Wind Turbine Research Articles

Studying and analysis of deformation and stresses in horizontal-axis wind turbine using fluid-structure interaction method

Fluid-structure interaction (FSI) studies have become an important tool in the development of wind turbine blades as well as for analyzing and optimizing Horizontal Axis Wind Turbine (HAWT). The most essential elements for estimating the turbine blade strength to withstand extreme wind loads and aerodynamic performance are the blade deformation and the associated stresses. This study aims to compare the strength and analyze the deformation behavior of two models of HAWT blades. A three-dimensional model was created and loaded into a Finite Element (FE) model for analysis. The Computational Fluid Dynamics (CFD) investigation was coupled with the structural model using one-way FSI. In this article, the effect of the aerodynamics on the blade surface of a small HAWT has been studied numerically and experimentally. The airfoil used for the blade profile is S826 airfoil. To provide an appropriate displacement for static measurements, the blade in the experimental investigation is made of thermoplastic polyurethane material (TPU). It is noted that the current experimental findings verify the simulation results, and a comparison between the simulations and the experimental findings reveals a reasonable agreement. The numerical simulations are also implemented on a HAWT epoxy E-glass unidirectional (EEGUD) material. It can be concluded from this study that the blade deformation and stress on the blades are related to the wind speed. The deformation along the span length exhibits nonlinear variation that gradually increases from the blade root and reaches its maximum at the blade tip position. The tip deflection and equivalent stress were found to increase with an increase in wind speed. The dynamic analysis at different tip speed ratios shows the highest deformation at λ = 4, for wind speed of 20 m/s.

Generación renovable eólica y fotovoltaica en Ecuador: Una revisión sistemática de literatura

INTRODUCTION. Photovoltaic and wind renewable generation has grown exponentially in recent years worldwide, in Ecuador this technology has not been the exception, as several photovoltaic and wind power plants have been installed, increasing the generation of renewable energy. OBJECTIVE. This article presents a systematic review of the literature on operational photovoltaic and wind power plants in Ecuador, offering a perspective on the renewable sources connected to the national energy system. METHOD. Through a bibliographic search in the Scopus database, 8 publications were obtained that met the eligibility criteria, in addition to reports issued by government entities, to ensure that the information and technical aspects are accurate. RESULTS. The documentary analysis allows us to know the current status of photovoltaic and wind power plants, identifying their installed capacity, technology used, and operational characteristics. DISCUSSION AND CONCLUSIONS. The information collected provides a solid basis for understanding the current status and development prospects of photovoltaic and wind power generation in Ecuador. Despite certain limitations related to the availability of data in open sources, it was possible to obtain significant information on the main renewable plants in the country. These results serve as a basis for future research aimed at the expansion and optimization of renewable energies in the national context..

Performance Evaluation of Small Wind Turbines Under Variable Winds of Cities: Case Study Applied to an Ayanz Wind Turbine with Screw Blades

Small wind turbines placed at city locations are affected by variable-speed winds that frequently change direction. Architectural constructions, buildings of different heights and abrupt orography of Cities make the winds that occur at City locations more variable than in flat lands or at sea. However, the performance of Small-wind turbines under this type of variable wind has not been deeply studied in the specialised literature. Therefore, this article analyses the behaviour of small wind turbines under variable and gusty winds of cities, also considering three types of power electronics conversion configurations: the generally used Maximum Power Point Tracking (MPPT) configuration, the simple only-rectifier configuration and an intermediate configuration in terms of complexity called pseudo-MPPT. This general-purpose analysis is applied to a specific type of wind turbine, i.e., the Ayanz wind turbine with screw blades, which presents adequate characteristics for city locations such as; safety, reduced visual and acoustic impacts and bird casualties avoidance. Thus, a wide simulation and experimental tests-based analysis are carried out, identifying the main factors affecting the maximisation of energy production of small wind turbines in general and the Ayanz turbine in particular. It is concluded that the mechanical inertia of the wind turbine, often not even considered in the energy production analysis, is a key factor that can produce decrements of up to 25% in energy production. Then, it was also found that electric factors related to the power electronics conversion system can strongly influence energy production. Thus, it is found that an adequate design of a simple pseudo-MPPT power conversion system could extract even 5% more energy than more complex MPPT configurations, especially in quickly varying winds of cities.

CFD simulation and experimental investigation of a Magnus wind turbine with an improved blade shape

With the advancement of technology and growing concern for environmental issues, the demand for energy derived from renewable sources is expected to increase. It is well established that traditional bladed wind turbines exhibit inefficiency in low wind speed conditions. To address this, Magnus wind turbines, featuring cylindrical blades, have been developed specifically for regions with predominantly low-speed winds. However, these turbines present a significant drawback, as they require electric drives to rotate the blades. This study aims to conduct a three-dimensional numerical and experimental investigation of the aerodynamics around a wind turbine with a geometrically complex blade design. The scientific contribution of this work lies in the introduction of a deflector to the cylindrical blades, intended to eliminate the need for an electric drive to initiate blade rotation. A laboratory prototype of a wind turbine with the proposed blade configuration was constructed, and numerical simulations were performed using Ansys Fluent, based on the Reynolds-averaged Navier—Stokes (RANS) equations. The results demonstrate that the turbine's power coefficient (Cp) is approximately 20 % higher than that of traditional Magnus wind turbines. These findings underscore the enhanced efficiency provided by the addition of a deflector to the cylindrical blades.

Observer based finite-time adaptive fractional-order sliding mode control of DFIG wind turbines with unknown dynamic parameters

In this study, we investigate adaptive finite-time fractional-order sliding mode control (AFFSMC) of DFIG wind turbines (DWTs) under model uncertainty in finite time. A fractional-order sliding surface is first constructed to develop the concept. A robust terminal adaptive sliding mode controller is then designed for pitch angle control of wind turbine to achieve finite-time stability at high wind speeds to regulate the produced electric power in Zone III of the turbine’s working range. This is all with this assumption that there is no any information about the dynamics of the turbine due to the significant uncertainties. Through the use of a stable adaptive law, the vector of the wind turbine’s unknown dynamic parameters is approximated in this method. An observer is also proposed to estimate both turbine speed and external disturbances term. The closed-loop system’s finite-time stability study and the finite-time convergence of the turbine output were both carried out using the dynamic model of the system and the proposed Lyapunov theory. Some numerical examples are also presented together with a comparison to the standard integer sliding mode control to demonstrate the correctness of the control law.

A tri-level hybrid stochastic-IGDT dynamic planning model for resilience enhancement of community-integrated energy systems

In this paper, a resilience-oriented dynamic planning framework is developed for optimal sizing of the community-integrated energy system components, including photovoltaic system, wind turbine, boiler, power to gas technology, combined heat and power, and storage devices. The proposed framework is formulated as a tri-level linear programming that performs Community-Integrated Energy Systems design under normal conditions at the first level while evaluating system operation during disastrous conditions at the second level. In the third level, re-planning is done based on information-gap decision theory to enhance community-integrated energy systems’ resilience against various natural disasters. Stochastic programming is also employed at all levels to address the uncertainty of electricity market price, energy demand, solar radiation, and wind speed. A detailed P2G system including, a methanation device, electrolysis, and hydrogen storage is designed to improve the resilience of the system. In addition, the power to gas proposed in this model is coupled with a carbon capture unit to mitigate carbon emission by reusing emitted carbon from the flue gas of the boiler and combined heat and power. Various economic metrics and technical constraints are also considered to achieve a realistic design. Numerical simulation results demonstrate that the positive interplay of renewable energy resources and energy storage technologies, specifically P2G, assisted the CIES in maintaining a stable and uninterrupted energy supply during extreme events. The results exhibit that increasing only 10 % of the resilience budget can decrease >93 % of unserved demand and helps reduction of >37 % of carbon emissions.

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.

Static analysis of wind blades based on weight loads to the gravitation and the centrifugal forces Using Finite Element

Static analysis of wind turbine blades that convert kinetic energy using wind to electrical power has been analyzed using Ansys model. The geometry was imported in ANSYS workbench 2020R1, where the material was changed to Epoxy E-Glass Wet. The mesh independence that occurred at 800 and 600 element sizes and an element size of 800 was chosen for subsequent static analysis. The static analysis shows that the total deformation and von Mises stress of the wind turbine blade was improved at both the vertical position and the horizontal position by 40.0% and 38.61%, respectively. Besides, the static analysis showed an almost linear mode shape increased from mode lines. Nevertheless, Campbell's diagram showed that resonance vibration occurred at an amplitude of 5.5x106Pa with a corresponding frequency of 5Hz, which gave a rotational speed of 262.4x106 rpm. This shows that the impulse force is better applied in wind turbine blade transient analysis at a lower von Mises stress to attain a quicker damping response steady-state, which favors wind turbine blade analysis. Overall, the wind turbine blade is better designed at a vertical position to allow better deformation stress at a static position. Conversely, the von Mises stress showed 44.64% and 38.61% at horizontal and vertical positions. This also agrees with static total deformation, since stress is better managed at a lower percentage range.

Performance of wind turbine system based on a brushless doubly fed reluctance generator and a six pulse bridge cycloconverter

In this paper, a variable speed wind energy conversion system (WECS) made with a six pulse bridge cycloconverter and a brushless doubly fed reluctance generator (BDFRG) is investigated. The novelty of the proposed work is to use a brushless doubly fed reluctance generator and a six pulses cycloconverter for working in high power and for reducing the cost, also the proposed work use a maximum power point tracking algorithm to capture the optimal power available in the wind in order to increase the efficiency of the system. The power control of this system is applied, to capture the maximum power from the wind by using a maximum power point tracking (MPPT) control technique, to achieve the independent control of the active and reactive powers exchanged between the wind generator and the grid, also to test the possibility to operate in two quadrant modes (subsynchronous and supersynchronous modes). The dynamic modeling equations that describe the suggested system are presented here. The results of the simulation are shown in order to illustrate the performance and efficiency of this system.

Shear behavior at the interface of rough surfaces and cohesive soils under axial loading

Foundation elements with rough (textured) surfaces mobilize larger interface shear resistance than ones with conventional smooth or random rough surfaces when sheared against soils under monotonic loading. The overall performance of foundation elements such as piles supporting offshore wind turbines, suction caissons supporting tidal energy converters, soil nails, and soil anchors installed in cohesive soils could be enhanced through utilizing rough (textured) surfaces to resist applied static and/or cyclic loading. This paper describes the shear behavior of smooth and rough (textured) surfaces in kaolinite clay and kaolinite clay-sand mixture soils under static and cyclic axial loading. The experimental investigation presented herein consists of a series of interface shear tests performed on 3D printed rough (textured) surfaces and a 3D printed smooth reference surface utilizing the Cyclic Interface Shear Test system. The paper includes a description of the interface testing system components, cohesive soil specimens’ preparation procedure, smooth and rough (textured) surfaces details, testing procedure, and results of static and cyclic tests. Test results indicate that kaolinite clay-sand mixture soil mobilized larger static and post-cyclic interface shear resistance and volume contraction relative to kaolinite clay soil when sheared against the smooth reference surface. When tested against rough (textured) surfaces with variable asperity height, larger shear resistance was mobilized and larger soil dilation greater than that mobilized by the reference untextured surface in both soils. The results also indicate rough (textured) surfaces exhibited a prevalent frictional anisotropy increases with asperity angle and height in cohesive soils, the surfaces mobilized larger shear resistance and volume change in one direction (i.e., against the asperity right-angled side) than the other direction (i.e., along the asperity inclined side).

Multi-objective optimal control of a hybrid offshore wind turbine platform integrated with multi-float wave energy converters

Offshore floating wind turbines will play a pivotal role in achieving net-zero emissions. This study explores a hybrid platform combining an offshore floating wind turbine with wave energy converters (WECs) from an active control perspective to mitigate turbine damage risk while maximizing wave energy conversion. Initially, the control-oriented state-space modelling of the hybrid platform and the validation of the time-domain Cummins-type models of different orders are performed by the Eulerian-Lagrangian method with model linearization and downscaling. Then, a multi-objective non-causal optimal controller is introduced, balancing wave energy capture and penalizing the nacelle acceleration. When wave energy capture maximization is set as the control target, the proposed optimal controller can improve energy output by 184% as compared against a well-tuned passive damper across all peak periods tested. However this causes peak hub acceleration to increase marginally beyond the desirable limits of 3–4 m/s2 for wind turbine operation. When hub acceleration is penalized, the multi-objective optimal controller can reduce peak values below limits by between 40% and 61% with trivial loss of wave energy capture.

Selected social impact indicators influenced by materials for green energy technologies

The social risks of green energy transition are underexplored. One of the important questions is which materials used in green energy technologies offer the greatest social benefits, such as ensuring decent living conditions, and which pose the most social risks. To address this issue, we develop a dynamic material-energy flow model integrating system dynamics, social life cycle assessment, and geometallurgical approaches. The analysis focuses on critical materials: Rare Earth Elements, Nickel, Silicon, Graphite, Magnesium, Gallium, Germanium, Indium, Aluminum, Cobalt, Lithium, Zinc, and Tellurium used in wind turbines, electric vehicles, lithium-ion batteries and solar photovoltaic panels. We assess their social impact on work safety, gender equality, informal employment, labor income share, employment rate, and child labor—key issues addressed by Sustainable Development Goals 1, 5, and 8. Here we show that Aluminum production for electric vehicles, wind turbines and solar photovoltaic panels generates the most jobs and income opportunities, while extraction of Cobalt, Lithium, Silicon, and Zinc carry the highest social risks.

Full-scale wind turbine performance assessment using the turbine performance integral (TPI) method: a study of aerodynamic degradation and operational influences

Abstract. This study investigates how blade aerodynamic modifications, including leading edge roughness (LER), influence wind turbine performance over their operational lifespan. It introduces a methodology developed to examine the intricate relationship between blade erosion, blade enhancements, operations and maintenance (O&M) events, control programmable logic controller (PLC) parameter updates, and their cumulative impact on turbine efficiency. Analysing data from 12 multi-megawatt offshore turbines over a 12-year period, the investigation hinges on the integration of supervisory control and data acquisition (SCADA) data, O&M records, and air density corrections. A key contribution is the development of the turbine performance integral (TPI) method, which, for the investigated turbines, leverages generator speed and power output data to track performance trajectories. Seasonal trend decomposition using locally estimated scatterplot smoothing (STL) further isolates long-term trends and seasonal variations in performance. Despite data availability and quality limitations, the study reveals significant findings concerning the impact of manufacturer software updates on turbine control strategies, resulting in improved performance; the variable effects of blade repairs and enhancements; and the complex interaction between O&M events and performance. This work applies a methodical approach and statistical rigour, offering a path forward for effectively monitoring wind turbine efficiency and advancing renewable energy.

Hybrid sine cosine and spotted Hyena based chimp optimization for PI controller tuning in microgrids

In this paper, a novel hybrid sine-cosine and spotted Hyena-based chimp optimization algorithm (hybrid SSC) is adopted for the precise tuning of proportional-integral (PI) controllers in a microgrid system. The microgrid integrates multiple renewable energy sources, including photovoltaic (PV) panels, wind turbines, a fuel cell, and a battery storage system, all connected to a common DC bus. This DC bus interfaces with the main grid through a voltage source converter (VSC). The microgrid comprises a total of eight PI controllers distributed across various components: the boost converter in the wind system, the fuel cell system, the battery energy storage device, and the VSC controller. The hybrid SSC optimization algorithm effectively combines the exploration capabilities of the sine-cosine algorithm (SCA) with the exploitation strengths of the spotted Hyena optimizer (SHO) and Chimp optimization algorithm (ChOA), aiming to achieve optimal tuning of the PI controllers. This hybrid approach ensures an enhanced dynamic response and overall system performance by minimizing the integral of the time-weighted squared error (ITSE) for each controller. The simulation results, directed in a MATLAB/SIMULINK environment, demonstrate the efficacy of the hybrid SSC algorithm in improving the stability, response time and efficacy of the microgrid. The proposed technique significantly outperforms traditional tuning techniques, ensuring robust operation and seamless addition of renewable energy sources with the main grid. This study contributes to the advancement of intelligent control strategies for modern microgrids, emphasizing the importance of hybrid optimization algorithms in achieving optimal performance in complex energy systems.

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%.

Double-pancake superconducting coil integrated with DFIG-based wind power systems under voltage-dip events: Design and performance analysis study

The doubly fed induction generator (DFIG)-based wind turbine provides massive development in power generation. Uncertain power oscillations and voltage-dip events affect the stability of the DFIG. To mitigate the effect of voltage-dips in short period, superconducting magnetic energy storage (SMES), which possess rapid energy exchange and high power density, is integrated with large-scale renewable power systems. SMES is integrated at the DC link of the DFIG to ensure the power balance and fault ride-through (FRT) capability. During this operation, frequent current changes occur in the high-temperature superconducting (HTS) coil, which leads to an increase in AC losses. As a remedy, an additional power converter structure controlled with a model-predictive controller (MPC) is proposed. Firstly, a scale-down HTS double-pancake coil model is designed using a finite element model by which transport current loss and magnetic field formulation are studied. Additionally, the critical coil current is also verified through experimental studies. The effectiveness of the proposed MPC-based SMES integrated DFIG on DC link voltage and grid current under IEC-61000-4-11 standard, symmetrical and asymmetrical voltage-dip cases are investigated. The obtained results demonstrate that the proposed system effectively reduces transients in DC link voltage and improves the FRT capabilities of the grid.

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.

Machine learning based parameter estimation for an adapted finite element model of a blade bearing test bench

Improving the reliability of blade bearings is essential for the safe operation of wind turbines. This challenge can be met with the help of virtual testing and digital-twin driven condition monitoring. For such approaches, a precise digital representation of the blade bearing and its test bench is an essential prerequisite. However, various factors prevent the capture of all parameters of the blade bearing and the associated test bench. Parameters such as bearing preload, rolling element and raceway dimensions, and bolt preload during assembly vary with each bearing and test bench setup. As these parameters cannot be measured directly, an alternative solution is required. This article presents a methodology to efficiently estimate non-measurable parameters of the test bench using a combination of model-based and data-driven approaches, improving the detailed and accurate virtual testing of blade bearings. It must be ensured to enable the fastest possible, most computationally efficient estimation of parameters during virtual testing or condition monitoring. The developed methodology is evaluated using the example of bolt preload on the test bench. By employing a random forest model and the strain gauge measurements attached to the blade bearing, the bolt preload parameters are estimated. The results demonstrate that the accuracy of the digital model of the blade bearing test bench is improved by up to 11 % in three out of four test bench setups. The great improvement in the accuracy of the digital model highlights the effectiveness of the proposed methodology in enhancing virtual blade bearing testing and digital-twin driven condition monitoring.

LiDAR-Based Unmanned Aerial Vehicle Offshore Wind Blade Inspection and Modeling

The deployment of offshore wind turbines (WTs) has emerged as a pivotal strategy in the transition to renewable energy, offering significant potential for clean electricity generation. However, these structures’ operation and maintenance (O&M) present unique challenges due to their remote locations and harsh marine environments. For these reasons, it is fundamental to promote the development of autonomous solutions to monitor the health condition of the construction parts, preventing structural damage and accidents. This paper explores the application of Unmanned Aerial Vehicles (UAVs) in the inspection and maintenance of offshore wind turbines, introducing a new strategy for autonomous wind turbine inspection and a simulation environment for testing and training autonomous inspection techniques under a more realistic offshore scenario. Instead of relying on visual information to detect the WT parts during the inspection, this method proposes a three-dimensional (3D) light detection and ranging (LiDAR) method that estimates the wind turbine pose (position, orientation, and blade configuration) and autonomously controls the UAV for a close inspection maneuver. The first tests were carried out mainly in a simulation framework, combining different WT poses, including different orientations, blade positions, and wind turbine movements, and finally, a mixed reality test, where a real vehicle performed a full inspection of a virtual wind turbine.

Control Structures for Combined H2/Electricity from Offshore Wind Turbines

Wind energy proves to be a highly favourable choice for electricity generation due to its clean and renewable nature, and is playing a significant role in reducing global greenhouse gas emissions. Offshore wind turbine systems have gained widespread popularity as they can capitalise on elevated and consistent wind speeds surpassing those found in onshore locations, resulting in increased energy efficiency. Furthermore, offshore wind power possesses the potential to emerge as a significant electricity source for the production of green hydrogen. As water electrolysis technology for hydrogen production continues to advance, utilizing offshore wind power for hydrogen generation is becoming more economically viable and practical. Offshore wind power with higher wind speeds in combination with efficient control structures presents an attractive option for electricity generation and hydrogen co-production. This paper aims to present and evaluate four different production structures for combined H2/energy generation from offshore wind turbines. Previous research studies in this area often overlook control structures and lack information on power converter operations. In contrast, this article studies control structures that enable proper functionality and ensure adequate interoperability, enhancing the reliability of renewable energy integration. Each structure, including both wind turbines and electrolyser, is described in detail, along with the corresponding controllers. Simulation results are presented for each structure and controller to demonstrate their effective operation.