Wind Energy Applications Research Articles

Electrical Launch Catapult and Landing Decelerator for Fixed-Wing Airborne Wind Energy Systems

This paper presents a (pre)feasibility study of the rail-based ultra-short launch and landing system ElektRail for fixed-wing airborne wind energy systems, such as Ampyx Power. The ElektRail concept promises airborne mass reductions through the elimination of landing gear as well as decreased landing stresses and ground stability requirements, opening possibilities for improved aerodynamics through a single fuselage configuration. Initially designed for operating fixed-wing drones from open fields, the ElektRail concept had to be significantly shortened for application in an airborne wind energy (AWE) context. This shorter size is required due to the much more limited space available at AWE sites, especially on offshore platforms. Hence, a performance enhancement using the integration of a bungee launching and landing system (BLLS) was designed and a system dynamics model for the launch and landing was derived. The results demonstrated the possibility for the ElektRail to be shortened from 140 m to just 19.3 m for use with an optimised tethered aircraft with a mass of 317 kg. A system length below 20 m indicates that an enhanced ElektRail launch and landing concept could be viable for airborne wind energy operations, even with relatively low-tech bungee cord boosters. Linear motor drives with a long stator linear motor actuator could potentially shorten the system length further to just 15 m, as well as provide better control dynamics. An investigation into improved AWE net power outputs due to reduced airborne mass and aerodynamic improvements remains to be conducted.

Micro-plasma actuator mechanisms in interaction with fluid flow for wind energy applications: operational parameters

Micro-plasma actuator mechanisms in interaction with fluid flow for wind energy applications: operational parameters

Review on Deep Learning Research and Applications in Wind and Wave Energy

Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification.

Effect of Chordwise Struts and Misaligned Flow on the Aerodynamic Performance of a Leading-Edge Inflatable Wing

Leading-edge inflatable (LEI) kites use a pressurized tubular frame to structurally support a single skin membrane canopy. The presence of the tubes on the pressure side of the wing leads to characteristic flow phenomena for this type of kite. In this paper, we present steady-state Reynolds-Averaged Navier-Stokes (RANS) simulations for a LEI wing for airborne wind energy applications. Expanding on previous work where only the leading-edge tube was considered, eight additional strut tubes that support the wing canopy are now included. The shape of the wing is considered to be constant. The influence of the strut tubes on the aerodynamic performance of the wing and the local flow field is assessed, considering flow configurations with and without side-slip. The simulations show that the aerodynamic performance of the wing decreases with increasing side-slip component of the inflow. On the other hand, the chordwise struts have little influence on the integral lift and drag of the wing, irrespective of the side-slip component. The overall flow characteristics are in good agreement with previous studies. In particular, it is confirmed that at a low Reynolds number of Re=105, a laminar separation bubble exists on the suction side of this hypothetical rigid wing shape with perfectly smooth surface. The destruction of this bubble at low angles of attack impacts negatively on the aerodynamic performance.

On the measurement of stability parameter over complex mountainous terrain

Abstract. Atmospheric stability has a significant effect on wind shear and turbulence intensity, and these variables, in turn, have a direct impact on wind power production and loads on wind turbines. It is therefore important to know how to characterise atmospheric stability in order to make better energy yield estimation in a wind farm. Based on the research-grade meteorological mast at Alaiz (CENER's test site in Navarre, Spain) named MP5, this work compares and evaluates different instrument set-ups and methodologies for stability characterisation, namely the Obukhov parameter, measured with a sonic anemometer, and the bulk Richardson number based on two temperature and one wind speed measurement. The methods are examined considering their theoretical background, implementation complexity, instrumentation requirements, and practical use in connection to wind energy applications. The sonic method provides a more precise local measurement of stability while the bulk Richardson is a simpler, robust and cost-effective technique to implement in wind assessment campaigns. Using the sonic method as a benchmark, it is shown that to obtain reliable bulk Richardson measurements in onshore sites it is necessary to install one of the temperature sensors close to the ground where the temperature gradient is stronger.

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

In this paper, two metaheuristic methods, genetic algorithm and particle swarm optimization, are proposed to determine the optimal locations, sizes, and power factors of single and double distributed generation units. In line with the 2050 carbon neutral goal, the aim was to integrate renewable distributed energy sources such as photovoltaic panels and wind turbines into the distribution system with a high penetration level. In contrast to most studies based on constant loads and dispatchable generations, an application considering the seasonal uncertainties of generation and consumption was performed to minimize the annual energy losses and voltage deviations of the distribution network. In addition, dispatchable, controllable and fuel-based conventional resources were allocated to compare the contributions of renewable resources. These seasonal case studies with various constraints were applied to IEEE 33-bus radial distribution network. To verify the feasibility and robustness of the proposed algorithms, case studies for peak loads were created and compared with the literature studies. While all distributed generation sources were operated at both unity and optimum power factor in all case studies, zero power factor and leading power factor scenarios were examined for a peak load only. Photovoltaic applications without energy storage technologies have not been efficient because of the uneven daily distribution of solar irradiance, especially insufficient irradiation in the evening and excessive irradiation at noon. However, wind energy applications are more reliable and feasible, because the wind speed distribution is relatively more uniform than that of solar irradiation, both seasonally and daily. In all cases, operating distributed generation sources at the optimal power factor provided better results than those operating at unity power factor. Consequently, wind turbines operating at optimal power factors have been found to contribute better than photovoltaic systems, and are almost as good as conventional sources with controllable power output. While both proposed algorithms yielded better results than those in the literature, particle swarm optimization was better than genetic algorithm in terms of providing the best solution, faster convergence, and shorter running time.

Machine learning-based simplified methods using shorter wind measuring masts for the time ahead wind forecasting at higher altitude for wind energy applications

Since wind is a fluctuating resource, the integration of wind energy into the electricity grid necessitates precise wind speed forecasting to maintain grid stability and power quality. Machine learning models built on different algorithms are widely used for wind forecasting. This requires a vast quantity of past wind speed data collected at the hub levels of the wind electric machines employed. Tall met masts pose a variety of practical issues in terms of installation and long-term maintenance, which will grow more challenging as next-generation wind turbines come with larger capacities and higher hub heights. In this paper, we propose four non-conventional methods for the time ahead forecasting of wind speed at a higher height by utilizing the wind speed data collected with relatively shorter wind measuring masts. We employ machine learning-based models and rely on the principle of interrelation between wind speeds at different altitudes in our investigations. Wind speed forecasts generated by the new methods at an altitude of 80 m above the ground level using wind speed data measured at lower altitudes of 50 m and 20 m are of industrially acceptable accuracy. The simplified physical requirements such methods demand far outweigh the marginal fall in prediction accuracy observed with these methods.

Real Time Simulation of Brushless Doubly Fed Reluctance Generator Driven Wind Turbine Considering Iron Saturation

Brushless doubly fed reluctance generator (BDFRG) provides a good substitute for the doubly fed induction generator for the wind energy applications due to the nonexistence of the slip rings. Real time modelling of the BDFRG is a challenge due to the high leakage inductance of the stator windings. While the magnetic saturation of the iron parts affects the inductances values, therefore studying the effect of magnetic saturation on the BDFRG performance is important. In this paper, a BDFRG model including iron saturation is built. The finite element (FE) analysis using FEMM software is used to generate the relationship between the generator currents and the inductances, the generated data are recorded in a lookup table. A Simulink model of the BDFRG driven wind turbine, with two cases of constant and variable inductances, are developed and verified with FE analyses at range of wind speeds above and below the synchronous speed. The lookup table proposes is verified using finite element analysis at different modes of operation of wind speed and compared with the constant inductance model to show the importance of saturation inclusion in the generator model for accurate generator modelling representation.

Assessment of the Wind Energy Potential of Two Burundian Sites

1-year hourly wind speed data from two Burundian stations, namely Bujumbura and Muyinga, have been processed in this work to bring an efficient help for the planning and installation of wind energy conversion systems (WECS) at those localities. Mean seasonal and diurnal variations of wind direction and wind shear exponent have been derived. Two-parameter Weibull probability density functions (PDFs) fitting the observed monthly and annual wind speed relative frequency distributions have been implemented. As shown through three complementary statistical tests, the fitting technique was very satisfactory. A wind resource analysis at 10 m above ground level (AGL) has led to a mean power density at Bujumbura which is almost thirteen fold higher than at Muyinga. The use of the empirical power law to extrapolate wind characteristics at heights from 150 to 350 m AGL has shown that energy potential of hilltops around Muyinga was only suitable for small, individual scale wind energy applications. At the opposite, wind energy potential of ridge-tops and hilltops around Bujumbura has been found suitable for medium and large scale electricity production. For that locality and at those heights, energy outputs and capacity factors (CF or Cf) have been computed for ten selected wind turbines (WTs), together with costs of electricity (COE) using the present value of cost (PVC) method. Amongst those WTs, YDF-1500-87 and S95-2.1 MW have emerged as the best options for installation owing to their highest CF and lowest COE. Moreover, an analysis of those two quantities at monthly basis for YDF-1500-87 WT has led to its best performance in the dry season. Compared to the average present COE of household hydroelectricity consumption, results of this study have evidenced economical feasibility and benefit of WECS setting in selected Burundian sites in order to supplement traditional electricity sources.

Evaluating Wind Speed and Power Forecasts for Wind Energy Applications Using an Open-Source and Systematic Validation Framework

Evaluating Wind Speed and Power Forecasts for Wind Energy Applications Using an Open-Source and Systematic Validation Framework

Three-level Vienna Rectifier with a Brushless and Permanent Magnetless Generator for Wind Energy Conversion Systems

Abstract This paper proposes a system design and control technique for a newly developed brushless and permanent magnetless synchronous generator-based variable-speed wind energy generation system, transferring power to a constant voltage dc grid via a three-level Vienna rectifier (VR). The recently established generator named Brushless Induction excited Synchronous Generator (BINSYG) is a wound field synchronous generator (WFSG), whose excitation is developed by controlling an Induction Machine fitted to the same machine structure and sharing the same magnetic core. A new controller is proposed that ensures the stable operation of BINSYG for a wide variation of shaft speeds. VR achieves sinusoidal input current and can control the power factor at its input, which is particularly suitable for wind energy applications. The top and bottom capacitor voltages of the VR are balanced using redundant switching combinations. The system with its proposed control algorithm is modelled in MATLAB/Simulink for a 5 kW rated BINSYG feeding power to a 750 V dc grid. The steady-state and dynamic state simulation results are presented and the controller performance is verified for a wide range of wind speeds. Further, real-time results using the OPAL-RT testbed are presented for the same system to verify the effectiveness of the overall control strategy.

Global optimisation approach for designing high-efficiency piezoelectric beam-based energy harvesting devices

The paper proposes a novel methodology for developing high-power energy harvesting gravity-based devices using an array of piezoelectric beams for wind energy applications. The methodology incorporates a global multidimensional constrained optimisation algorithm, which accounts for the physical size of the device, the physical, geometrical and electrical properties of the piezoelectric beams, and the power management circuit to increase the device’s efficiency. As the beams are plucked sequentially, they vibrate out-of-phase, which consequently leads to charge cancellation issues. The paper proposes and incorporates an electrical circuit design to avoid such problems, being able to further increase the efficiency of the device by 35% when compared against the output from the standard energy harvesting (SEH) circuit with independent rectifiers. The proposed optimisation methodology is applied to the devices utilising flexible polyvinylidene fluoride beams. The developed dynamic numerical model of the beams’ vibration is validated using the experimental results and the results of a Finite Element Analysis. To study the electro-mechanical coupling of the beams, an electric circuit and the power management circuit are created and modelled in Matlab/Simulink software. The optimised device delivers 6 to 17 times higher energy output compared to the unoptimised device. The performance of this device was also compared to that of the device with much stiffer LiNbO3 beams (Clementi et al., 2021, [1]) to demonstrate the direct applicability of such devices to power sensors and transmitter units for structural monitoring of wind turbine blades. It has been demonstrated that the LiNbO3-beam device yields an energy output with one order of magnitude higher. The applied optimisation methodology enabled a 0.057 × 0.017 × 2 m3 dimension device to produce a power output in the range from 0.5 to 1 W depending on the blades’ speed, resulting in 1.06 mW/cm3 power density of the device.

Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Mountains, Ecuador

Wind speed forecasting systems over complex terrain at high altitude are very complex and conventional forecasting systems are unable to be applied due to wind variability. This study proposes an approach developed specifically for this study with application of linear regression models as baseline, and Recurrent Neural Networks (RNN): Long Short Term Memory (LSTM) network, and Dynamic Neural Networks (DNN): Nonlinear Autoregressive Exogenous (NARX) network to perform accurate wind speed forecasting in complex terrain in the Ecuadorian Andes to identify feasible places for wind energy applications. This work starts with the installation of two meteorological stations within of the mountainous zone of study to collect measured variables during 2018. Later on, the measured variables were evaluated by using statistical tools and Pearson Correlation Coefficient (PCC) to determine the input variables. Finally, the proposed forecasting models were trained, validated, and tested by using measured data. The DNN and RNN models were compared to determine the best performance through the statistical error forecast measurements as follows, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and correlation coefficient. The comparison results of the proposed models indicated that the most precise values were for multivariable LSTM network, suggesting this model as a powerful approach to forecast wind speed over complex terrain, demonstrating the importance by using measured variables. Furthermore, the forecasted wind speed showed high values which are suitable for high wind power generation.

Wind speed reconstruction from mono-static wind lidar eliminating the effect of turbulence

Light detection and ranging (lidar) is an attractive technique to measure wind speed via the Doppler effect, thanks to its flexibility. Although stand-alone deployment is growing, current standards for wind energy applications often require wind lidars to be verified against an in situ met mast with cup anemometers, which deliver point-like measurements. Analyzing 38 lidar-to-cup verification campaigns at eight accredited sites, we investigate the difference between these techniques during turbulence influenced atmospheric conditions. We demonstrate that strong turbulence results in the lidar 10-min scalar average wind speed overestimate the cup value, whereas the lidar vector average results in an underestimate. Although scalar and vector averaging provide acceptable measurements, we show that a linear combination, with appropriate weighting, reduces the lidar turbulence sensitivity by more than one order of magnitude. The resulting overall lidar-to-cup bias is within the one-standard-deviation comparison uncertainty at all sites. The resulting ensemble mean bias is [Formula: see text], where the uncertainty stems from variations between sites. We explain the experimental observations by a model derived from first principles. The developed hybrid averaging is implemented in the Leosphere WindCube v2.1 wind lidar.

Breeze-driven triboelectric nanogenerator for wind energy harvesting and application in smart agriculture

Smart agriculture is becoming an inevitable trend with the wide application of sensor networks. To supply energy for agricultural sensors, the wind energy harvester supports a possible solution. However, the average wind speed on the earth surface is only 3.28 m/s, which cannot easily be harvested by traditional generators efficiently. To efficiently harvest breeze energy in the farmland environment, a breeze-driven triboelectric nanogenerator (BD-TENG) was proposed. By selecting lightweight rotor materials and designing suitable wind scoops structures, the start-up wind speed of BD-TENG is as low as 3.3 m/s, and when the wind speed is 4 m/s, the energy conversion efficiency of the BD-TENG can reach 12.06%. Moreover, under 4 m/s wind speed, the output performance of the BD-TENG is 330 V, 7 μA, 137 nC, and the peak power is 2.81 mW. So, the BD-TENG is easier to operate normally even in low wind speed environments and can harvest natural breeze energy efficiently. Experiments prove that in natural environments, the BD-TENG successfully lights up 300 red and blue light-emitting diodes in series, which can be applied to increase lighting time for plants at night. Moreover, the BD-TENG can power a soil thermometer by harvesting natural breeze energy. Therefore, the BD-TENG can be widely used in farmland environments to provide energy for agricultural sensor networks. The BD-TENG has bright prospects in smart agriculture and can promote its sustainable development.

Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks

Renewable energy is widely applied in the world. The key problem of wind energy application is to improve the reliability of wind turbine and reduce its downtime. Supervisory control and data acquisition (SCADA) has created reliable and cost-effective status data for health conditioning of wind turbine operation. Effectively extracting useful information from SCADA is critical to the reliability of applied wind energy. In this paper, a new method is proposed to extract multidirectional spatio-temporal features of SCADA data for wind turbine condition monitoring based on convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) with attention mechanism. Firstly, the quartile method is developed to distribute the SCADA data for cleaning and deleting the abnormal data so as to improve the data validity. Then, the input variables are selected through Pearson correlation coefficient, and they are transformed into high-dimensional features by using CNN. These features are input into BiGRU network through attention mechanism layer. Attention mechanism strengthens the impact of important information to improve learning accuracy. In the end, it is verified that the proposed method can detect early abnormal operation and identify failed components of wind turbine by real case analysis from wind farm. The proposed method presents better feasibility of practical wind energy application, which can promote the application of clean energy.

The COTUR project: remote sensing of offshore turbulence for wind energy application

Abstract. The paper presents the measurement strategy and data set collected during the COTUR (COherence of TURbulence with lidars) campaign. This field experiment took place from February 2019 to April 2020 on the southwestern coast of Norway. The coherence quantifies the spatial correlation of eddies and is little known in the marine atmospheric boundary layer. The study was motivated by the need to better characterize the lateral coherence, which partly governs the dynamic wind load on multi-megawatt offshore wind turbines. During the COTUR campaign, the coherence was studied using land-based remote sensing technology. The instrument setup consisted of three long-range scanning Doppler wind lidars, one Doppler wind lidar profiler and one passive microwave radiometer. Both the WindScanner software and LidarPlanner software were used jointly to simultaneously orient the three scanner heads into the mean wind direction, which was provided by the lidar wind profiler. The radiometer instrument complemented these measurements by providing temperature and humidity profiles in the atmospheric boundary layer. The scanning beams were pointed slightly upwards to record turbulence characteristics both within and above the surface layer, providing further insight on the applicability of surface-layer scaling to model the turbulent wind load on offshore wind turbines. The preliminary results show limited variations of the lateral coherence with the scanning distance. A slight increase in the identified Davenport decay coefficient with the height is partly due to the limited pointing accuracy of the instruments. These results underline the importance of achieving pointing errors under 0.1∘ to study properly the lateral coherence of turbulence at scanning distances of several kilometres.

Review of Wind Turbine Icing Modelling Approaches

When operating in cold climates, wind turbines are vulnerable to ice accretion. The main impact of icing on wind turbines is the power losses due to geometric deformation of the iced airfoils of the blades. Significant energy losses during the wind farm lifetime must be estimated and mitigated. Finding solutions for icing calls on several areas of knowledge. Modelling and simulation as an alternative to experimental tests are primary techniques used to account for ice accretion because of their low cost and effectiveness. Several studies have been conducted to replicate ice growth on wind turbine blades using Computational Fluid Dynamics (CFD) during the last decade. While inflight icing research is well developed and well documented, wind turbine icing is still in development and has its peculiarities. This paper surveys and discusses the models, approaches and methods used in ice accretion modelling in view of their application in wind energy while summarizing the recent research findings in Surface Roughness modelling and Droplets Trajectory modelling. An An additional section discusses research on the modelling of electro-thermal icing protection systems. This paper aims to guide researchers in wind engineering to the appropriate approaches, references and tools needed to conduct reliable icing modelling for wind turbines.

High Spatial and Temporal Resolution Bistatic Wind Lidar

The high-resolution bistatic lidar developed at the Physikalisch-Technische Bundesanstalt (PTB) aims to overcome the limitations of conventional monostatic lidar technology, which is widely used for wind velocity measurements in wind energy and meteorology applications. Due to the large measurement volume of a combined optical transmitter and receiver tilting in multiple directions, monostatic lidar generally has poor spatial and temporal resolution. It also exhibits large measurement uncertainty when operated in inhomogeneous flow; for instance, over complex terrain. In contrast, PTB’s bistatic lidar uses three dedicated receivers arranged around a central transmitter, resulting in an exceptionally small measurement volume. The coherent detection and modulation schemes used allow the detection of backscattered, Doppler shifted light down to the scale of single aerosols, realising the simultaneous measurement of all three wind velocity components. This paper outlines the design details and theory of operation of PTB’s bistatic lidar and provides an overview of selected comparative measurements. The results of these measurements show that the measurement uncertainty of PTB’s bistatic lidar is well within the measurement uncertainty of traditional cup anemometers while being fully independent of its site and traceable to the SI units. This allows its use as a transfer standard for the calibration of other remote sensing devices. Overall, PTB’s bistatic lidar shows great potential to improve the capability and accuracy of wind velocity measurements, such as for the investigation of highly dynamic flow processes upstream and in the wake of wind turbines.

Analysis and Design of Controller for Doubly-Fed Induction Generator in Wind Energy Application

Now a days, wind energy is developing as a significant wellspring of unpolluted and eco-friendly energy to supplant the enormous scope utilization of expendable wellsprings of energy. Wind Energy is pulling in enthusiasm of analysts everywhere throughout the globe as one of the most significant inexhaustible wellspring of energy. Be that as it may, the primary confinements lie in factor speed wind vitality. The under and above Synchronous speeds are obtained by utilizing a bidirectional power flow converter. In this paper a systematic transfer function model of a Doubly Fed Induction Generator based wind turbine structure connected to grid is developed in Mat lab/Simulink environment. The control structure of generator and that of turbine is developed and implemented. A simple approach is proposed to obtain the gains of Proportional Integral (PI) controller. A systematic detail of this control structure is presented. Under varying load conditions, it is observed that the reactive power is controlled and power factor of the system is maintained close to unity by using this scheme. Stator flux situated vector control method is conveyed for both stator and rotor side converters to supply autonomous control of active and reactive power and keep the DC link voltage consistent.