Assessment of Wind Turbine’s Power Curve Based on Performance Reliability Theory

2014 ◽  
Vol 538 ◽  
pp. 270-273
Author(s):  
Mu Yong Zhang ◽  
Xiao Ming Rui ◽  
Juan Huo ◽  
Yan Feng Zhao
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
North China ◽  
Reliability Theory ◽  
Power Curve ◽  
Important Indicator ◽  
Assessment Measure ◽  
Generation Capacity ◽  
Power Curves ◽  
Unit Performance

Power curve of wind turbine is an important indicator in assessing unit performance and evaluating unit generation capacity. However, due to the ambiguity of assessment measure and lack of assessment methods, it is difficult to assess the power curve. For solving the problem, a method based on performance reliability is proposed in this article. Using the operating data recorded in SCADA, the measured power curve was tested, and according to the manufacturer promised power curve, based on performance reliability theory, a method of assessing power curve was proposed. Using the method provided herein, the power curves of 5 wind turbines, which were erected in a wind farm in north China, were assessed. Results show that the method is accurate, simple and practical. And it provides an important theoretical basis for power curve assessment of wind turbine in site.

Power curve model classification to estimate wind turbine power output

2018 ◽  
Vol 43 (3) ◽  
pp. 213-224 ◽  
Author(s):  
Bharti Dongre ◽  
Rajesh K Pateriya
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Spline Interpolation ◽  
Specific Power ◽  
Power Curve ◽  
Model Classification ◽  
Curve Model ◽  
Modelling Methodology ◽  
Power Curves ◽  
Modelling Data

This article presents a comparative study of empirical power curve models to estimate the output power of the turbine as a function of the wind speed. In these models, modelling strategy relies on the objective of modelling, data being used for the modelling and targeted accuracy. It has been observed that models based on presumed shape of power curve lack desired accuracy since these are developed using the power ratings of wind turbine which are not sufficient to exactly replicate the turbine’s actual behaviour. The performance of various models which comes under manufacturer power curve modelling methodology has been compared with reference to commercially available wind turbines. It has been found that power curves obtained through method of least squares and cubic spline interpolation methods exactly match with manufacturer power curve, whereas 5PL method gives sufficiently accurate results. Modelling based on actual data of wind farm has been found to be a powerful technique for developing site-specific power curves.

Statistical calculation of thrust curve of a wind turbine based on available power curve and general specifications data

2021 ◽  
Author(s):  
Evgeny Atlaskin ◽  
Irene Suomi ◽  
Anders Lindfors
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Curve ◽  
Wake Effect ◽  
Thrust Coefficient ◽  
Simple Method ◽  
Wind Speeds ◽  
Power Curves

<p>Power curves for a substantial number of wind turbine generators (WTG) became available in a number of public sources during the recent years. They can be used to estimate the power production of a wind farm fleet with uncertainty determined by the accuracy and consistency of the power curve data. However, in order to estimate power losses inside a wind farm due to wind speed reduction caused by the wake effect, information on the thrust force, or widely used thrust coefficient (Ct), is required. Unlike power curves, Ct curves for the whole range of operating wind speeds of a WTG are still scarcely available in open sources. Typically, power and Ct curves are requested from a WTG manufacturer or wind farm owner under a non-disclosure agreement. However, in a research study or in calculations over a multitude of wind farms with a variety of wind turbine models, collecting this information from owners may be hardly possible. This study represents a simple method to define Ct curve statistically using power curve and general specifications of WTGs available in open sources. Preliminary results demonstrate reasonable correspondence between simulated and given data. The estimations are done in the context of aggregated wind power calculations based on reanalysis or forecast data, so that the uncertainty of wake wind speed caused by the uncertainty of predicted Ct is comparable, or do not exceed, the uncertainty of given wind speed. Although the method may not provide accurate fits at low wind speeds, it represents an essential alternative to using physical Computational Fluid Dynamics (CFD) models that are both more demanding to computer resources and require detailed information on the geometry of the rotor blades and physical properties of the rotor, which are even more unavailable in open sources than power curves.</p>

scholarly journals ESTUDO DA PRODUÇÃO DE ENERGIA EÓLICA A PARTIR DE MEDIÇÕES ANEMOMÉTRICAS DA ESTAÇÃO DE CRAÍBAS/AL E SIMULAÇÕES COM O MODELO WASP

2016 ◽  
Vol 38 ◽  
pp. 477
Author(s):  
Thays Paes de Oliveira ◽  
Rosiberto Salustiano da Silva Junior ◽  
Roberto Fernando Fonseca Lyra ◽  
Sandro Correia Holanda
Keyword(s):  
Power Generation ◽  
Probability Distribution ◽  
Wind Turbine ◽  
Wind Direction ◽  
Wind Farm ◽  
Scale Parameters ◽  
Wind Potential ◽  
Generation Capacity ◽  
Intensity Density ◽  
Different Levels

Wind energy is seen as one of the promising generation of electricity, as a source of cheap and renewable, is benefit to reduce the environmental impacts of the dam. Along with the hydroelectric networks, the energy produced by the wind will help to increase power generation capacity in the country. That from speed data and direction municipality Wind Craíbas in the corresponding period 2014 - 2015, estimated the wind potential of the region. The tool used in the treatment of the collected data was the Wasp, making simulations of three different levels of measurement, producing a fictitious wind farm with powerful wind turbine. With the model, WASP helps estimate the probability distribution of Weibull and scale parameters A and K. he predominant wind direction is southeast and the best wind power and intensity density levels took place in 70m and 100m high , with about 201 W / m² and 243 W / m² respectively. But when evalua ted the inclusion of fictitious wind farm, the best use happened at 100m tall with production around 73.039 GWh , which can be attributed this improvement to increased efficiency of the wind turbine used in the simulation.

Statistical power curve modeling to estimate wind turbine power output

2019 ◽  
pp. 0309524X1989167
Author(s):  
Bharti Dongre ◽  
Rajesh Kumar Pateriya
Keyword(s):  
Wind Turbine ◽  
Statistical Power ◽  
Polynomial Regression ◽  
Smoothing Splines ◽  
Optimal Number ◽  
Specific Power ◽  
Power Curve ◽  
Curve Modeling ◽  
A Site ◽  
Power Curves

In the wind industry, the power curve serves as a performance index of the wind turbine. The machine-specific power curves are not sufficient to measure the performance of wind turbines in different environmental and geographical conditions. The aim is to develop a site-specific power curve of the wind turbine to estimate its output power. In this article, statistical methods based on empirical power curves are implemented using various techniques such as polynomial regression, splines regression, and smoothing splines regression. In the case of splines regression, instead of randomly selecting knots, the optimal number of knots and their positions are identified using three approaches: particle swarm optimization, half-split, and clustering. The National Renewable Energy Laboratory datasets have been used to develop the models. Imperial investigations show that knot-selection strategies improve the performance of splines regression. However, the smoothing splines-based power curve model estimates more accurately compared with all others.

scholarly journals Evaluation of the Energy Performance of the Amougdoul Wind Farm, Morocco

2017 ◽  
Vol 7 (2) ◽  
pp. 692
Author(s):  
Asma Ezzaidi ◽  
Mustapha Elyaqouti ◽  
Lahoussine Bouhouch ◽  
Ahmed Ihlal
Keyword(s):  
Wind Turbine ◽  
Calculation Method ◽  
Wind Farm ◽  
Shape Parameter ◽  
Scale Parameter ◽  
Energy Performance ◽  
Energy Output ◽  
Power Calculation ◽  
Power Curve ◽  
Useful Power

This paper is concerned with the assessment of the the performance of the Amougdoul wind farm. We have determined the Weibull parameters; namely the scale parameter, <em>c</em> (m/s) and shape parameter, <em>k</em>. After that, we have estimated energy output by a wind turbine using two techniques: the useful power calculation method and the method based on the modeling of the power curve, which is respectively 134.5 kW and 194.19 KW corresponding to 27% and 39% of the available wind energy, which confirm that the conversion efficiency does not exceed 40%.

scholarly journals Multivariate Wind Turbine Power Curve Model Based on Data Clustering and Polynomial LASSO Regression

2021 ◽  
Vol 12 (1) ◽  
pp. 72
Author(s):  
Davide Astolfi ◽  
Ravi Pandit
Keyword(s):  
Wind Turbine ◽  
Data Clustering ◽  
Performance Monitoring ◽  
Power Curve ◽  
Lasso Regression ◽  
Data Set ◽  
Power Monitoring ◽  
Turbine Performance ◽  
Input Variables ◽  
Power Curves

Wind turbine performance monitoring is a complex task because of the non-stationary operation conditions and because the power has a multivariate dependence on the ambient conditions and working parameters. This motivates the research about the use of SCADA data for constructing reliable models applicable in wind turbine performance monitoring. The present work is devoted to multivariate wind turbine power curves, which can be conceived of as multiple input, single output models. The output is the power of the target wind turbine, and the input variables are the wind speed and additional covariates, which in this work are the blade pitch and rotor speed. The objective of this study is to contribute to the formulation of multivariate wind turbine power curve models, which conjugate precision and simplicity and are therefore appropriate for industrial applications. The non-linearity of the relation between the input variables and the output was taken into account through the simplification of a polynomial LASSO regression: the advantages of this are that the input variables selection is performed automatically. The k-means algorithm was employed for automatic multi-dimensional data clustering, and a separate sub-model was formulated for each cluster, whose total number was selected by analyzing the silhouette score. The proposed method was tested on the SCADA data of an industrial Vestas V52 wind turbine. It resulted that the most appropriate number of clusters was three, which fairly resembles the main features of the wind turbine control. As expected, the importance of the different input variables varied with the cluster. The achieved model validation error metrics are the following: the mean absolute percentage error was in the order of 7.2%, and the average difference of mean percentage errors on random subsets of the target data set was of the order of 0.001%. This indicates that the proposed model, despite its simplicity, can be reliably employed for wind turbine power monitoring and for evaluating accumulated performance changes due to aging and/or optimization.

scholarly journals Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique

2019 ◽  
Vol 9 (22) ◽  
pp. 4930 ◽  
Author(s):  
Shenglei Pei ◽  
Yifen Li
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Power Output ◽  
Parametric Models ◽  
Wind Data ◽  
Support Vector ◽  
Power Curve ◽  
Hybrid Strategy ◽  
Curve Modeling ◽  
Power Curves

A power curve of a wind turbine describes the nonlinear relationship between wind speed and the corresponding power output. It shows the generation performance of a wind turbine. It plays vital roles in wind power forecasting, wind energy potential estimation, wind turbine selection, and wind turbine condition monitoring. In this paper, a hybrid power curve modeling technique is proposed. First, fuzzy c-means clustering is employed to detect and remove outliers from the original wind data. Then, different extreme learning machines are trained with the processed data. The corresponding wind power forecasts can also be obtained with the trained models. Finally, support vector regression is used to take advantage of different forecasts from different models. The results show that (1) five-parameter logistic function is superior to the others among the parametric models; (2) generally, nonparametric power curve models perform better than parametric models; (3) the proposed hybrid model can generate more accurate power output estimations than the other compared models, thus resulting in better wind turbine power curves. Overall, the proposed hybrid strategy can also be applied in power curve modeling, and is an effective tool to get better wind turbine power curves, even when the collected wind data is corrupted by outliers.

scholarly journals Application of the Nacelle Transfer Function by a Nacelle-Mounted Light Detection and Ranging System to Wind Turbine Power Performance Measurement

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1087 ◽  
Author(s):  
Dongheon Shin ◽  
Kyungnam Ko
Keyword(s):  
Wind Speed ◽  
Transfer Function ◽  
Performance Measurement ◽  
Wind Turbine ◽  
Cumulative Distribution ◽  
Light Detection And Ranging ◽  
Power Curve ◽  
Power Performance ◽  
Light Detection ◽  
Power Curves

To examine the applicability of the nacelle transfer function (NTF) derived from nacelle light detection and ranging (LIDAR) measurements to wind turbine power performance testing without a met mast, wind turbine power performance measurement was carried out at the Dongbok wind farm on Jeju Island, South Korea. A nacelle LIDAR was mounted on the nacelle of a 2-MW wind turbine to measure wind conditions in front of the turbine rotor, and an 80-m-high met mast was installed near another wind turbine to measure the free-stream wind speed. The power measurement instruments were installed in the turbine tower base, and wind speeds measured by the nacelle anemometer of the turbine were collected by the SCADA (Supervisory control and data acquisition) system. The NTF was determined by the table method, and then the power curve drawn using the NTF by the nacelle LIDAR (PCNTF, NL) was compared with the power curves drawn in compliance with International Electrotechnical Commission (IEC) standards, 61400-12-1 and 61400-12-2. Next, the combined standard uncertainties of the power curves were calculated to clarify the magnitude of the components of the uncertainties. The uncertainties of annual energy production (AEP) were also estimated by assuming that wind speed is a Rayleigh cumulative distribution. As a result, the PCNTF, NL was in good agreement with the power curves drawn in accordance with the IEC standards. The combined standard uncertainty of PCNTF, NL was almost the same as that of the power curve based on IEC 61400-12-2.

scholarly journals Nacelle power curve measurement with spinner anemometer and uncertainty evaluation

2017 ◽  
Vol 2 (1) ◽  
pp. 97-114 ◽  
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.

scholarly journals Interannual variability of wind climates and wind turbine annual energy production

2018 ◽  
Vol 3 (2) ◽  
pp. 651-665 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Interannual Variability ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Curve ◽  
Wind Speeds ◽  
Eastern Usa

Abstract. The interannual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in preconstruction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derive in part from variability in wind climates. However, the magnitude of IAV in wind speeds at or close to wind turbine hub heights is poorly defined and may be overestimated by assuming annual mean wind speeds are Gaussian distributed with a standard deviation (σ) of 6 %, as is widely applied within the wind energy industry. There is a need for improved understanding of the long-term wind resource and the IAV therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub heights over the eastern USA indicate median gross capacity factors (computed using 10 min wind speeds close to wind turbine hub heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at or near typical wind turbine hub heights in these simulations and AEP computed using the power curve of the most commonly deployed wind turbine is lower than is implied by assuming σ=6 %. Indeed, rather than 9 out of 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP as implied by assuming a Gaussian distribution with σ of 6 %, the results presented herein indicate that in over 90 % of the area in the eastern USA that currently has operating wind turbines, simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, the IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to preconstruction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

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