) is designed and verified in simulation environment. The proposed RL algorithm considers multivariable states and actions, as well as the mechanical loads due to the yaw rotation of the wind turbine nacelle and rotor. Furthermore, a particle swarm optimization (PSO) and Pareto optimal front (PoF)-based algorithm have been developed in order to find the optimal actions that satisfy the compromise between the power gain and the mechanical loads due to the yaw rotation. Maximizing the power generation and minimizing the mechanical loads in the yaw bearings in an automatic way are the objectives of the proposed RL algorithm. The data of the matrices Q (s,a) of the RL algorithm are stored as continuous functions in an artificial neural network (ANN) avoiding any quantification problem. The NREL 5-MW reference wind turbine has been considered for the analysis, and real wind data from Salt Lake, Utah, have been used for the validation of the designed yaw control strategy via simulations with the aeroelastic code FAST.

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“…As regards the former issue, the fields of research are layout optimization [3][4][5][6][7], cooperative wind turbine control [8][9][10], and yaw active control for wake mitigation [11][12][13][14][15]. As regards the latter issue, several types of wind turbines retrofitting for power production improvement are possible: They can be aerodynamic and based on the modification of the blade profile (installation of vortex generators, passive flow control devices, Gurney flaps, and so on [16][17][18][19][20]), or they can involve the control system and deal with the management of the blade pitch [21], the rotor rpm, the cut-out wind speed, and the high wind speed cut-in (defined as the wind speed at which the wind turbine starts producing again after a gust) [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…Its weakness consists of the fact that it needs vast data sets in order to provide meaningful results: The authors of [28] have addressed this problem by employing time-resolved data sets with sampling time of the order of the second, rather than ten minutes, like the typical Supervisory Control And Data Acquisition (SCADA) data. Some proposals to overcome this issue have been formulated in References [21,29]: The idea is that the power-power approach can be generalized by modeling the power of the wind turbine of interest as a function of operation variables of more than one reference near wind turbines. If an upgrade has a non-negligible effect on the performances of the wind turbine, it can be detected as a variation in the behavior of the residuals between model estimates (when the model is trained with pre-upgrade data) and measurements.…”

Section: Introductionmentioning

confidence: 99%

Numerical and Experimental Methods for the Assessment of Wind Turbine Control Upgrades

et al. 2018

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Megawatt-scale wind turbine technology is nowadays mature and, therefore, several technical improvements in order to optimize the efficiency of wind power conversion have been recently spreading in the industry. Due to the nonstationary conditions to which wind turbines are subjected because of the stochastic nature of the source, the quantification of the impact of wind turbine power curve upgrades is a complex task and in general, it has been observed that the efficiency of the upgrades can vary considerably depending on the wind flow conditions at the microscale level. In this work, a test case of wind turbine control system improvement was studied numerically and through operational data. The wind turbine is multi-megawatt; it is part of a wind farm sited in a complex terrain in Italy, featuring 17 wind turbines. The analyzed control upgrade is an optimization of the revolutions per minute (rpm) management. The impact of this upgrade was quantified through a method based on operational data: It consists of the study, before and after the upgrade, of the residuals between the measured power output of the wind turbine of interest and an appropriate model of the power output itself. The input variables for the model were selected to be some operational parameters of the nearby wind turbines: They were selected from the data set at disposal with a stepwise regression algorithm. This work also includes a numerical characterization of the problem, by means of aeroelastic simulations performed with the FAST software: By mimicking the pre-and post-upgrade generator rpm-generator torque curve, it is subsequently possible to estimate how the wind turbine power curve changes. The main result of this work is that the two estimates of production improvement have the same order of magnitude (1.0% of the production below rated power). In general, this study sheds light on the perspective of employing not only operational data, but also a sort of digital replica of the wind turbine of interest, in order to reliably quantify the impact of control system upgrades.

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Wind Turbine Power Curve Upgrades

Cited by 40 publications

References 26 publications

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

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

Performance enhancement of the artificial neural network–based reinforcement learning for wind turbine yaw control

Numerical and Experimental Methods for the Assessment of Wind Turbine Control Upgrades

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