Performance evaluation of a horizontal axis wind turbine in operation

The operation of modern horizontal axis wind turbine (HAWT) includes a number of important factors, such as wind power (P), power coefficient (C_P), axial flow induction factor (a), rotational speed (Ω), tip speed ratio (λ), and thrust force (T). The aerodynamic qualities of these aspects are evaluated and discussed in this study. For this aim, the measured data are obtained from the Sebenoba Wind Energy Power Plant (WEPP) that is located in the Sebenoba region in Hatay, Turkey, and a wind turbine with a capacity of 2 MW is selected for evaluation. According to the results obtained, the maximum turbine power output, maximum power coefficient, maximum axial flow induction factor, maximum thrust force, optimum rotational speed, probability density of optimum rotational speed, and optimum tip speed ratio are found to be 2 MW, 30%, 0.091, 140 kN, 16.11 rpm, 46.76%, and 7, respectively. This study has revealed that wind turbines must work under optimum conditions in order to extract as much energy as possible for approaching the ideal limit.     

A novel blade design for Savonius wind turbine based on polynomial bezier curves for aerodynamic performance enhancement

ABSTRACT

Advanced wind turbine designs and technologies have been evolved to take advantage of wind energy. Despite the significant progress already attained, the need for a dependable wind energy converter particularly devoted to small-scale applications remains a challenging issue. Due to its design simplicity, Savonius wind turbine is the most suitable candidate for such applications. It operates at low wind speed, with the necessary starting capacity and insensitivity to wind directions. Moreover, in the literature related to wind energy, the Savonius rotor is known for its low performance compared to other types of wind turbines. In this paper, we present a study into the utilization of Bézier curves and transient computational fluid dynamics (CFD) to optimize the conventional Savonius blade design. The k-ω SST turbulence model is employed to perform a series of CFD simulations in order to assess the power coefficient of each generated design. A validation of optimization results using the Taguchi method was carried out. The comparative analysis of the torque and power coefficients shows a significant increase in the power coefficient (C_p). The optimal C_p is 0.35 and is 29% higher than the conventional Savoniu wind turbine (SWT). Subsequently, the effectiveness of the innovative geometry is proved by improved pressure and velocity distributions around blades of novel design.     

The effects of sinusoidal leading edge of turbine blades on the power coefficient of horizontal-axis wind turbine (HAWT)

In order to improve the aerodynamic performance of horizontal-axis wind turbine (HAWT), a sinusoidal shape is applied to turbine blade. In this study, four types of modified blades were chosen based on variations in amplitude and wavelength of protuberance along the leading edge. Compared with the baseline model, the power coefficients (C_p) of HAWT with modified blades were improved, especially at low tip speed ratios. At low wind speed (V = 6 m/s), blades with short wavelength obtain significant improvement in C_p compared with the baseline model. As wind speed increases, this improvement decreases. In addition, turbine blade with large amplitude and long wavelength obtains better C_p values at higher wind speeds than lower ones, which have a great potential to be more superior at relatively higher wind speeds.     

An optimized airfoil geometry for vertical-axis wind turbine applications

ABSTRACT

In this work, a new airfoil shape optimized for vertical-axis wind turbine applications is proposed. Different airfoil shapes have been analyzed with JavaFoil, a panel method software. Then, the results from the analysis have been used to optimize the performance of the new airfoil shape. Afterward, Computational Fluid Dynamics (CFD) simulations of the proposed airfoil, UO-17-LDA, are run for different angles of attack to provide insight into the flow field and the mechanisms related to this increase in performance. The UO-17-LDA airfoil presents a high lift-to-drag ratio and a delayed stall angle with respect to the original FX-63-137 airfoil, making it suitable for vertical-axis wind turbine applications. This increase in performance has been verified by comparing two VAWT designs with the original and the proposed airfoil using a double-multiple streamtube model. Finally, the practicality of JavaFoil for the comparison of different airfoil geometries has been verified, as it is capable of obtaining results for a wide number of flow conditions in small computational times and with a user-friendly interface. Nevertheless, the results diverge from the actual solution for high angles of attack (beyond stall). Hence, the time and effort required to perform CFD simulations is justified to gain insight into the actual behavior of a particular airfoil, as well as to obtain a richer analysis of the flow field and the mechanisms related to the airfoil performance.     

Starting improvement of micro-wind turbines operating in low wind speed regions

The study deals with the design and optimization of external and internal geometry of micro-wind turbines blades. A specified objective function which consists of the power coefficient and the starting time was defined and the genetic algorithm optimization technique in conjunction with the blade-element momentum theory was adopted to find the geometry of the blades including the distributions of the chord, the twist angle and also the shell thickness. Moreover, the allowable stress of the blades was considered as a constraint to the objective function. Results show that a reasonable compromise is achievable such that the starting time of the blades reduces noticeably in return for a small drop in the power coefficient. The significant improvement of the hollow blades over the solid ones indicates that the power coefficient and the starting performance could be improved through the appropriate distributions of the considered decision variables, i.e. the chord, the twist angle and also the shell thickness.     

A numerical investigation of fluid-structure coupling of 3 MW wind turbine blades

In this paper, computational modeling is created for 3 MW wind turbine blade using the software Solidworks, and the computations are carried out using Workbench for the fluid–structure interaction of blades due to both the rated and the extreme wind load. The methods developed are applied to the simulation of the NREL Phase VI wind turbine blades, and validation against published data is presented. Moreover, the static structural analysis for wind turbine rotor is performed using Mechanical APDL. The results show that the maximum values of stress reach 458 MPa and 76.9 MPa due to the rated and extreme wind load, respectively, which are lower than 78.3 MPa, the flexural strength of Swancor 2511-A epoxy resin for blade material.     

Investigations on Blade Tip Tilting for Hawt Rotor Blades Using CFD

The main purpose of this paper is to study the aerodynamic effects of blade tip tilting on power production of horizontal-axis wind turbines by using Computational Fluid Dynamics (CFD). For validation and as a baseline rotor, the NREL Phase VI wind turbine rotor blade is used. The Reynolds-Averaged Navier–Stokes Equations are solved and different turbulence models including the Spalart–Allmaras, Standard k-?, k-? Yang–Shih and SST k–ω models are used and tested. The results are shown in terms of power generation at different wind speeds and the pressure distribution at different sections of the blade, and the comparisons are made with the available experimental data. For tip tilting analysis, 16 different geometries belonging to four different configurations are studied. The geometries are generated based on changing the twist and the cant angles of the winglet. The four different configurations are obtained from tilting the blade tip toward pressure side, suction side, leading edge, and trailing edge. The effect of the different configurations on the flow characteristics and hence on the power production of the wind turbine is investigated.     

Wind energy potential analysis for Thailand: Uncertainty from wind maps and sensitivity to turbine technology

Rapid development of wind energy has been witnessed in Thailand. However, different wind resource maps (over land) have brought great uncertainty to wind energy planning. Here, four important mesoscale wind maps were considered: DEDP (2001), World Bank (2001), Manomaiphiboon et al. (2010) of JGSEE, and DEDE (2010). The wind maps were first harmonized to a common grid at 100 m and then compared. The earlier wind maps (DEDP and World Bank) are shown to represent the lower and upper limits of predicted speed, respectively, while JGSEE and DEDE tend to be more moderate with predictions statistically closer to observations. A consolidated wind map was constructed based on their median and shown to have the best prediction performance. It was then used for the technical potential analysis, in which three large (2-MW) turbine models (two conventional and one designed for low wind speed) were considered. By GIS techniques, any land areas not feasible for large wind turbines were excluded, and the corresponding overall onshore technical potential ranges between 50 and 250 GW, depending on map and turbine model. Considering only economically feasible turbines (with capacity factors of 20%) and the median-based map, the final technical potential equals 17 GW when using the low-wind-speed model but is reduced to 5 GW with the conventional models, adequately meeting the national wind energy target of 3 GW by the year 2036. The results suggest a strong sensitivity of estimated technical potential to turbine technology and a suitability of low-wind-speed turbines for wind conditions in Thailand.     

Best location of the small wind turbine around a single high-rise building

This research is a three-dimensional investigation about the aerodynamic interaction between the wind flow and a single high-rise building. In order to find location(s) with high potential of velocity around the building, a wide variety of wind speeds ranging from 2 to 10 m/s is studied. On the other hand, a high-rise building with the ratio of height to width of H/W = 3 is considered. Computations are performed numerically by means of the finite volume approach. Several results are obtained in the present numerical study. For example, it is found that due to wind-structure vertical interaction, locations with enhanced velocities are developed on the building roof in which the rate of this enhancement increases with increasing the wind speed. In addition, over the building, “lines C and D” are realized as the best locations having high power potentials and low turbulence intensities. In addition, lateral wind-structure interaction revealed that for all wind speeds, location of L/W = 0.5 is the best for the small wind turbine installation.     

Nonlinear control of a wind turbine based on nonlinear estimation techniques for maximum power extraction

This work proposes nonlinear estimators with nonlinear controllers, for variable speed wind turbine (VSWT) considering that either the wind speed measurement is not available or not accurate. The main objective of this work is to maximize the energy capture from the wind and minimizes the transient load on the drive train. Controllers are designed to adjust the generated torque for maximum power output. Estimation of effective wind speed is required to achieve the above objectives. In this work the estimation of effective wind speed is done by using the Modified Newton Rapshon (MNR), Neural Network (NN) trained by different training algorithms and nonlinear time series based estimation. Initially the control strategies applied was the classical ATF (Aerodynamic torque feed forward) and ISC (Indirect speed control), however due their weak performance and unmodeled WT disturbances, nonlinear static and dynamic feedback linearization techniques with the above wind speed estimators are proposed.     

A stochastic approach for the operation of a wind and pumped storage plant under a deregulated environment

To improve the competiveness in the energy market, it is necessary that the wind power plants provide guaranteed power generation, although, it is not possible to forecast power availability from wind power plant accurately. This paper presents a stochastic model and solution technique for the combined operation of wind and pumped storage power plants to improve the power availability and increasing the profit considering uncertainties of wind power generation. In this model, uncertainties in wind data have been forecasted for grid connected day-ahead market using Weibull distribution model. The imbalances in the forecasted wind data and the market demand have been reduced by operating the pumped storage power plant. In this stochastic mixed integer problem, pumped storage plant can take the supply either from the grid or from the wind power plant for the pumping operation to store the energy in order to utilize this energy during peak hours for increasing the overall revenue. The reliability of the pumped storage is improved by replacing the conventional unit with the adjustable speed type pumped storage unit. In order to prove the optimality of the solution, two case studies were considered. In case study–I, scheduling is provided by operating the conventional pumped storage unit, whereas in case study–II, adjustable speed pumped storage unit has been used. It has been found that the adjustable speed pumped storage unit has further reduced the imbalance between generated power and demand. The complete approach has been formulated and implemented using AMPL software.     

Performance enhancement of a stand-alone induction generator-based wind energy system using neural network controller

ABSTRACT

The limitation of self-excited induction generator (SEIG) when used in the stand-alone wind energy system (WES) is poor voltage regulation at variable speed. The indirect vector control (IVC) technique is employed for both the generator-side converter (GSC) and load-side converter (LSC) to regulate the variation of SEIG speed, DC link voltage, and electromagnetic torque independently. Further performance of the proposed IVC technique has been analyzed independently with neural network controller (NNC) and fuzzy logic controller (FLC) as its components. The FLC is replaced by an NNC to improve the performance of the proposed system. IVC of SEIG-based WES has been simulated in MATLAB/SIMULINK software, and the prototype model of the proposed WES is developed to experimentally validate the performance using dSPACE DS-1104 R&D controller board.     

Availability estimation of a multi-state wind farm in fuzzy environment

Wind is one of the fastest growing renewable energy resources in the electric power system. Availability of wind energy is volatile in nature due to the stochastic behavior of wind speed and non-linear variation of the wind power curve of wind turbine generator. Because of this impression and uncertainty, the availability estimation of wind power has become a challenging issue. In this paper, Markov Fuzzy Reward technique has been proposed for finding out the reliability of wind farm by assessing the availability of wind power. According to this technique, availability of the wind power has been estimated considering wind farm and demand both as a multi-state system. In addition to the availability, different reliability indices such as the number of absolute failures, mean time to deficiency, and probability of failures of a wind farm have been assessed in a time horizon, which can provide useful information for the power system planner at wind farm installing stage. A comparison of this study reveals the efficacy of the proposed Markov Fuzzy Reward approach over the conventional Markov Reward approach.     

Performance improvement of airfoils for wind blade with the groove

This study was a basic one to explore how much the aerodynamic characteristics of wind blade improve. The extent of improvement according to the shapes of groove placed on the surface of airfoil (NACA0015) was analyzed through computational analysis. A commercial computational fluid dynamics (CFD) code, the ANSYS Fluent 13, was used in this study. In this study, regarding with the positions and shapes of groove, the end of groove was placed at a certain distance (length, l) from both the front and back of separation starting point, the depth and the width were designated as h and d respectively. Analysis was conducted at the 7° angle of attack under the following conditions; the thickness (δ) of boundary layer to the depth (h) of groove ratio (h/δ) 0.6–1.0, the depth (h) of groove to the width (d) of groove ratio (h/d) 0.1–1.4, and the length (l) between the end of groove and separation point to the thickness (δ) of boundary layer ratio (l/δ) ?0.5–0.5. Among these conditions, the best improvement of lift to drag ratio, standing at 15.3%, was under h/δ = 1.0, h/d = 0.12, and l/δ = –0.5 (7° AOA, Re = 360k). In addition, throughout the range of angle of attack, 2–14°, lift to drag ratio improved by 0.8%, 5.1%, 3.2%, and 1.8% each when Reynolds numbers were 280k, 360k, 450k, and 530k. It is also confirmed that the shape of groove contributed to recovering velocity around airfoil wall and the lift to drag ratio improvements by groove were maintained at the given range of Reynolds number and around the angle of attack, 7°.     

A Weibull and finite mixture of the von Mises distribution for wind analysis in Mersing,Malaysia

Studies of wind direction receive less attention than that of wind speed; however, wind direction affects daily activities such as shipping, the use of bridges, and construction. This research aims to study the effect of wind direction on generating wind power. A finite mixture model of the von Mises distribution and Weibull distribution are used in this paper to represent wind direction and wind speed data, respectively, for Mersing (Malaysia). The suitability of the distribution is examined by the R² determination coefficient. The energy analysis, that is, wind power density, only involves the wind speed, but the wind direction is vital in measuring the dominant direction of wind so that the sensor could optimize wind capture. The result reveals that the estimated wind power density is between 18.2 and 25 W/m², and SSW is the most common wind direction for this data.     

Short-term wind speed forecasting: Application of linear and non-linear time series models

This study forecasts day-ahead wind speed at 15 minute intervals at the site of a wind turbine located in Maharashtra, India. Wind speed exhibits non-stationarity, seasonality and time-varying volatility clustering. Univariate linear and non-linear time series techniques namely MSARIMA, MSARIMA-GARCH and MSARIMA-EGARCH have been employed for forecasting wind speed using data span ranging from 3 days to 15 days. Study suggests that mean absolute percentage error (MAPE) values first decrease with the increase in data span, reaches its minima and then start increasing. All models provide superior forecasting performances with 5 days data span. It is further evident that ARIMA-GARCH model generates lowest MAPE with 5 days data span. All these models provide superior forecasts with respect to current industry practices. This study establishes that employing various linear and non-linear time series techniques for forecasting day-ahead wind speed can benefit the industry in terms of better operational management of wind turbines and better integration of wind energy into the power system, which have huge financial implications for wind power generators in India.     

Sensitivity of Landsat‐Scale Energy Balance to Aerodynamic Variability in Mountains and Complex Terrain

The speed and direction of air flow through complex terrain are difficult to define. Both impact sensible and latent heat flux exchanges at the surface. Evapotranspiration (ET) models such as Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC?) estimate ET as a residual of the surface energy process and are thus sensitive to aerodynamics, including terrain‐induced impacts on roughness governing convective heat transfer (H). There is a need to explore the sensitivities of H estimation and thereby ET estimation to wind speed and terrain roughness in mountainous areas and to determine the merit of operating complex mesoscale wind field models in conjunction with the energy balance process. A sensitivity analysis is explored in METRIC where we increased wind speed in proportion to a relative elevation parameter and we increased aerodynamic roughness to assimilate impacts of relative terrain roughness, estimated in proportion to standard deviation of elevation within a 3 km locality. These aerodynamic modifications increased convective heat transfer in complex terrain and reduced estimated ET. In other sensitivity runs, we reduced estimated wind speed on estimated leeward slopes. Estimated ET with and without these sensitivity adjustments is shown for mountainous areas of Montana and Nevada. Changes in ET ranged from little change (<5%) for lower slopes to about 30% reductions on windward slopes and 25% increases on leeward slopes for some mid to high elevations in the Montana application.     

A novel wind speed forecasting model for wind farms of Northwest China

Wind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon’s Signed-Rank test, and Morgan--Granger--Newbold test tell us that the proposed model is different from the compared models.     

Design and analysis of artificial bee-colony-based MPPT algorithm for DFIG-based wind energy conversion systems

In this article, the proposed maximum power point tracking (MPPT) method is designed by taking rotor speed as an optimization problem, which is solved by artificial bee colony (ABC) algorithm to generate the maximum power output. The main advantage of this algorithm is that its optimal solution is independent of the initial positions and requirement of lesser number of control parameters, which leads to simple and robust MPPT algorithm than other algorithm. Furthermore, the hill climb search and particle swarm optimization-based MPPT algorithm are also discussed and the results obtained by these are compared to verify the effectiveness of proposed algorithm. Simulations for MPPT control along with doubly fed induction-generator-based wind energy conversion system is carried out in MATLAB/Simulink environment. Three statistical methods are used to evaluate the accuracy of each MPPT algorithm. All results are analyzed and compared under randomly selected wind as well as real wind speed configuration. Comparison of both numerical and simulation results under two different varying wind speed conditions strongly suggest that the proposed ABC-based MPPT algorithm is superior than other two MPPT algorithms.     

A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China

Wind energy, one of the most promising renewable and clean energy sources, is becoming increasingly significant for sustainable energy development and environmental protection. Given the relationship between wind power and wind speed, precise prediction of wind speed for wind energy estimation and wind power generation is important. For proper and efficient evaluation of wind speed, a smooth transition periodic autoregressive (STPAR) model is developed to predict the six-hourly wind speeds. In addition, the Elman artificial neural network (EANN)-based error correction technique has also been integrated into the new STPAR model to improve model performance. To verify the developed approach, the six-hourly wind speed series during the period of 2000–2009 in the Hebei region of China is used for model construction and model testing. The proposed EANN-STPAR hybrid model has demonstrated its powerful forecasting capacity for wind speed series with complicated characteristics of linearity, seasonality and nonlinearity, which indicates that the proposed hybrid model is notably efficient and practical for wind speed forecasting, especially for the Hebei wind farms of China.