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

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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Wind Turbine Operation Curves Modelling Techniques

Electronics ◽

10.3390/electronics10030269 ◽

2021 ◽

Vol 10 (3) ◽

pp. 269 ◽

Cited By ~ 1

Author(s):

Davide Astolfi

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Wind Turbines ◽

Rotational Speed ◽

Gaussian Kernel ◽

Percentage Error ◽

Support Vector ◽

Power Monitoring ◽

Independent Variables ◽

Error Metrics

Wind turbines are machines operating in non-stationary conditions and the power of a wind turbine depends non-trivially on environmental conditions and working parameters. For these reasons, wind turbine power monitoring is a complex task which is typically addressed through data-driven methods for constructing a normal behavior model. On these grounds, this study is devoted the analysis of meaningful operation curves, which are rotor speed-power, generator speed-power and blade pitch-power. A key point is that these curves are analyzed in the appropriate operation region of the wind turbines: the rotor and generator curves are considered for moderate wind speed, when the blade pitch is fixed and the rotational speed varies (Region 2); the blade pitch curve is considered for higher wind speed, when the rotational speed is rated (Region 2 12). The selected curves are studied through a multivariate Support Vector Regression with Gaussian Kernel on the Supervisory Control And Data Acquisition (SCADA) data of two wind farms sited in Italy, featuring in total 15 2 MW wind turbines. An innovative aspect of the selected models is that minimum, maximum and standard deviation of the independent variables of interest are fed as input to the models, in addition to the typically employed average values: using the additional covariates proposed in this work, the error metrics decrease of order of one third, with respect to what would be obtained by employing as regressors only the average values of the independent variables. In general it results that, for all the considered curves, the prediction of the power is characterized by error metrics which are competitive with the state of the art in the literature for multivariate wind turbine power curve analysis: in particular, for one test case, a mean absolute percentage error of order of 2.5% is achieved. Furthermore, the approach presented in this study provides a superior capability of interpreting wind turbine performance in terms of the behavior of the main sub-components and eliminates as much as possible the dependence on nacelle anemometer data, whose use is critical because of issues related to the sites complexity.

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Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring

Energies ◽

10.3390/en14041105 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1105 ◽

Cited By ~ 2

Author(s):

Davide Astolfi ◽

Francesco Castellani ◽

Andrea Lombardi ◽

Ludovico Terzi

Keyword(s):

Standard Deviation ◽

Wind Turbine ◽

Wind Turbines ◽

Curve Analysis ◽

Gaussian Kernel ◽

Support Vector ◽

Features Selection ◽

Power Curve ◽

Working Parameters ◽

Extracted Power

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible.

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Perspectives on SCADA Data Analysis Methods for Multivariate Wind Turbine Power Curve Modeling

Machines ◽

10.3390/machines9050100 ◽

2021 ◽

Vol 9 (5) ◽

pp. 100

Author(s):

Davide Astolfi

Keyword(s):

Data Analysis ◽

Wind Turbine ◽

Wind Turbines ◽

Data Driven ◽

Power Curve ◽

Research Perspectives ◽

Analysis Methods ◽

Curve Modeling ◽

Working Parameters ◽

Data Analysis Methods

Wind turbines are rotating machines which are subjected to non-stationary conditions and their power depends non-trivially on ambient conditions and working parameters. Therefore, monitoring the performance of wind turbines is a complicated task because it is critical to construct normal behavior models for the theoretical power which should be extracted. The power curve is the relation between the wind speed and the power and it is widely used to monitor wind turbine performance. Nowadays, it is commonly accepted that a reliable model for the power curve should be customized on the wind turbine and on the site of interest: this has boosted the use of SCADA for data-driven approaches to wind turbine power curve and has therefore stimulated the use of artificial intelligence and applied statistics methods. In this regard, a promising line of research regards multivariate approaches to the wind turbine power curve: these are based on incorporating additional environmental information or working parameters as input variables for the data-driven model, whose output is the produced power. The rationale for a multivariate approach to wind turbine power curve is the potential decrease of the error metrics of the regression: this allows monitoring the performance of the target wind turbine more precisely. On these grounds, in this manuscript, the state-of-the-art is discussed as regards multivariate SCADA data analysis methods for wind turbine power curve modeling and some promising research perspectives are indicated.

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Nacelle power curve measurement with spinner anemometer and uncertainty evaluation

Wind Energy Science ◽

10.5194/wes-2-97-2017 ◽

2017 ◽

Vol 2 (1) ◽

pp. 97-114 ◽

Cited By ~ 4

Author(s):

Giorgio Demurtas ◽

Troels Friis Pedersen ◽

Rozenn Wagner

Keyword(s):

Wind Speed ◽

Transfer Function ◽

Wind Turbine ◽

Wind Turbines ◽

Energy Production ◽

Power Curve ◽

Power Performance ◽

Mean Wind Speed ◽

Set Up ◽

Power Curves

Abstract. The objective of this investigation was to verify the feasibility of using the spinner anemometer calibration and nacelle transfer function determined on one reference wind turbine, in order to assess the power performance of a second identical turbine. An experiment was set up with a met mast in a position suitable to measure the power curve of the two wind turbines, both equipped with a spinner anemometer. An IEC 61400-12-1-compliant power curve was then measured for both wind turbines using the met mast. The NTF (nacelle transfer function) was measured on the reference wind turbine and then applied to both turbines to calculate the free wind speed. For each of the two wind turbines, the power curve (PC) was measured with the met mast and the nacelle power curve (NPC) with the spinner anemometer. Four power curves (two PCs and two NPCs) were compared in terms of AEP (annual energy production) for a Rayleigh wind speed probability distribution. For each wind turbine, the NPC agreed with the corresponding PC within 0.10 % of AEP for the reference wind turbine and within 0.38 % for the second wind turbine, for a mean wind speed of 8 m s−1.

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Analysis of Wind Turbine Aging through Operation Curves

Energies ◽

10.3390/en13215623 ◽

2020 ◽

Vol 13 (21) ◽

pp. 5623

Author(s):

Davide Astolfi ◽

Raymond Byrne ◽

Francesco Castellani

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

General Idea ◽

Support Vector ◽

Test Case ◽

Power Curve ◽

Horizontal Axis Wind Turbines ◽

Performance Decline ◽

Institute Of Technology ◽

The Impact

The worsening with age of technical systems performance is a matter of fact which is particularly timely to analyze for horizontal-axis wind turbines because they constitute a mature technology. On these grounds, the present study deals with the assessment of wind turbine performance decline with age. The selected test case is a Vestas V52 wind turbine, installed in 2005 at the Dundalk Institute of Technology campus in Ireland. Operation data from 2008 to 2019 have been used for this study. The general idea is analyzing the appropriate operation curves for each working region of the wind turbine: in Region 2 (wind speed between 5 and 9 m/s), the generator speed–power curve is studied, because the wind turbine operates at fixed pitch. In Region 2 12 (wind speed between 9 and 13 m/s), the generator speed is rated and the pitch control is relevant: therefore, the pitch angle–power curve is analyzed. Using a support vector regression for the operation curves of interest, it is observed that in Region 2, a progressive degradation occurs as regards the power extracted for given generator speed, and after ten years (from 2008 to 2018), the average production has diminished of the order of 8%. In Region 2 12, the performance decline with age is less regular and, after ten years of operation, the performance has diminished averagely of the 1.3%. The gearbox of the test case wind turbine was substituted with a brand new one at the end of 2018, and it results that the performance in Region 2 12 has considerably improved after the gearbox replacement (+3% in 2019 with respect to 2018, +1.7% with respect to 2008), while in Region 2, an improvement is observed (+1.9% in 2019 with respect to 2018) which does not compensate the ten-year period decline (−6.5% in 2019 with respect to 2008). Therefore, the lesson is that for the test case wind turbine, the generator aging impacts remarkably on the power production in Region 2, while in Region 2 12, the impact of the gearbox aging dominates over the generator aging; for this reason, wind turbine refurbishment or component replacement should be carefully considered on the grounds of the wind intensity distribution onsite.

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Analysis of the Wind Turbine Selection for the Given Wind Conditions

Energies ◽

10.3390/en14227740 ◽

2021 ◽

Vol 14 (22) ◽

pp. 7740

Author(s):

Waldemar Kuczyński ◽

Katarzyna Wolniewicz ◽

Henryk Charun

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Wind Turbines ◽

Wind Farm ◽

Medium Size ◽

Wind Speeds ◽

The North ◽

Wind Measurements ◽

Hourly Distribution ◽

The Given

The aim of the current paper is to present an approach to a wind turbine selection based on an annual wind measurements. The proposed approach led to a choice of an optimal device for the given wind conditions. The research was conducted for two potential wind farm locations, situated on the north of Poland. The wind measurements pointed out a suitability of the considered localizations for a wind farm development. Six types of wind turbines were investigated in each localization. The power of the wind turbines were in the range of 2.0 to 2.5 MW and with a medium size of the rotor being in the range of 82 to 100 m. The purpose of the research was to indicate a wind turbine with the lowest sensitivity to the variation of wind speed and simultaneously being most effective energetically. The Weibull density distribution was used in the analyses for three values of a shape coefficients k. The energy efficiency of the considered turbines were also assessed. In terms of the hourly distribution of the particular wind speeds, the most effective wind turbines were those with a nominal power of 2 MW, whereas the least effective were those with the nominal power of 2.3–2.5 MW. The novelty of the proposed approach is to analyze the productivity for many types of wind turbines in order to select the one which is the most effective energy producer. The analyses conducted in the paper allowed to indicate a wind turbine which generates the highest amount of energy independently on the wind speed variation.

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Output wind power curve optimization based on design wind speed concept

Wind Engineering ◽

10.1177/0309524x19901005 ◽

2020 ◽

pp. 0309524X1990100

Author(s):

Cherif Khelifi ◽

Fateh Ferroudji

Keyword(s):

Wind Speed ◽

Wind Energy ◽

Wind Turbine ◽

Wind Power ◽

Wind Turbines ◽

Wind Farms ◽

Operating Conditions ◽

Speed Ratio ◽

Power Curve ◽

Design Wind Speed

The output wind power curve versus wind speed is the most important characterization parameter of wind turbines. It allows quantifying and analyzing the design performances of wind turbines, monitoring its database, and controlling the operation modes and manufacturing products. Wind power curve can be used to select the proper rotor size to estimate the potential of wind energy at candidate wind sites and to assess the control device of the operating conditions. Developing model strategies for wind farms has the basic objectives such as the optimization of wind power produced and the minimization of dynamic loads to provide the best quality of output wind power at reasonable cost. Optimal design of wind turbines requires maximum-closing to the cubical output wind power curve despite technical and economic considerations. This study aims to determine the design wind speed of a wind turbine based on modeling-optimization of the output wind power curve under certain working conditions. The procedure is applied to a unit wind turbine in Gamesa wind farm (G52/850, 10.2 MW, http://www.thewindpower.net ) connected to an electrical grid located in south-west Algeria and extrapolated for other windy sites in Algeria. From simulation results, the design wind speed to inlet wind speed ratio [Formula: see text] increased from 0.35 to 7.68 once [Formula: see text] increased from 0.001 to 2.9999. Consequently, the output wind power predicted an increase of about 17.7% and an annual specific wind energy factor of about 2.55%–4% than nominal value given by the manufacturer, reducing the unit average cost of the electricity, generated by wind farms, by about 18.75%.

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About the Extension of Wind Turbine Power Curve in the High Wind Region

Journal of Solar Energy Engineering ◽

10.1115/1.4041156 ◽

2018 ◽

Vol 141 (1) ◽

Cited By ~ 5

Author(s):

Davide Astolfi ◽

Francesco Castellani ◽

Andrea Lombardi ◽

Ludovico Terzi

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Wind Turbines ◽

Wind Farm ◽

Wind Farms ◽

Test Case ◽

Power Curve ◽

High Wind ◽

High Wind Speed ◽

Wind Speeds

The financial sustainability and the profitability of wind farms strongly depend on the efficiency of the conversion of wind kinetic energy. This motivates further research about the improvement of wind turbine power curve. If the site is characterized by a considerable occurrence of very high wind speeds, it can become particularly profitable to update the power curve management. This is commonly done by raising the cut-out velocity and the high wind speed cut-in regulating the hysteresis logic. Doing this, on one side, the wind turbine possibly undergoes strong vibration and loads. On the other side, the energy improvement is almost certain and the point is quantifying precisely its magnitude. In this work, the test case of an onshore wind farm in Italy is studied, featuring 17 2.3 MW wind turbines. Through the analysis of supervisory control and data acquisition (SCADA) data, the energy improvement from the extension of the power curve in the high wind speed region is simulated and measured. This could be useful for wind farm owners evaluating the realistic profitability of the installation of the power curve upgrade on their wind turbines. Furthermore, the present work is useful for the analysis of wind turbine behavior under extremely stressing load conditions.

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SHAPING OF AN AUTONOMOUS POWER SYSTEM FOR GUARANTEED POWER SUPPLY WITH THE PREDOMINANCE WIND ENERGY

Alternative Energy and Ecology (ISJAEE) ◽

10.15518/isjaee.2018.22-24.028-050 ◽

2018 ◽

pp. 28-50

Author(s):

S. G. Ignatiev ◽

S. V. Kiseleva

Keyword(s):

Energy Storage ◽

Wind Speed ◽

Wind Energy ◽

Wind Turbine ◽

Wind Turbines ◽

Energy Demand ◽

Diesel Generator ◽

Average Wind Speed ◽

Wind Speeds ◽

Average Wind

Optimization of the autonomous wind-diesel plants composition and of their power for guaranteed energy supply, despite the long history of research, the diversity of approaches and methods, is an urgent problem. In this paper, a detailed analysis of the wind energy characteristics is proposed to shape an autonomous power system for a guaranteed power supply with predominance wind energy. The analysis was carried out on the basis of wind speed measurements in the south of the European part of Russia during 8 months at different heights with a discreteness of 10 minutes. As a result, we have obtained a sequence of average daily wind speeds and the sequences constructed by arbitrary variations in the distribution of average daily wind speeds in this interval. These sequences have been used to calculate energy balances in systems (wind turbines + diesel generator + consumer with constant and limited daily energy demand) and (wind turbines + diesel generator + consumer with constant and limited daily energy demand + energy storage). In order to maximize the use of wind energy, the wind turbine integrally for the period in question is assumed to produce the required amount of energy. For the generality of consideration, we have introduced the relative values of the required energy, relative energy produced by the wind turbine and the diesel generator and relative storage capacity by normalizing them to the swept area of the wind wheel. The paper shows the effect of the average wind speed over the period on the energy characteristics of the system (wind turbine + diesel generator + consumer). It was found that the wind turbine energy produced, wind turbine energy used by the consumer, fuel consumption, and fuel economy depend (close to cubic dependence) upon the specified average wind speed. It was found that, for the same system with a limited amount of required energy and high average wind speed over the period, the wind turbines with lower generator power and smaller wind wheel radius use wind energy more efficiently than the wind turbines with higher generator power and larger wind wheel radius at less average wind speed. For the system (wind turbine + diesel generator + energy storage + consumer) with increasing average speed for a given amount of energy required, which in general is covered by the energy production of wind turbines for the period, the maximum size capacity of the storage device decreases. With decreasing the energy storage capacity, the influence of the random nature of the change in wind speed decreases, and at some values of the relative capacity, it can be neglected.

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