Power Production Of Wind Turbine Research Articles

Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains

The correct prediction of the wind speed and turbulence levels over complex terrain is essential for accurately assessing wind turbine wake recovery, power production, safety, and wind farm design. In this paper, two modified RANS turbulence models are proposed, which are innovative variants of the conventional SST k-ω model and the linear Reynolds stress model (RSM) featuring optimized closure constants. Then, these two modified models and their origin models are applied to compare and analyze wind flows from a 3D hill wind tunnel experiment and two field measurements over typical complex terrain, including Askervein hill and Bolund island, with the aim of analyzing the sensitivity of wind flows to different RANS turbulence models. The study focuses on analyzing the effects of different turbulence models on the self-sustainability of wind speed and turbulent kinetic energy upstream of the computational domain and on the accuracy of wind flow prediction over complex terrain. The results show that our modified RSM model shows better agreement with the available experimental data on the upstream and leeward sides of all simulated hills. The wind speed on the leeward slope is particularly sensitive to the turbulence model, with a maximum difference in the relative root mean square error (RRMSE) that can reach 11% among the four models. The accuracy of the turbulent kinetic energy depends on the self-sustainability of the upstream turbulent kinetic energy and the predictive ability of the turbulence model for separated flows, and the maximum difference in the RRMSE of the four models can reach 47%. In addition, the advantages and disadvantages of the tested models are discussed to provide guidance for model selection during wind flow simulations in complex terrain.

The influence of the rotor spacing distance and rotating speed ratio on the power production of dual rotor wind turbines using a modified two-way interaction blade element momentum method

The present study aims to develop an accurate and fast improved blade element method (BEM) with two-way rotor-rotor interaction model which can account for two-way rotor-rotor interaction and the influence of rotor spacing while most previous BEM models for co-axis dual rotor wind turbines (DRWT) ignore the rear to front rotor interaction effect. A modified BEM model distinct from computationally intensive Computation Fluids Dynamics (CFD) model, is proposed for DRWTs. Validation of the present model is conducted by comparing it with experimental data from referenced literature for both co- and counter-rotating configuration. In our case study, for both co- and counter-rotating configurations, the power coefficient (CP) maps exhibit a double-hump shape instead of a single peak on the power output -λ curve observed in a single rotor wind turbine system. This characteristic may lead the DRWT to a local CPmax condition rather than the global CPmax while pursing the optimum operational condition. Although the counter-rotating DRWT can achieve a higher CPmax for most rotor spacing ratio (S/R) cases, it exhibits inferior power output than the single rotor for the S/R = 0.1 case. The present model can serve as a rapid and accurate model for configuring and control strategy evaluating DRWTs.

The role of utilizing artificial intelligence and renewable energy in reaching sustainable development goals

Many nations want to use only renewable energy by 2050. Given the recent rapid expansion in RE use in the global energy mix and its progressive impact on the global energy sector, the evaluation and analysis of Renewable Energy's impact on achieving sustainable development goals is insufficient. Wind energy could be renewable. For smart grid supply-and-demand issues, wind power forecasting is critical. One of the biggest challenges of wind energy is its significant fluctuation and intermittent nature, which makes forecasting difficult. This study's goal is to develop data-driven models to predict wind speed and power. This paper uses Machine Learning (ML) and Deep Learning (DL) to improve a wind speed prediction and recommendation system for wind turbine power production using site climatological data. This system optimizes turbine use by selecting the right number of turbines to operate solely based on the required energy, which is related to wind power and speed, and recommends the best power station location to determine the best turbine run time. The Proposed Enhanced Recommendation System (PERS) includes the Wind Speed Prediction Module (WSPM), Wind Speed vs. Power Consumption Calculation (WSPC), and Recommendation Module (RM). Test results showed the suggested method works in run time. The XGBoost or Random Forest regressor predicted 15-day power output with 94 % accuracy and 6 % mean average percentage error.

Statistical characterization of marine wind structure over wind-turbine-relevant heights in tropical cyclones

Tropical cyclones (or typhoons, hurricanes) may influence the power production and structural integrity of offshore wind turbines. However, understanding of tropical cyclone wind structure over turbine-relevant heights remains limited. Based on observations from a wind lidar and a meteorological mast at a planned offshore wind farm site, this paper investigates the marine wind characteristics, e.g., wind speed shear, wind direction veer, and turbulence intensity, over turbine-relevant heights in tropical cyclones. Statistical distributions of the marine wind characteristics are analyzed, and their variations with upstream fetch conditions and hub-height wind speeds are examined. Some noteworthy features of the marine wind structure in tropical cyclones are observed, such as the median wind veer rate of 0.018°/m and veer angle of 2.2° across the turbine rotor heights, different wind shear coefficients and wind veer rates for upper and lower rotors, and levelling off or decrease of wind shear coefficient and turbulence intensity with increasing wind speed at hub-height wind speed over 25 m/s. The outcome of this study is expected to provide useful information for design and operation of offshore wind turbines, marine platforms, and other ocean structures in tropical cyclone-prone regions.

Wind farm structural response and wake dynamics for an evolving stable boundary layer: computational and experimental comparisons

Abstract. The wind turbine design process requires performing thousands of simulations for a wide range of inflow and control conditions, which necessitates computationally efficient yet time-accurate models, especially when considering wind farm settings. To this end, FAST.Farm is a dynamic-wake-meandering-based mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. This work is an extension of a study that addressed constructing a diurnal cycle evolution based on experimental data (Quon, 2024). Here, this inflow is used to validate the turbine structural and wake-meandering response between experimental data, FAST.Farm simulation results, and high-fidelity large-eddy simulation results from the coupled Simulator fOr Wind Farm Applications (SOWFA)–OpenFAST tool. The validation occurs within the nocturnal stable boundary layer when corresponding meteorological and turbine data are available. To this end, we compared the load results from FAST.Farm and SOWFA–OpenFAST to multi-turbine measurements from a subset of a full-scale wind farm. Computational predictions of blade-root and tower-base bending loads are compared to 10 min statistics of strain gauge measurements during 3.5 h of the evolving stable boundary layer, generally with good agreement. This time period coincided with an active wake-steering campaign of an upstream turbine, resulting in time-varying yaw positions of all turbines. Wake meandering was also compared between the computational solutions, generally with excellent agreement. Simulations were based on a high-fidelity precursor constructed from inflow measurements and using state-of-the-art mesoscale-to-microscale coupling.

Analyzing wind turbine and wind farm outputs under dependence between wind speed and wind turbine availability

In this work, we assess the power production of a single wind turbine and wind farm under probabilistic distributions where wind speed and wind turbine availability are dependent on each other. In our developments, the wind speed distribution is assumed to be discrete. Our method of finding the corresponding distributions is based on classical probabilistic techniques.

Analytical Simulation Approach to Evaluate the Ambient Humidity Effects on the Performance of a Wind Accelerator

Abstract The power production of wind turbines is experiencing an upward trend. However, on the other hand, the wind turbine’s performance slows down due to unseen changes in the environment. Humidity is one of the environmental factors affecting the performance of wind turbines. When humidity levels are high in the atmosphere, air is less dense. This air is lighter, and it results in reduced air pressure. A remedy to increase the air pressure in the direction of wind flow is to locate a wind accelerator, an aerodynamic device before the wind turbine to amplify the incoming wind speed and reduce the impact of humidity on the wind energy. This study intends to investigate the impact of environmental humidity on the performance of the wind accelerator. The study includes modelling the wind accelerator using Ansys 2023R1, to simulate different humidity levels. An analytical simulation is conducted to describe the aerodynamic behaviour of the wind accelerator in response to the changes in ambient humidity. The air at different percentage levels of humidity is passed through the proposed wind accelerator geometry to observe the wind speed variation and kinetic energy of the wind. The simulation study reveals that the wind speed and kinetic energy show higher values when the humidity is at a lower level, while the opposite is true when the humidity is higher. This emphasizes how important it is to consider all environmental elements while designing and developing wind turbines and wind accelerators for optimal performance subjected to various climatic conditions.

Impact of inflow characteristics on the instantaneous power production using detrended fluctuation analysis

This study investigates the influence of inflow characteristics on wind turbine power production using Detrended Fluctuation Analysis (DFA). The research focuses on extracting indicators for the energy of flow fluctuations at small timescales and their scaling properties. This methodology is demonstrated using one year of 1-second SCADA data from an offshore wind farm. These indicators are then compared with the commonly used Turbulence Intensity (TI). The findings demonstrate that DFA coefficients, specifically the DFA y-intercept and the DFA slope, account for a significantly larger portion of the variability in active power. This research enhances SCADA-based performance analysis.

Modeling the effect of wind speed and direction shear on utility‐scale wind turbine power production

AbstractWind speed and direction variations across the rotor affect power production. As utility‐scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations, and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility‐scale wind turbine. In the field measurements of the utility‐scale turbine, discrete combinations of speed and direction shear induce changes in power production of −19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over‐performance and increasing magnitudes of direction shear to greater under‐performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub‐models. To further assess the influence of complex, non‐monotonic wind profiles, we also drive the models with best‐fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time‐varying, non‐monotonic wind shear affects wind power production.

Power production and large-scale wake effects of offshore wind turbines with low specific rating

With the aim to increase the energy yield of large offshore wind farm clusters, especially during low wind speeds, innovative rotor concepts with low specific ratings are being developed. We use mesoscale simulations, to compare wakes and power production of a wind farm cluster with a rated power of 1.17 GW, equipped with a turbine featuring a low specific rating turbine concept, and the IEA 15 MW offshore reference turbine. While the IEA turbine resembles typical offshore wind turbine concepts, the HL rotor features a very large rotor diameter compared to its rated power, thus resulting in a very low specific rating for an offshore turbine. We simulate two wind farms made up of either of the two turbines that have the same installed capacity and farm layout. We found an improved power production of 86 % to 100 % for the HL wind farm in partial load conditions and comparable power production at rated operation. While the HL wind farm exhibits a more pronounced cluster wake during partial load operation, the IEA wind farm induces a larger wind speed deficit in the wake during rated operation. We discuss observed differences in wake behaviour, attributing them to the larger rotor diameter and lower thrust coefficient of the HL concept.

Dynamic interaction of inflow and rotor time scales and impact on single turbine wake recovery

The entrainment and recovery of wind turbine wakes are highly dependent on atmospheric inflow conditions, which has typically been quantified through the turbulent intensity. However, recent studies have shown that the integral time scales of the inflow has significant impact on the wake recovery. Concurrently, increased power production can also be achieved through intentionally introducing beneficial time scales by altering the control of the individual wind turbines. This study studies the combined impact of the dynamic interaction between dominant inflow and rotor time scales. The results show increased power production of a downstream wind turbine of more than 50% for the largest thrust coefficients and tip-speed ratios (TSR). However, the peak power gain occurs at different downstream positions indicating that combinations of inflow time scales and TSR = 6 result in faster near wake breakdown compared to the same inflow time scales combined with higher thrust coefficient of TSR = 8.

Impact of motions on floating wind turbine power production

Floating offshore wind opens new possibilities for harnessing wind energy in deeper waters where it is not feasible to install traditional fixed-bottom turbines. Accessing deeper waters enables the utilization of stronger and more consistent wind resources, potentially leading to higher energy production. However, one of the challenges of floating offshore wind is the impact of increased motions on floating turbine’s power. This paper addresses this challenge by investigating wind field reconstruction and motion compensation algorithms when using a nacelle lidar to characterize floating turbine inflow wind speed. The fully instrumented TetraSpar floating demonstrator with a 3.6 MW wind turbine and a nacelle lidar, offers a unique opportunity to investigate the effect of motions on power production. Observations from real measurements are complemented with numerical simulations, highlighting that motion-compensating for mean tilt angle is an effective correction for 10 min average power performance measurements. Results showed that mean tilt angles causing the lidar beams to shift upwards, result in overestimation of the estimated hub height wind speed if no motion compensation is applied. The paper also assesses the impact of motions induced by different sea states on power production.

Maximizing wind farm power output with the helix approach: Experimental validation and wake analysis using tomographic particle image velocimetry

AbstractWind farm control can play a key role in reducing the negative impact of wakes on wind turbine power production. The helix approach is a recent innovation in the field of wind farm control, which employs individual blade pitch control to induce a helical velocity profile in a wind turbine wake. This forced meandering of the wake has turned out to be very effective for the recovery of the wake, increasing the power output of downstream turbines by a significant amount. This paper presents a wind tunnel study with two scaled wind turbine models of which the upstream turbine is operated with the helix approach. We used tomographic particle image velocimetry to study the dynamic behavior of the wake under the influence of the helix excitation. The measured flow fields confirm the wake recovery capabilities of the helix approach compared with normal operation. Additional emphasis is put on the effect of the helix approach on the breakdown of blade tip vortices, a process that plays an important role in re‐energizing the wake. Measurements indicate that the breakdown of tip vortices and the resulting destabilization of the wake are enhanced significantly with the helix approach. Finally, turbine measurements show that the helix approach was able to increase the combined power for this particular two‐turbine setup by as much as 15%.

Integral Backstepping Sliding Mode Control for Maximizing the Power Production of Wind Turbines

Wind turbine control has attracted increasing attention, driven in part by evolving challenges due to the growing size and complexity of wind turbines. Addressing these challenges and maximizing wind turbine power production requires the application of advanced nonlinear control methods. Sliding Mode Control (SMC) has emerged as a promising approach in this context. Recent studies have explored the integration of an integral term with SMC, called I-SMC. This technique has been shown to result in system responses that exhibit chattering phenomena with noticeable state errors. This study aimed to address these issues through the introduction of a novel controller known as Integral Backstepping SMC (IB-SMC). This study demonstrated that IBSMC not only ensured the stability of wind turbines but also outperformed other control strategies, even in the presence of disturbances of approximately 30% of the rated electromagnetic torque. To validate the effectiveness of the proposed controller, extensive simulation tests were carried out using MATLAB /Simulink software to evaluate the controller's responsiveness to rapid changes in conditions, as well as its robustness and overall performance. A comparison was carried out between the IBSMC and previous SMCs to evaluate their ability to reduce steady-state error and chattering.

Analytical model for the power production of a yaw-misaligned wind turbine

Wake steering has proven to be effective in enhancing the power output of a wind farm. However, this approach still highly relies on empirical formulas to predict the power production of yawed turbines, limiting its potential in practical applications. In this study, an analytical model is proposed to predict the power production of a yaw-misaligned turbine under uniform inflow conditions. The model is based on the combination of the blade element theory and the momentum theory, with a modification in the latter to account for the disturbance on the spanwise velocity caused by the yawed turbine. A series of large eddy simulations were performed using a utility-scale wind turbine operating at yaw angles |γ|≤30° and tip-speed ratios λ=5–8. The validity of the proposed model is confirmed by the good agreement between the theoretical predictions and the simulation data. Furthermore, the well-known cosine model is shown to describe well the power production of the yawed turbine within the studied parameter range. However, the power-yaw loss exponent is not a constant, but rather a function of the tip-speed ratio. These findings may be useful in yaw optimization and control strategies in wind farms.

Multi-Agent Systems and Machine Learning for Wind Turbine Power Prediction from an Educational Perspective

Artificial intelligence (AI) is an umbrella term that encompasses different fields of study, and topics related to these fields are addressed separately or within the scope of AI. Multi-agent systems (MASs) and machine learning (ML) are the core concepts of AI that are taught during AI courses. The separate explanation of these core research areas is common, but the emergence of federated learning has triggered their combined usage. This paper describes a practical scenario in the energy domain where these technologies can be used together to provide a sustainable energy solution for predicting wind turbine active power production. The projects in the AI course were assigned prior to the step-by-step learning of MASs and ML. These concepts were applied using a wind turbine energy dataset collected in Turkey to predict the power production of wind turbines. The observed performance improvements, achieved by applying various agent architectures and data partitioning scenarios, indicate that boosting methods such as LightGBM yield better results even when the settings are modified. Additionally, a questionnaire about the assignments was filled out by the student groups to assess the impact of learning MASs and ML through project-based education. The application of MASs and ML in a hybrid way proves valuable for learning core concepts related to AI education, as evidenced by feedback from students.

Exploring influence of air density deviation on power production of wind energy conversion system: Study on correction method

Thousands of wind turbines are already installed, providing renewable energy to meet the growing energy demand of consumers. This article investigates the effect of air density deviation, which depends on weather conditions, on power production of operational wind turbines, and examines strategy to mitigate this effect. Throughout the year, the air density in the area where the wind turbines are installed experiences continuous variations. It affects the annual power production of the wind turbine. Therefore, it is essential to discover a method that can reduce the effect of air density changes on power production of the wind turbine without adding extra extender blade. The effect of air density deviation on parameter of the wind turbines is investigated using four years measurement data (with a 10-minute interval) as well as simulation data. To reduce air density effect, the pitch angle and torque gain are readjusted rather than setting it constant based on the air density value. The analysis and simulation study were conducted in Matlab/Simulink® software. The results revealed that the suggested correction in pitch angle and torque gain according air density effectively alleviated the effect of air density variations, and the power production of the wind turbine was increased.

Brief communication: On the definition of the low-level jet

Abstract. Low-level jets (LLJs) are examples of non-logarithmic wind speed profiles affecting wind turbine power production, wake recovery, and structural/aerodynamic loading. However, there is no consensus regarding which definition should be applied for jet identification. In this study we argue that a shear definition is more relevant to wind energy than a falloff definition. The shear definition is demonstrated and validated through the development of a European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) LLJ climatology for six sites. Identification of LLJs and their morphology, frequency, and intensity is critically dependent on the (i) vertical window of data from which LLJs are extracted and (ii) the definition employed.

Fixed-time fuzzy adaptive control scheme for doubly fed induction generator–based wind turbine

This work focuses on the issue of designing a fixed-time fuzzy adaptive controller for variable-speed doubly fed induction generator-based wind turbine. The main goal is to optimize the wind turbine power production, while improving the performance of the overall control system such as, convergence speed, tracking accuracy, and robustness. For the control design, the backstepping technique is adopted, and fuzzy adaptive systems are employed as universal approximators to estimate continuous functional uncertainties. Using fixed–time stability concept, a detailed mathematical stability proof of the closed-loop control system is performed. Simulation results are presented to testify the efficiency of the designed control approach.

Wake and power prediction of horizontal-axis wind farm under yaw-controlled conditions with machine learning

The main objective of this study is to employ the Extreme Gradient Boosting (XGBoost) machine learning algorithm to predict the power, wake, and turbulent characteristics of horizontal-axis wind farms under yaw-controlled conditions. For this purpose, a series of high-fidelity numerical simulations using LES method are performed over tandem NREL-5 MW wind turbines to generate the input data for training and testing in machine learning analysis. It is observed that XGBoost is more accurate for wake prediction of the yaw-controlled wind farms compared to ANN, which was used in previous studies. The results illustrate that XGBoost can predict the power with a mean deviation of 0.94 % for different yaw angles, while ANN can estimate the power generation with a mean deviation of 2.15 % for various tested yaw angles. At far wake regions (X > 2000 m) of the second wind turbine, the deviations reach below 1 %. Moreover, XGBoost requires a much shorter training time, 87.5 % faster than ANN. The power production of both wind turbines can be predicted more accurately with XGBoost compared to ANN. The wake prediction time of XGBoost is just 0.105 sec, while this time is 4.480 for the ANN model. In conclusion, XGBoost provides a significant reduction in error and training time compared to ANN and deep learning algorithms over yaw-misaligned wind farms.