Wind Turbine Power Research Articles

Study on aerodynamic performance of floating offshore wind turbine with fusion winglets under yaw motion

ABSTRACT Floating offshore wind turbines (FOWTs) are subject to the effects of waves, currents, and wind, which induced the platform's six degrees of freedom motion affects the aerodynamic performance of FOWTs. The yaw motion not only changes the incidence angle of the incoming flow, but also adds an angular velocity to the blades. Numerous studies have shown that winglets can increase the blade's total aerodynamic efficiency. This paper investigates the aerodynamic performance of FOWTs with fusion winglets during yaw motion. The findings demonstrate that the effect of changing frequency on wind turbine power is more significant at high amplitude conditions, when the frequency is increased from 0.1 Hz to 0.2 Hz with the amplitude of 6°, the average power growth rate of the wind turbine with winglets is 3.91% higher than the average power growth rate of the wind turbine without winglets.

Towards machine learning applications for structural load and power assessment of wind turbine: An engineering perspective

Over the past decades, the increasing energy demand has accelerated the construction of wind farms, raising higher expectations for precise load and power assessments in wind turbine performance. Traditional methods, which rely on analytical wake models and performance curves, often fail to adapt to complex inflow scenarios, leading to significant inaccuracies in predicting turbine loads and power output. This research addresses these challenges by introducing a novel two-phase framework for various phases of wind farm planning and development, using the NREL 5MW baseline wind turbine as a case study. The first part involves deriving recommended values for simplified thrust modulation factors at the preliminary design phase, enabling swift evaluation of maximum and fatigue thrust loads crucial for wind farm optimization. The second part focuses on designing and training a machine learning model at the detailed design phase. A gradient-boosting-based framework based on LightGBM provides comprehensive methods for assessing wind turbine load and power, enhancing the precision and efficiency of these assessments. The proposed model achieves significant improvements in predictive accuracy, achieving mean R-Squared of 0.995, 0.988, and 0.995 for power, peak load, and damage equivalent load evaluation, respectively. The framework streamlines the assessment process, enhancing both the accuracy and speed of power and load evaluations for wind farm design. This is expected to reduce computational costs and improve the effectiveness of downstream tasks, such as layout optimization and wake steering.

Machine learning approaches in predicting the wind power output and turbine rotational speed in a wind farm

ABSTRACT Accurate wind energy forecasting has become increasingly important to effectively manage the energy produced by wind turbine power plants and optimize their operational performance. In this study, several artificial intelligence techniques are recommended to simulate turbine rotation speed to predict wind energy production 10 minutes ahead. Four tools are employed: The fuzzy c-means (FCM) approach of adaptive neuro-fuzzy inference system (ANFIS), long short-term memory (LSTM), grid partitioning (GP) method of adaptive neuro-fuzzy inference system and subtractive clustering (SC) algorithm of adaptive neuro-fuzzy inference system were used for predictions. These methods use historical data as input for the physical parameters to be estimated and estimate the subsequent value as the output. Thus, using only historical data, future values of the considered parameter can be easily predicted without the need for other physical parameters, such as meteorological data or data regarding the design of the mechanical installation, or without solving complex differential equations containing many unknowns. In the study, wind power and rotor rotational speed data from one wind turbine operating in a wind farm is taken into consideration. Among 34 models, LSTM is shown to perform best in capturing real observed wind turbine parameters. In the estimations of wind output power of wind turbine, it is reported that the rates of evaluation criteria were computed as 136.04 kW MAE, 242.64 kW RMSE, and 0.9711 R. Besides, in the predictions of turbine rotational speed, it is notified that 0.72 rpm MAE, 1.16 rpm RMSE, and 0.9452 R values were computed. On the other hand, among the generated ANFIS models, ANFIS-FCM model yields best accurate results with 138.69 kW MAE, 244.40 kW RMSE and 0.9708 R values in wind power, 0.73 rpm MAE, 1.17 rpm RMSE and 0.9451 R values in turbine rotor rotation.

Wind Turbine Static Errors Related to Yaw, Pitch or Anemometer Apparatus: Guidelines for the Diagnosis and Related Performance Assessment

The optimization of the efficiency of wind turbine systems is a fundamental task, from the perspective of a growing share of electricity produced from wind. Despite this, and given the complex multivariate dependence of the power of wind turbines on environmental conditions and working parameters, the literature is lacking studies specifically devoted to a careful characterization of wind farm performance. In particular, in the literature, it is overlooked that there are several types of faults which have similar manifestations and that can be defined as static errors. This kind of error manifests as a static bias occurring from a certain time onward, which can affect the anemometer, the absolute or relative pitch of the blades, or the yaw system. Static or systematic errors typically do not cause the functional failure of the wind turbine system, but they deserve attention due to the fact that they cause power production loss throughout the operation time. Based on this, the first objective of the present study is a critical review of the recent papers devoted to three types of wind turbine static errors: anemometer bias, static yaw error, and pitch misalignment. As a result, a comprehensive viewpoint, enhancing the state of the art in the literature, is developed in this study. Given that the use of data collected by Supervisory Control And Data Acquisition (SCADA) systems has, up to now, been prevailing for the diagnosis of systematic errors compared to the use of further specific sensors, particular attention in the present study is thus devoted to the discussion of the phenomena which can be observable through SCADA data analysis. Based on this, finally, a rigorous work flow is formulated for detecting static errors and discriminating among them through SCADA data analysis. Nevertheless, methods based on additional information sources (like further sensors or meteorological data) are also discussed. An important aspect of this study is that, for each considered type of systematic error, some previously unpublished results based on real-world SCADA data are reported in order to corroborate the proposed framework. Summarizing, then, the present is the first paper which considers and discusses several types of wind turbine static errors in a unified viewpoint, correctly interprets apparently controversial results collected in the literature, and finally provides guidelines for the diagnosis of this kind of error and for the quantification of the performance drop associated with their presence.

Multifractal analysis of wind turbine power and rainfall from an operational wind farm – Part 1: Wind turbine power and the associated biases

Abstract. The inherent variability in atmospheric fields, which extends over a wide range of temporal and spatial scales, is also transferred to energy fields extracted from them. In the specific case of wind power generation, this can be seen in the theoretical power available for extraction and the empirical power produced by turbines. To model and analyse them, it is important to quantify their variability, intermittency, and correlations with other interacting fields across scales. To understand the uncertainties involved in power production, power outputs from four 2 MW turbines are analysed (from an operational wind farm at Pay d'Othe, 110 km south-east of Paris, France) using the scale-invariant framework of universal multifractals (UM). Their scaling properties were compared with power available at the same location from simultaneously measured wind velocity. While statistically analysing the turbine output, the rated power acts like an upper threshold that results in biased estimators. This is identified and quantified here using the theoretical framework of UM and validated using numerical simulations. Understanding the effect of instrumental thresholds in statistical analysis is important in retrieving actual fields and modelling them, more so in wind power production, where the uncertainties due to turbulence are already a leading challenge. This is expanded in Part 2, where the influence of rainfall on power production is studied across scales using UM and joint multifractals.

Multifractal analysis of wind turbine power and rainfall from an operational wind farm – Part 2: Joint analysis of available wind power and rain intensity

Abstract. In the increasing global transition towards renewable and carbon-neutral energy, understanding the uncertainties associated with wind power production is extremely important. In addition to the widely acknowledged uncertainties from turbulence and wind intermittency, further complexity arises from the influence of rainfall, which only a limited number of studies have addressed so far. To understand this, multiple 3D sonic anemometers, mini meteorological stations, and optical disdrometers were employed on a meteorological mast on the Pays d'Othe wind farm (110 km south-east of Paris, France) in the framework of the Rainfall Wind Turbine or Turbulence (RW-Turb) project (https://hmco.enpc.fr/portfolio-archive/rw-turb/, last access: 26 November 2024). With these simultaneously measured data, wind power and its associated atmospheric fields were studied under various rainy conditions. Variations of the wind velocity, power available on the wind farm, power produced by wind turbines, and air density are examined here, under rainy and dry conditions, using the scale-invariant framework of universal multifractals (UM). Since rated power acts like an upper threshold in statistical analysis of turbine power (discussed in Part 1), theoretically available power was used as a proxy. From an event-based analysis, differences in UM parameters were observed between rainy and dry conditions for the fields. This is explored further using joint multifractal analysis, which revealed an increase in the correlation exponent between various fields with the rain rate. Here we also examine the possibility of variation in power production by rainy conditions (convective or stratiform) as well as by regimes of wind velocity. While examining time steps according to wind velocity, turbine power curves showed different regions of departure from the state curve according to the rain rate.

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.

A LES-ALM Study for the Turbulence Characteristics of Wind Turbine Wake Under Different Roughness Lengths

To investigate the characteristics of wind turbine wakes under different aerodynamic roughness lengths, a series of LES-ALM simulations were carried out in this study. First, a sensitivity analysis of the time step of the simulation results was performed. Then, the study compared the power and thrust of wind turbines under different roughness conditions. Finally, the mean velocity deficit, added turbulence intensity, and Reynolds shear stresses in the wake were analyzed under different roughness conditions. This study finds that a 0.1 s time step can provide satisfactory results for the LES-ALM compared to a 0.02 s time step. Furthermore, for the same hub-height wind speed, the thrust coefficient varies from 0.75 to 0.8 under the different roughness levels. As the roughness length increases, the time-averaged velocity deficit and added turbulence intensity decreases, and the wake recovers more quickly at the incoming level. However, the effect of roughness length on the Reynolds shear stress is weak within the downstream range of x = 6D to 10D. For the velocity deficit, a single Gaussian function is not able to describe its vertical distribution. Additionally, under higher roughness conditions, the height of the wake center is distinctively higher than the hub height as the wake develops downstream. The findings of this paper are beneficial for selecting the approximate numerical parameters for the wake simulations and provide deeper insights into the turbulence mechanisms of wind turbine wake, which are crucial for establishing analytical models to predict the wake field.

Effect of Electrolyte pH during Pretreatment of Carbon Electrodes on the Subsequent Kinetics of the VII/VIII and VIV/VV Reactions in Vanadium Flow Batteries

Growing environmental concerns and the depletion of fossil fuel reserves have prompted increased focus on renewable energy sources like wind and solar power.1-3 However, integrating these renewables into power grids is challenging due to their intermittent nature. Large-scale energy storage systems are seen as a solution to improve the reliability and efficiency of renewable energy. Vanadium flow batteries (VFBs) are gaining attention for their potential in large-scale energy storage due to the advantage of utilizing the same active element on both sides of the battery, which eliminates cross-contamination issues and extends the lifetime of the battery.4-6 A typical VFB consists of a cell stack and two external tanks containing electrolytes with redox active species, circulated through the cell stack via pumps. The negative half-cell contains V2+/V3+ (VII/VIII) species and the positive half-cell contains VO2+/VO2 + (VIV/VV) species, separated by an ion exchange membrane. Carbon or graphite felts are commonly used as electrodes in VFBs due to their high electrical conductivity, chemical stability, and availability.2,9 However, voltage inefficiencies arising from sluggish electrode kinetics can lead to increased overpotentials and reduced coulombic efficiency, impacting the overall energy efficiency of the battery.5,6 Therefore, it is important to investigate the electrode kinetics to better understand and improve the performance of VFBs.Various electrode treatments, including chemical, electrochemical, and thermal treatments, have been reported to have significant impacts on the carbon electrode kinetics.7-9 We have previously reported that cathodic treatment enhances the kinetics of the positive (VIV/VV) electrode but inhibits the kinetics of the negative (VII/VIII) electrode, while anodic treatment inhibits the kinetics of the positive electrode but enhances the kinetics of the negative electrode. 9-18 Other factors affecting the electrode activity include ageing of the electrode, the final treatment potential, the redox rest potential, and the treatment potential window.14,15 Recently we showed that the electrochemical activity of the VIV/VV and VII/VIII redox reactions in highly acidic vanadium electrolytes at electrochemically treated glassy carbon electrodes is also affected by the pH of the treatment electrolyte.19,20 The effect of pH of the treatment electrolyte was investigated for electrolytes from pH 0 to pH 5. The activities of carbon electrodes towards both the VIV/VV and VII/VIII redox reactions increase with increasing pH of the treatment electrolytes.In this presentation, we report detailed results on treating glassy carbon electrodes electrochemically at different pH and the subsequent effects on kinetics. The activity of variously pretreated electrodes towards both the VIV/VV and VII/VIII redox couples will be compared and contrasted, and the important differences discussed. Acknowledgements Varsha Sasikumar S P would like to thank the Irish Research Council (IRC) for a postgraduate scholarship to perform this research and acknowledges the facilitation of this research by the University of Limerick (UL) and the South East Technological University (SETU) in Ireland. References M. J. Leahy et al., Wind Power Generation and Wind Turbine Design, ed. W. Tong (WIT Press, Southampton) 661 (2010).C. Roth et al., (ed.), Flow Batteries: From Fundamentals to Applications (New York, Wiley) (2022).Z. Zhang et al., Renew. Sustain. Energy Rev., 148, 111263 (2021).M. Zarei-Jelyani et al., J. Appl. Electrochem., 54, 719 (2024).H. Agarwal et al., Adv. Sci., 11, 2307209 (2024).Z. Huang et al., ACS Sustainable Chem. Eng., 10, 7786 (2022).R. K. Sankaralingam et al., J. Energy Storage, 41, 102857 (2021).K. J. Kim et al., J. Mater. Chem. A, 3, 16913 (2015).M. A. Miller et al., J. Electrochem. Soc., 163, A2095 (2016).A. Bourke, et al., ECS Trans., 64, 1 (2014).A. Bourke et al., ECS Trans., 61, 15 (2014).A. Bourke et al., ECS Trans., 53, 59 (2013).A. Bourke et al., ECS Trans., 66, 181 (2015).M. Al-Hajji-Safi et al., ECS Trans., 109, 67 (2022).M. Al-Hajji-Safi et al., ECS Trans., 77, 117 (2017).A. Bourke et al., J. Electrochem. Soc., 163, A5097 (2016).A. Bourke et al., J. Electrochem. Soc., 162, A1547 (2015).A. Bourke et al., J. Electrochem. Soc., 170, 030504 (2023).V. Sasikumar et al., ECS Trans., 109, 107 (2022).V. Sasikumar et al., Electrochim. Acta., Under revision (2024).

Analysing policy gaps in protecting avian species from electrocution and power-line collision in Kenya

Summary As countries transition from fossil fuels to renewable energy, impacts on wildlife, particularly avian species, have become a concern. In Kenya, the effects of human-made infrastructure such as power lines and wind turbines on birds have been overlooked. To prevent further loss of biodiversity, it is necessary for infrastructure development policies to consider these impacts on birds. We aim to identify gaps in current policies by analysing the intersection of wildlife conservation and power-line infrastructure development in Kenya. Through content analysis, we evaluate the effectiveness of existing wildlife protection and energy-related policies and identify strengths and weaknesses to highlight areas for improvement. Our analysis reveals that current policies neglect threats posed by power lines and other infrastructure to birds. This oversight points to challenges such as a lack of awareness among policymakers and stakeholders and a lack of legal obligation for energy institutions to implement mitigation measures; conservationists may also face conflicts with those responsible for electricity distribution. Addressing these policy gaps is essential for effective wildlife conservation and sustainable development. This paper underscores the need to integrate wildlife conservation considerations into energy infrastructure planning to mitigate adverse impacts on avian species.

Wind turbine and PV power prediction using a deterministic data-driven model with variational mode decomposition preprocessing

This study develops a method for accurately forecasting solar radiation (SR), wind speed (WS), and air temperature (AT) for the coming 24 hours in order to predict energy production from photovoltaic (PV) panels and wind turbines (WT) in positive energy buildings. Input data are pre-processed through variational mode decomposition (VMD) for broadband feature extraction, which is then decomposed into smooth modes. The application of the Salp Swarm Algorithm (SSA) aims to optimize VMD parameters to enhance the precision of feature extraction. A thorough data analysis is performed to identify the essential input features. Residual pre-processing between input variables and their decomposed modes further enhances model performance. The stacking algorithm (SA) is used to predict both modes and residuals of the input data. Performance evaluation using metrics such as root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute error (MAE), and normalized mean absolute error (NMAE) indicates a reduction in error rates across measurement scales. For example, under adverse weather conditions, the NRMSE and NMAE for PV power are 2.50% and 1.95%, respectively.

Novel Fractional Order Differential and Integral Models for Wind Turbine Power–Velocity Characteristics

This work presents an improved modelling approach for wind turbine power curves (WTPCs) using fractional differential equations (FDE). Nine novel FDE-based models are presented for mathematically modelling commercial wind turbine modules’ power–velocity (P-V) characteristics. These models utilize Weibull and Gamma probability density functions to estimate the capacity factor (CF), where accuracy is measured using relative error (RE). Comparative analysis is performed for the WTPC mathematical models with a varying order of differentiation (α) from 0.5 to 1.5, utilizing the manufacturer data for 36 wind turbines with capacities ranging from 150 to 3400 kW. The shortcomings of conventional mathematical models in various meteorological scenarios can be overcome by applying the Riemann–Liouville fractional integral instead of the classical integer-order integrals. By altering the sequence of differentiation and comparing accuracy, the suggested model uses fractional derivatives to increase flexibility. By contrasting the model output with actual data obtained from the wind turbine datasheet and the historical data of a specific location, the models are validated. Their accuracy is assessed using the correlation coefficient (R) and the Mean Absolute Percentage Error (MAPE). The results demonstrate that the exponential model at α=0.9 gives the best accuracy of WTPCs, while the original linear model was the least accurate.

Simulation of Small-Scale Wind Turbine Performance with Taperless Blade

Utilizing wind turbines typically involves a large-scale project with extremely huge wind turbine sizes. Because of this, the use of wind turbines in Indonesia is not yet very common and widespread among the community. Basically, a wind turbine has blades; the number of blades on a wind turbine can affect the overall performance of the turbine. Large-scale wind turbine projects usually implement tapered blades, while small-scale projects use taperless blades. Therefore, in this research, a horizontal-axis wind turbine with taperless blades will be used as the object of study to find out the optimal number of taperless blades that can achieve the best performance of a small-scale wind turbine category. The method used in this research is to design a horizontal axis wind turbine equipped with taperless blades with varying numbers of blades, namely three blades, four blades, and five blades, and then simulate the turbine's performance with wind speeds of 3 m/s to 6 m/s. The present research found that the greater the number of turbine blades tends to produce greater power. This is proven by research results, where the highest power in a small-scale horizontal axis wind turbine produces a power of 20,30 Watts in a 5-blade turbine variation for the wind speed of 6 m/s.

Catch the wind: Optimizing wind turbine power generation by addressing wind veer effects.

Wind direction variability with height, known as "wind veer," results in power losses for wind turbines (WTs) that rely on single-point wind measurements at the turbine nacelles. To address this challenge, we introduce a yaw control strategy designed to optimize turbine alignment by adjusting the yaw angle based on specific wind veer conditions, thereby boosting power generation efficiency. This strategy integrates modest yaw offset angles into the existing turbine control systems via a yaw-bias-look-up table, which correlates the adjustments with wind speed, and wind veer data. We evaluated the effectiveness of this control strategy through extensive month-long field campaigns for an individual utility-scale WT and at a commercial wind farm. This included controlling one turbine using our strategy against nine others in the vicinity using standard controls with LiDAR-derived wind veer data and a separate 2.5 MW instrumented research turbine continuously managed using our method with wind profiles provided by meteorological towers. Results from these campaigns demonstrated notable energy gains, with potential net gains exceeding 10% during extreme veering conditions. Our economic analysis, factoring in various elements, suggests an annual net gain of up to approximately $700 K for a 100-MW wind farm, requiring minimal additional investment, with potential for even larger gains in offshore settings with the power of individual turbines exceeding 10 MW nowadays. Overall, our findings underscore the considerable opportunities to improve individual turbine performance under realistic atmospheric conditions through advanced, cost-effective control strategies.

A high robust control scheme of grid‐side converter for DFIG system

AbstractIn this study, a high robust control—second‐order sliding‐mode control (SOSMC) scheme is proposed to improve the DC‐link voltage dynamic performance of the grid‐side converter (GSC) for the doubly‐fed induction generator (DFIG) system under the wind turbine power disturbance and DC‐link capacitance parameter disturbance. In general, the wind speed is change with the environment and further has an effect on the power generation of the DFIG system. Besides, the capacitance of DC‐link capacitor may change with the working condition. To address this issue, a SOSMC scheme is proposed to replace the conventional proportional integral (PI) control for the DC‐link voltage controller of the GSC for the DFIG system in this study. By using the non‐linear SOSMC controller, the DFIG system is robust to the disturbance of the wind speed and the parameter of DC‐link capacitance. Compared with the conventional PI control scheme, the DFIG system with the proposed SOSMC scheme is much more robust, which has been verified in the MATLAB/Simulink platform.

Investigation of the Features Influencing the Accuracy of Wind Turbine Power Calculation at Short-Term Intervals

The accurate prediction of wind power generation, as well as the development of a digital twin of a wind turbine, require estimation of the power curve. Actual measurements of generated power, especially over short-term intervals, show that in many cases the power generated differs from the calculated power, which considers only the wind speed and the technical parameters of the wind turbine. Some of these measurements are erroneous, while others are influenced by additional factors affecting generation beyond wind speed alone. This study presents an investigation of the features influencing the accuracy of calculations of wind turbine power at short-term intervals. The open dataset of SCADA-system measurements from a real wind turbine is used. It is discovered that using ensemble machine learning models and additional features, including the actual power from the previous time step, enhances the accuracy of the wind power calculation. The root-mean-square error achieved is 113 kW, with the nominal capacity of the wind turbine under consideration being 3.6 MW. Consequently, the ratio of the root-mean-square error to the nominal capacity is 3%.

Improved Sliding Mode Control of a Wind Turbine System Based on a Developed Permanent Magnet Synchronous Generator

Wind energy conversion systems (WECS) are emerging as among the most reliable alternative energy sources, requiring innovative control strategies to improve performance and reduce operating costs. The objective of this paper is the implementation of sliding mode control (SMC) for a permanent magnet synchronous generator (PMSG) to optimize the dynamical response and wind turbine system (WTS) stability. The control architecture comprises the PMSG, an AC-DC power converter, and a DC bus link, with a particular focus on extracting the optimum wind turbine power during variable wind speeds. A complete system model was developed and tested in MATLAB for a PMSG of 2 MW subjected to uncertain fluctuations in wind speed. The simulation results confirm that the SMC technique significantly improves speed-tracking accuracy, minimizes current ripple, and improves overall system stability. In addition, the proposed method ensures robust performance under non-linear and uncertain conditions, making it a viable solution for large-scale wind applications. These results highlight the potential of SMC to enhance the accuracy and efficiency of the WECS.

Effects of Inflow Turbulent Coherent Structures on a Small-Scale Wind Turbine in the Atmospheric Boundary Layer of Long Corridor Terrains: An Example Study in the Hexi Corridor

Considering its Venturi effect, long corridor terrain is considered as one of the outstanding wind-energy resources, but has not been developed due to unclear inflow turbulence effects on wind turbines. To advance the wind-power technology in long corridors and reveal the physics behind it, we conducted a series of data-fusion analyses combining field measurements in the Hexi Corridor, Gansu, China, and numerical simulations and summarize the effort here. In this study, the classical spectrum for the atmospheric boundary layer for wind-energy research, the IEC Kaimal spectrum (also known as IECKwind, International Elector technical Commission 61400-1) cannot describe the wind profile in corridors. Here, we proposed a new spectrum to describe the wind in the Hexi Corridor by modifying the IEC Kaimal spectrum, and we call it the IECK-HX spectrum. The results demonstrate that it preferentially resides at lower frequencies (larger spatial scales) compared with IECKwind. Large-scale motions (LSMs) and very large-scale motions (VLSMs) coexist in the Hexi Corridor atmospheric boundary layer, which can be well described by the IECK-HX spectrum, and these two dominate the large fluctuations in the power, thrust, and blade root flapwise moment of wind turbines, while the high-frequency small-scale coherent structures mainly cause high-frequency fluctuations in wind-turbine load. Furthermore, IEC Kaimal spectrum can not describe the VLSMs, and the generated flow presents significant fluctuations and thereby reduces the power output in high wind-speed regions. This study provides fundamental support for wind-energy scientists and engineers who are interested in wind energy in corridor terrains. Published by the American Physical Society 2024

Assessing wind energy exploitation potential in several regions of Viet Nam using Kernel density estimation model

This article analyzes and assesses the potential for wind energy exploitation in six regions of Vietnam. The wind speed data are used to construct wind speed probability distributions (WSPDs) based on kernel density estimation (KDE). The KDE distribution, with six bandwidth selection methods, is implemented to generate probability density functions (PDFs) for each region's data to describe wind speed characteristics. The statistical tests Cramér-Von Mises (CvM), Anderson-Darling (A-D), and Kolmogorov-Smirnov (K-S) are applied to evaluate the PDFs' goodness-of-fit performance. The analysis results present the KDE distribution using the least-squares cross-validation (LSCV), and the Scott bandwidth selection method has outstanding fitting performance. Based on these PDF distributions, the wind turbine (WT) power curve is used to estimate and predict the amount of electricity that can be produced. This study also proposes a reliable method for wind power output planning based on wind speed that can be universally applied.

Optimization of the Small Wind Turbine Design—Performance Analysis

In recent decades, the intensive development of renewable energy technology has been observed as a great alternative to conventional energy sources. Solutions aimed at individual customers, which can be used directly in places where electricity is required, are of particular interest. Small wind turbines pose a special challenge because their design must be adapted to environmental conditions, including low wind speed or variability in its direction. The research study presented in this paper considers the energy efficiency of a small wind turbine with a horizontal axis of rotation. Three key design parameters were analyzed: the shape and inclination of the turbine blades and additional confusor–diffuser shape casings. The tests were carried out for three conceptual variants: a confusor before the turbine, a diffuser after the turbine, and a confusor–diffuser combination. Studies have shown that changing the shape of the blade can increase the analyzed wind turbine power by up to 35%, while changing the blade inclination can cause an increase of up to 16% compared to the initial installation position and a 66% increase in power when comparing the extreme inclination of the blades of the tested turbine. The study has shown that to increase the wind speed, the best solution is to use a confusor–diffuser configuration, which, with increased length, can increase the air velocity by up to 21%.