Effective Wind Speed Research Articles

Modelling the Wind Turbine by Using the Tip-Speed Ratio for Estimation and Control

The development of dynamic models for control purposes is characterised by the challenge of finding a compromise between the minimum necessary information about the system dynamics contained in the model and a model with a low level of complexity such that the model-based control system design becomes comfortable. To achieve this balance, a modified dynamic model for the drivetrain of a wind turbine is proposed in this contribution. The main idea is to introduce the tip-speed ratio as a state variable so that an interval observer can be designed in such a way that its estimates can be used in the torque control during the partial load operation as well as for the estimation of the effective wind speed. During the runtime, the observer’s matrix gain is recalculated to adapt the behaviour to the current operational state, which changes all the time with the wind speed. Besides the theoretical formulation, a numerical example of a 20 MW reference wind turbine illustrates the utility of the method. The results show good control performance concerning the tip-speed ratio control loop and a satisfactory estimation of the effective wind speed.

Parameter analysis of jet tunnel ventilation for long distance construction tunnels at high altitude

With the advancement of railroad tunnel construction work in the high-altitude areas of southwest China, the jet tunnel ventilation technology is used more frequently in long-distance tunnel. In this paper, a tunnel under construction at an altitude of 3500m in the Sichuan-Tibet Railway was taken as the background, and the key parameters of jet tunnel ventilation technology were simulated numerically and analyzed by combining the response surface method, while taking the altitude factor into consideration. The obtained results demonstrate that: Two jet fans with the air volume of 2250 m3/min could boost the tunnel air pressure by 12.85 Pa, making the air flow smoother in the tunnel. When the jet fan interval was 350m and the air volume reached 2250 m3/min, the polluted air circulation disappeared. When fans were installed at the effective spacing, wind speed and turbulence intensity were stable and fan comprehensive influence coefficient k was the highest on the center control line, which the ventilation effect was the best. The effective spacing under different air-volume conditions was analyzed to fit the effective spacing as a function of fan air volume change: y = 0.00560214x1.71734. Finally, the key parameters of jet tunnel ventilation technology were optimized by response surface method.

Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow Using Proper Orthogonal Decomposition

Preview measurements of the inflow by turbine-mounted lidar systems can be used to optimise wind turbine performance or alleviate structural loads. However, nacelle-mounted lidars suffer data losses due to unfavourable environmental conditions and laser beam obstruction by the rotating blades. Here, we apply proper orthogonal decomposition (POD) to the simulated line-of-sight wind speed measurements of a turbine-mounted scanning lidar obtained from two large eddy simulations. This work aimed at identifying the dominant POD modes that can be used to subsequently derive a reduced-order representation of the turbine inflow. Secondly, we reconstructed the data points lost due to blade passage by using Gappy-POD. We found that only a few modes are required to capture the dynamics of the wind field parameters commonly used for lidar-assisted wind turbine control, such as the effective wind speed, vertical shear and directional misalignment. By evaluating turbine-relevant metrics in the time and frequency domain, we found that a ten-mode reconstruction could accurately describe most spatio-temporal variations in the inflow. Furthermore, a modal interpretation is presented by direct comparison with these wind field parameters. We found that the Gappy-POD method performs substantially better than spatial interpolation techniques, accurately reconstructing up to even 50% of missing data. A POD-based wind field reconstruction offers a trade-off between wind field reconstruction techniques requiring flow assumptions and more complex physics-based representations, offers dimensional reduction and can overcome the blade passage limitation of nacelle-mounted lidar systems.

An effective wind speed prediction model combining secondary decomposition and regularised extreme learning machine optimised by cuckoo search algorithm

AbstractWind speed prediction has an important impact on the planning, economic operation and safe maintenance of wind power systems. However, the high volatility and intermittency of wind speed make it difficult to predict accurately. To improve the prediction accuracy, we developed a hybrid multistep wind speed prediction model named EWP‐CS‐RELM. In this model, a secondary decomposition technique of ensemble empirical mode decomposition (EEMD) and wavelet packet transform (WPT) is used, and it is called the EWP decomposition technique. This decomposition technique can achieve an adaptive processing of the data and accurately extract the characteristic components of the signal, avoiding the occurrence of pattern overlap and reducing the mutual interference between components. At the same time, the high and low‐frequency parts of the complex signal (component) can be decomposed into different frequency bands, and the corresponding frequency band can be selected adaptively to match the signal spectrum. The subsequence obtained after EWP decomposition is then predicted using a regularised extreme learning machine (RELM) optimised by the cuckoo search (CS) algorithm with strong global search ability to obtain the results. The hybrid prediction model is validated using four seasons of wind speed data from two wind farms in Shandong, China, and compared with seven other prediction models. Simulation results illustrate that the EWP‐CS‐RELM model outperforms the other seven models with the smallest statistical errors.

Application of Effective Wind Speed Estimation and New Sliding Mode Observer in Wind Energy Conversion System

The wind speed information measured by the wind speed sensor in the wind turbine generator may differ from sufficient wind speed. Therefore, this paper proposes a new effective wind speed calculation method and applies it to the optimal maximum power point tracking (MPPT) of wind energy. After estimating the turbine torque and its rotor speed, the process reverses the turbine’s aerodynamic model. The extended state observer (ESO) based on sliding mode control is used to estimate the aerodynamic torque, which solves complex and challenging tuning of the traditional ESO parameters, and replaces the conventional PI controller, thereby improving the anti-interference and robustness of the system. The sliding mode observer (SMO) is used to estimate the rotor speed. While satisfying the conditions of Lyapunov’s inequality, the design of the SMO is discussed in detail. The wind speed estimation method proposed for evaluating permanent magnet semidirect drive wind turbine and its application performance in maximum power tracking. The simulation was carried out in MATLAB/Simulink; the simulation confirmed that the estimated wind speed under different wind conditions is accurate. Compared with the traditional PI control, the utilization rate of wind energy is increased by 2%, which can be used for the MPPT control of the wind energy conversion system.

Overcoming Blade Interference: A Gappy-POD Data Reconstruction Method for Nacelle-Mounted Lidar Measurements

Nacelle-mounted lidar systems suffer data loss due to unfavourable atmospheric conditions such as rain or fog and most importantly the rotation of the blades that obstruct the laser beam from measuring upstream of the turbine. In this paper, we apply Gappy Proper Orthogonal Decomposition (Gappy-POD) to reconstruct incomplete flow fields from nacelle-mounted lidar measurements. For this purpose, two scanning nacelle-based SpinnerLidar simulations are performed inside a Large Eddy Simulation, one measuring the undisturbed wind inflow and the other in the wake of a reference turbine. Data loss of up to 90 % is simulated by artificially removing measurement points. The performance of Gappy-POD in reconstructing the wind fields is evaluated by comparing metrics such as effective wind speeds, vertical shear, yaw misalignment, wake deficit, wake meandering and the turbulent spectra in fixed and rotating frames of reference. We see that Gappy-POD is capable of accurately reconstructing missing data in comparison to normally used spatial interpolation techniques even in cases where 90 % of the data was missing. As a result, the dynamics of the reconstructed wind fields can be investigated based on highly accurate lidar-based wind field retrievals. The methodology can be used as a tool to develop effective wind field reconstruction techniques from sparse data.

Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions

This paper presents an analysis of the power loss of the upstream turbine for yaw misalignment depending on the inflow condition. High-quality experimental field data, obtained from a lidar wind profiler, met mast, GPS based yaw sensor, turbine data, and load data is used. To reduce the spreading of the power loss during yaw misalignment, the rotor effective wind speed is estimated using load data. Furthermore, the so-called power loss coefficient currently used in most engineering models, for yaw misalignment, is assessed at various inflow conditions. As main finding, the power loss coefficient increases for stronger vertical wind shear. In addition, there is an asymmetry with respect to the direction of the yaw misalignment that is enhanced by the wind shear exponent and the wind veer. Neglecting these two effects could result in in-conservative wake-steering decisions at stable stratification

Wind turbine LIDAR-assisted control: Power improvement, wind coherence and loads reduction

This paper aims to firstly review various LIDAR-assisted wind turbine control methods proposed in the past ten years for improving power production in the below rated wind speed region in order to clarify their performance and quantify the potential benefits and drawbacks of LIDAR-assisted control. Secondly, a new LIDAR-assisted control algorithm based on Extremum Seeking Control (ESC) using LIDAR preview information for tracking the optimal power coefficient value is presented and compared to the traditional control strategy. The results show that the newly proposed LIDAR-assisted control strategy is beneficial in the below rated wind speed region when the aerodynamic properties of blades are changed largely compared to the original design. Lastly, the effects of the wind field coherence between the LIDAR measured points and the rotor plane on the LIDAR-assisted control for loads reduction in above rated region is discussed. An important conclusion is that the major benefit of LIDAR-assisted wind turbine control is to improve collective pitch control for reducing fatigue loads in above rated region, when the coherence bandwidth between the LIDAR measured wind speed and the rotor effective wind speed is high.

Wind field reconstruction using nacelle based lidar measurements for floating wind turbines

In this work we investigate the influence of floater motions on nacelle based lidar wind speed measurements. The analysis is focused on wind field characteristics which are most relevant for performance analysis, namely rotor effective wind speed, shear and turbulence intensity. A numerical approach, coupling the publicly available in-house lidar simulation framework ViConDAR (Virtual Constrained turbulence and liDAR measurements) with an aeroelastic floating offshore wind turbine simulation is employed. Synthetic turbulent wind fields are generated with the open source turbulence generator TurbSim. The dynamics of the floating offshore wind turbine are simulated in the aeroelastic simulation code OpenFAST. Turbine dynamics and the corresponding synthetic wind field are then passed to the lidar simulation module of ViConDAR, which has been adapted for the consideration of turbine dynamics in all six degrees of freedom to simulate the lidar measurements under influence of motion. The simulation framework is demonstrated in a case study, simulating a lidar system on the nacelle of the IEA 15 MW turbine in combination with the WindCrete floater concept. Two different realistic lidar patterns are investigated under different metocean conditions. Different motion cases are examined individually and combined to evaluate the influence of rotational and translational degrees of freedom. Results show an increase of mean absolute error between lidar estimated and full wind field rotor effective wind speed of 0 to 25% depending on the environmental conditions. Observed overestimation of mean rotor effective wind speed was found to be in the region of up to 1%.

On the ill-conditioning of the combined wind speed estimator and tip-speed ratio tracking control scheme

In recent years, industrial controllers for modern wind turbines have been designed as a combined wind speed estimator and tip-speed ratio (WSE-TSR) tracking control scheme. In contrast to the conventional and widely used Kω 2 torque control strategy, the WSE-TSR scheme provides flexibility in terms of controller responsiveness and potentially improves power extraction performance. However, both control schemes heavily rely on prior information about the aerodynamic properties of the turbine rotor. Using a control-oriented linear analysis framework, this paper shows that the WSE-TSR scheme is inherently ill-conditioned. The ill-conditioning is defined as the inability of the scheme to uniquely determine the wind speed from the product with other model parameters in the power balance equation. Uncertainty of the power coefficient contribution in the latter mentioned product inevitably leads to a biased effective wind speed estimate. As a consequence, in the presence of uncertainty, the real-world wind turbine deviates from the intended optimal operating point, while the controller believes that the turbine operates at the desired set-point. Simulation results confirm that inaccurate model parameters lead to biased estimates of the actual turbine operating point, causing sub-optimal power extraction efficiency.

FLORIDyn - A dynamic and flexible framework for real-time wind farm control

This paper presents a new framework of the FLOw Redirection and Induction Dynamics (FLORIDyn) model. It is able to dynamically simulate the wake behaviour in wind farms under heterogeneous and changing environmental conditions at a low computational cost. The novelty of this work is the improved segregation of wake dynamics and wake influence: the framework creates Observation Points (OPs) at each turbine, which propagate wind field states and turbine states downstream and follow the wind direction of the free stream velocity. These observation points cover the dynamic aspects of the simulation. The OPs, along with the stored states, are now used to derive so-called Temporary Wind Farms (TWF), which approximate the effective intra-farm wind conditions at a given location. Within these TWF, the flow conditions are homogeneous and steady state. This way, arbitrary wake models can be used to calculate the farm influence on the location. The FLORIDyn framework also provides interfaces to flow field estimators, which is tested with an effective wind speed estimator. A nine turbine case is used to highlight the quality and performance of the simulation result. Compared to its predecessor, the new FLORIDyn framework decreases the computational cost by one to two orders of magnitude, which makes it a promising candidate for real-time model predictive dynamic wind farm control.

Smart Vertical Axis Highway Wind Turbine

Energy is an important aspect of our everyday life. The resources we use are limited whereas the population consuming the same is increasing day by day. Nowadays the requirement for electricity is much higher than its generation; hence the main objective of our work is to produce electricity at low cost with no effect on the environment. The objective of the work is to design a wind turbine to recapture wind energy from vehicles on the highway. A considerable amount of wind energy is produced due to the pressure difference created by the moving vehicles on the highways. This wind energy can be utilized for the generation of electrical energy with the help of vertical axis wind turbines. This work aims to extract this energy in the most efficient manner. A vertical axis wind turbine can be installed on the median of the roads so that the wind from both sides of the median will act tangentially in opposite directions on both sides of the turbine thereby increasing the effective wind speed acting on the turbine. This wind flow will depend on the velocity of the vehicle, size of the vehicle, and intensity of the traffic. Based on the studies made an optimal wind turbine design has to be made. The wind power harnessed through this method can be used for street lighting, traffic signal lighting, toll gates, etc.

A Positive Climatic Trend in the Global Offshore Wind Power

The climatic variation of offshore wind energy has a close relationship with the long-term plan of energy utilization. However, the work on this aspect is scarce and mainly focuses on the variation of wind power density (WPD). There is little research on the climatic trends of effective wind speed occurrence (EWSO) and occurrence of energy level greater than 200 W/m2 (rich level occurrence, RLO), which are directly related to the available rate and richness of wind energy. Based on the ERA-Interim wind product from the ECMWF, this study calculated the climatic trends of series of key factors of wind energy in the global oceans, including the WPD, EWSO, and RLO. The results show that the wind energy exhibits a positive trend globally for the past 36 years, with overall annual increasing trends in WPD, EWSO, and RLO, of 0.698 (W/m2)/yr, 0.076%/yr, and 0.090%/yr separately. The annual trend exhibits evident regional differences. The areas with significant increasing trends are mainly distributed in the mid- low-latitude waters of global oceans and part of the southern hemisphere westerlies. The annual increasing trend of WPD is strongest in the southern westerlies, especially in the extratropical South Pacific (ETSP), of about 1.64 (W/m2)/yr. The annual increasing trends of EWSO and RLO are strongest in the tropical waters, especially the tropical Pacific Ocean (TPO), of 0.17%/yr and 0.19%/yr separately. The annual and seasonal WPD, EWSO, and RLO in most global oceans have significant increasing trends or no significant variation, meaning that the wind energy trends are rich or stable, which is beneficial for energy development. The climatic trends of wind energy are dominated by different time periods. There is no evident abrupt change of wind energy in the extratropical waters globally and tropical Atlantic Ocean (TAO). The abrupt periods of wind energy in the TIO and TPO occurred in the end of the 20th century and the beginning of the 21st century. The wind energy of the South China Sea, Arabian Sea, and the Bay of Bengal and nino3 index share a common period of approximately 5 years. The offshore wind energy was controlled by an oscillating phenomenon.

Effective wind speed estimation study of the wind turbine based on deep learning

Wind speed is the driver of wind turbines, and the precise estimate of that makes it possible to improve the control effects and the efficiency of energy production. This paper proposes a method of effective wind speed estimator that considers the variation in blade radius and reconstructs the mapping of the aerodynamic. First, the proposed method utilizes available data of the wind turbine to train two neural network models based on radial basis functions (RBF). The models estimate the effective radius and reconstruct the aerodynamic mapping surface, respectively. Then, on this basis, train a wind speed estimation model based on the Long Short-Term Memory (LSTM) neural network, which can effectively estimate the current wind speed in real-time and predict the wind speed of the next time step as the reference for the following estimation. In addition, the RBF model and LSTM model can be updated and improved adaptively based on new data to ensure the accuracy of an effective wind speed estimator. Finally, the proposed wind speed estimation method is compared with the existing methods. The experimental results show that the proposed method has strong anti-interference characteristics, and improves the effective wind speed estimation accuracy over 70% on average.

An environmental‐friendly green energy system with a real wind speed prediction based on innovative hierarchical forecast error correction model

Precise forecasting of wind speed is an important technology to permit the reliable and efficient operation of sustainable energy system. Here, the authors offer an effective wind speed prediction (EWSP) technique based on advanced forecast error correction model (AFECM). Ramapuram, Chennai is selected to fit the wind-energy-based systems and ENNORE thermal power station, Chennai is selected to decrease the carbon footprints. In the proposed technique, the error correction model is made to produce the final forecast. The proposed wind speed prediction based on innovative hierarchical forecast error correction model reveals that with the proposed AFEC+ MSVM technique, the hourly average RMSEs, MADs, MSEs and MAPEs in Delivery 24 are decreased by 2.277%, 1.012%, 0.234%, and 1.245 % as compared to the normal SVM technique, while with AFEC+SVM method, average errors in RMSEs, MADs, MSEs and MAPEs in Delivery-24 are reduced by 3.385%, 2.056%, 1.956% and 2.546% as compared with the normal SVM technique, whereas the forecasting errors with AFEC+BP method, the middling errors in RMSEs, MADs, MSEs and MAPEs in Delivery-24 are decreased by 2.867%, 1.654%, 1.834% and 2.298%, compared with simple BP method. The study displays that 1,485,550 kg CO2 emission is decreased from the ENNORE thermal power plant.

The effect of wind speed averaging time on the study of soil wind erosion on typical land surfaces

Wind erosion studies based on wind speed of different averaging times always weaken the effect of effective wind force and wind speed fluctuation, and produce large errors in wind erosion assessment. To quantitatively reveal the time scale effect of average wind speed on wind erosion research, this study analyzed the changes in wind speed distribution, as well as that in wind erosion event statistics and wind erosion amount calculation under different statistical time intervals, based on field observations. To further eliminate this time scale effect, wind speed conversion relationships of different averaging times were established through quantile matching of wind speed distribution function. The results show that as the averaging time increases: (1) both high and low wind speeds are filtered, making the proportion of medium wind speeds increasing, and wind speed distribution gets narrow significantly; (2) both the number and cumulative duration of wind erosion events decrease exponentially; (3) wind erosion amount corresponding to different wind speed grades differ significantly; (4) a continuous conversion equation between wind speed of different averaging times and that of one minute were established; (5) the time scale effect on different surface types are significantly different. In general, by using the statistical characteristics of wind speed distribution innovatively and considering difference in land surface properties, this study established an empirical wind speed conversion model on typical wind erosion surfaces, which will improve the evaluation accuracy of soil wind erosion intensity, and provide basis for the construction of process-based soil wind erosion model.

The Proportional Integral Notch and Coleman Blade Effective Wind Speed Estimators and Their Similarities

The estimation of the rotor effective wind speed is used in modern wind turbines to provide advanced power and load control capabilities. However, with the ever increasing rotor sizes, the wind field over the rotor surface shows a higher degree of spatial variation. A single effective wind speed estimation therefore limits the attainable levels of load mitigation, and the estimation of the Blade Effective Wind Speed (BEWS) might present opportunities for improved load control. This letter introduces two novel BEWS estimator approaches: A Proportional Integral Notch (PIN) estimator based on individual blade load measurements, and a Coleman estimator targeting the estimation in the non-rotating frame. Given the seeming disparities between these two estimators, the objective of this letter is to analyze the similarities between the approaches. It is shown that the PIN estimator, which is equivalent to the diagonal form of the Coleman estimator, is a simple but effective method to estimate the BEWS. The Coleman estimator, which takes the coupling effects between individual blades into account, shows a more well behaved transient response than the PIN estimator.

DC nanogrid for Buildings: Study based on experimental investigation of load performance and Annual energy consumption

Considering that most end-use electrical equipments are DC-driven, a thorough examination of the energy delivery system for buildings has become essential. Since alternate energy sources are majorly DC-based, using a DC distribution system instead of a conventional AC distribution system reduces the number of AC/DC conversion stages. This work focuses on the experimental verification of the difference in power consumption between an energy-efficient AC load and its equivalent energy-efficient DC load. Separate case studies are done for two different buildings, comprising mainly of light and fan applications. Since the consumption is majorly for light and fan applications for buildings, 48 V Extra Low Voltage DC (ELVDC) supply is most suited for DC nanogrid. The savings from DC appliances are highlighted, with a focus on actual outputs like illuminance for lights and effective wind speed for fans rather than rpm (the common practice). The energy savings and associated reduction in Green House Gas(GHG) emissions are calculated for buildings of different occupancies. If all the driving forces, viz. renewable energy agenda, loss avoidance in the distribution system and the opportunistic switch to energy-efficient appliances are clubbed together, the benefits are enormous when looked upon with the end destination as a cleaner green planet.

WITHDRAWN: Nonlinear Kalman filters based rotor effective wind speed estimation: An experimental comparison

This article has been withdrawn at the request of the editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy .

The Immersion and Invariance Wind Speed Estimator Revisited and New Results

The Immersion and Invariance (I&I) wind speed estimator is a powerful and widely-used technique to estimate the rotor effective wind speed on horizontal axis wind turbines. Anyway, its global convergence proof is rather cumbersome, which hinders the extension of the method and proof to time-delayed and/or uncertain systems. In this letter, we illustrate that the circle criterion can be used as an alternative method to prove the global convergence of the I&I estimator. This also opens up the inclusion of time-delays and uncertainties. First, we demonstrate that the I&I wind speed estimator is equivalent to a torque balance estimator with a proportional correction term. As the nonlinearity in the estimator is sector bounded, the well-known circle criterion is applied to the estimator to guarantee its global convergence for time-delayed systems. By looking at the theoretical framework from this new perspective, this letter further proposes the addition of an integrator to the correction term to improve the estimator performance. Case studies show that the proposed estimator with an additional integral correction term is effective at wind speed estimation. Furthermore, its global convergence can be guaranteed by the circle criterion for time-delayed systems.