scholarly journals Assessment and Performance Evaluation of a Wind Turbine Power Output

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1992 ◽  
Author(s):  
Akintayo Abolude ◽  
Wen Zhou
Keyword(s):  
Time Series ◽  
Wind Turbine ◽  
Wind Power ◽  
Power Output ◽  
Estimation Error ◽  
Power Curve ◽  
Estimation Errors ◽  
Site Specific ◽  
Wind Speeds ◽  
And Performance

Estimation errors have constantly been a technology bother for wind power management, often time with deviations of actual power curve (APC) from the turbine power curve (TPC). Power output dispersion for an operational 800 kW turbine was analyzed using three averaging tine steps of 1-min, 5-min, and 15-min. The error between the APC and TPC in kWh was about 25% on average, irrespective of the time of the day, although higher magnitudes of error were observed during low wind speeds and poor wind conditions. The 15-min averaged time series proved more suitable for grid management and energy load scheduling, but the error margin was still a major concern. An effective power curve (EPC) based on the polynomial parametric wind turbine power curve modeling technique was calibrated using turbine and site-specific performance data. The EPC reduced estimation error to about 3% in the aforementioned time series during very good wind conditions. By integrating statistical wind speed forecasting methods and site-specific EPCs, wind power forecasting and management can be significantly improved without compromising grid stability.

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.

Gaussian mixture models for site-specific wind turbine power curves

2020 ◽  
pp. 095765092093172
Author(s):  
Bruno Srbinovski ◽  
Andriy Temko ◽  
Paul Leahy ◽  
Vikram Pakrashi ◽  
Emanuel Popovici
Keyword(s):  
Wind Turbine ◽  
Power Output ◽  
Learning Algorithm ◽  
Probabilistic Method ◽  
Wind Farms ◽  
Gaussian Mixture ◽  
Model Parameters ◽  
Power Curve ◽  
Site Specific ◽  
Power Curves

A probabilistic method for modelling empirical site-specific wind turbine power curves is proposed in this paper. The method is based on the Gaussian mixture model machine learning algorithm. Unlike standard wind turbine power curve models, it has a user-selectable number (N) and type of input features. The user can thus develop and test models with a combination of measured, derived or predicted input features relevant to wind turbine power-output performance. The proposed modelling approach is independent of the site location where the measurable input features (i.e. wind speed, wind direction, air density) are collected. However, the specific models are location and turbine dependent. An N-feature wind turbine power curve model developed with the proposed method allows us to accurately estimate or forecast the power output of a wind turbine for site-specific field conditions. All model parameters are selected using a k-fold cross-validation method. In this study, five models with different numbers and types of input features are tested for two different wind farms located in Ireland. The power forecast accuracy of the proposed models is compared against each other and with two benchmarks, parametric wind turbine power curve models. The most accurate models for each of the sites are identified.

EXPERIMENTAL AND ANALYTICAL STUDIES OF VERTICAL AXIS WIND TURBINES

2018 ◽  
pp. 51-58
Author(s):  
B. P. Khozyainov
Keyword(s):  
Wind Turbine ◽  
Flow Velocity ◽  
Wind Power ◽  
Wind Turbines ◽  
Power Efficiency ◽  
Air Flow ◽  
Geometrical Parameters ◽  
Wind Speeds ◽  
Analytical Studies ◽  
Air Flow Velocity

The article carries out the experimental and analytical studies of three-blade wind power installation and gives the technique for measurements of angular rate of wind turbine rotation depending on the wind speeds, the rotating moment and its power. We have made the comparison of the calculation results according to the formulas offered with the indicators of the wind turbine tests executed in natural conditions. The tests were carried out at wind speeds from 0.709 m/s to 6.427 m/s. The wind power efficiency (WPE) for ideal traditional installation is known to be 0.45. According to the analytical calculations, wind power efficiency of the wind turbine with 3-bladed and 6 wind guide screens at wind speedsfrom 0.709 to 6.427 is equal to 0.317, and in the range of speed from 0.709 to 4.5 m/s – 0.351, but the experimental coefficient is much higher. The analysis of WPE variations shows that the work with the wind guide screens at insignificant average air flow velocity during the set period of time appears to be more effective, than the work without them. If the air flow velocity increases, the wind power efficiency gradually decreases. Such a good fit between experimental data and analytical calculations is confirmed by comparison of F-test design criterion with its tabular values. In the design of wind turbines, it allows determining the wind turbine power, setting the geometrical parameters and mass of all details for their efficient performance.

Hub height optimisation of commercial WTGs based on accurate wind resource analysis

2021 ◽  
pp. 0309524X2110227
Author(s):  
Kyle O Roberts ◽  
Nawaz Mahomed
Keyword(s):  
Power Generation ◽  
Wind Turbine ◽  
Wind Power ◽  
Capacity Factor ◽  
Resource Analysis ◽  
Wind Speeds ◽  
Unique Maximum ◽  
Wind Measurements ◽  
Generation Capacity ◽  
Wind Resource

Wind turbine selection and optimal hub height positioning are crucial elements of wind power projects. However, in higher class wind speeds especially, over-exposure of wind turbines can lead to a reduction in power generation capacity. In this study, wind measurements from a met mast were validated according to specifications issued by IRENA and NREL. As a first step, it is shown that commercial WTGs from a database may be matched to the wind class and turbulence intensity. Secondly, a wind turbine selection algorithm, based on maximisation of capacity factor, was implemented across the range of WTGs. The selected WTGs were further exposed to an iterative algorithm using pointwise air density and wind shear coefficients. It is shown that a unique maximum capacity factor, and hence wind power generation, exists for a wind turbine, premised on its eventual over-exposure to the wind resource above a certain hub height.

scholarly journals Enhancing Wind Turbine Power Forecast via Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 261
Author(s):  
Tianyang Liu ◽  
Zunkai Huang ◽  
Li Tian ◽  
Yongxin Zhu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Rapid Development ◽  
Superior Performance ◽  
Learning Platform ◽  
Novel Approach ◽  
Wind Power Forecast ◽  
Transformation Methods

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

Effect of Air Density on Output Power of Wind Turbine

2013 ◽  
Vol 336-338 ◽  
pp. 1114-1117 ◽  
Author(s):  
Ying Zhi Liu ◽  
Wen Xia Liu
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Output Power ◽  
Power Output ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Curve ◽  
Air Density ◽  
Wind Farm Design ◽  
Farm Design

This paper elaborates the effect of wind speed on the output power of the wind farms at different locations. It also describes the correction of the power curve and shows the comparison chart of the standard power curve and the power curve after correction. In China's inland areas, wind farms altitude are generally higher, the air density is much different from the standard air density. The effect of air density on wind power output must be considered during the wind farm design.

A Probabilistic Model for Simulating Long-Term Wind-Power Output

2003 ◽  
Vol 27 (3) ◽  
pp. 167-181 ◽  
Author(s):  
Scott Kennedy ◽  
Peter Rogers
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Power ◽  
Power Output ◽  
Energy Resource ◽  
Autoregressive Process ◽  
Resource Planning ◽  
Offshore Wind ◽  
Arbitrary Length

This paper describes a chronological wind-plant simulation model for use in long-term energy resource planning. The model generates wind-power time series of arbitrary length that accurately reproduce short-term (hourly) to long-term (yearly) statistical behaviour. The modelling objective and methodology differ from forecasting models, which focus on minimizing prediction error. In the present analysis, periodic cycles are isolated from historical wind-speed data from a known local site and combined with a first-order autoregressive process to produce a wind-speed time series model. Corrections for negative wind-speed values and spatial smoothing for geographically disperse wind turbines are discussed. The resulting model is used to simulate the output from a hypothetical offshore wind-plant south of Long Island, New York. Modelled differences of power output between individual turbines result from wind speed variability; wake effects are not considered in this analysis.

scholarly journals The problem of the second wind turbine – a note on a common but flawed wind power estimation method

2010 ◽  
Vol 1 (1) ◽  
pp. 103-114 ◽  
Author(s):  
F. Gans ◽  
L. M. Miller ◽  
A. Kleidon
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Energy Demand ◽  
Estimation Method ◽  
Renewable Resource ◽  
Global Scale ◽  
Experimental Setup ◽  
Conservation Of Energy ◽  
Wind Speeds ◽  
The Common

Abstract. Several recent wind power estimates suggest how this renewable resource can meet all of the current and future global energy demand with little impact on the atmosphere. These estimates are calculated using observed wind speeds in combination with specifications of wind turbine size and density to quantify the extractable wind power. Here we show that this common methodology is flawed because it does not account for energy removal by the turbines that is necessary to ensure the conservation of energy. We will first illustrate the common but flawed methodology using parameters from a recent global quantification of wind power in a simple experimental setup. For a small number of turbines at small scales, the conservation of energy hardly results in a difference when compared to the common method. However, when applied at large to global scales, the ability of radiative gradients to generate a finite amount of kinetic energy needs to be taken into account. Using the same experimental setup, we use the simplest method to ensure the conservation of energy to show a non-negligble decrease in wind velocity after the first turbine that will successively result in lower extraction of the downwind turbines. We then show how the conservation of energy inevitably results in substantially lower estimates of wind power at the global scale. Because conservation of energy is fundamental, we conclude that ultimately environmental constraints set the upper limit for wind power availability at the larger scale rather than detailed engineering specifications of the wind turbine design and placement.

scholarly journals A method to analyze power output of vertical-axis wind turbines under rain

2018 ◽  
Vol 56 (6) ◽  
pp. 761
Author(s):  
Duc Huu Nguyen
Keyword(s):  
Wind Turbine ◽  
Power Output ◽  
Vertical Axis ◽  
Power Production ◽  
Vertical Axis Wind Turbine ◽  
Physical Processes ◽  
Vertical Axis Wind Turbines ◽  
Rain Drops ◽  
Weather Changes ◽  
And Performance

A method to analyze effect power output of a vertical axis wind turbine under rain is proposed. The rain had the effect of increasing the drag, slowing the rotational speed of the wind turbine and decreasing the power and performance. More and more ambitious projects for wind turbine production being set on many where on Vietnam, it is necessary to understand all the factors, especially by weather changes, that might affect wind power production. In this research, we lay out a model to estimate the effect of rainfall by simulating the actual physical processes of the rain drops forming on the surface of the blades of a vertical-axis wind turbine (VAWT), thereby determining optimal wetness, then power and performance respectively. This could have an effect on the control strategy necessary for designing and controlling wind turbine.

Comparison of Various Blade Profiles in a Two-Blade Conventional Savonius Wind Turbine

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Rahim Hassanzadeh ◽  
Milad Mohammadnejad ◽  
Sajad Mostafavi
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Power Output ◽  
Urban Areas ◽  
Cost Effective ◽  
Maximum Power ◽  
Power Coefficient ◽  
Total Power ◽  
Wind Speeds ◽  
Savonius Wind Turbine

Abstract Savonius turbines are one of the old and cost-effective turbines which extract the wind energy by the drag force. Nowadays, they use in urban areas to generate electricity due to their simple structure, ease of maintenance, and acceptable power output under a low wind speed. However, their efficiency is low and the improvement of their performance is necessary to increase the total power output. This paper compares four various blade profiles in a two-blade conventional Savonius wind turbine. The ratios of blade diameter to the blade depth of s/d = 0.3, 0.5, 0.7, and 1 are tested under different free-wind speeds of 3, 5, and 7 m/s and tip speed ratios (TSRs) in the range from 0.2 to 1.2. It is found that the profile of blades in a Savonius rotor plays a considerable role in power characteristics. Also, regardless of blades profile and free-wind speed, the maximum power coefficient develops in TSR = 0.8. In addition, increasing the free-wind speed enhances the rotor performance of all cases under consideration. Finally, it is revealed that the rotor with s/d = 0.5 provides maximum power coefficients in all free-wind speeds and TSR values among the rotors under consideration, whereas the rotor with s/d = 1 is the worth cases.

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