Wind Turbine Research Articles

The impact of extreme wind conditions and yaw misalignment on the aeroelastic responses of a parked offshore wind turbine

As global energy demands rise and the urgent need to reduce carbon emissions, offshore wind energy technology has evolved rapidly, becoming a vital component of the world’s energy portfolio. However, offshore wind turbines face new challenges due to complex marine environments. Under extreme conditions such as strong winds and yaw misalignment, parked wind turbines exhibit complex aerodynamic and structural responses that significantly differ from those in normal operating conditions. These responses, crucial to the integrity and safety of wind turbines, demand thorough analysis. This article presents an in-depth aeroelastic analysis of a parked 5 MW offshore wind turbine using a sophisticated and precise numerical model. It explores the impacts of incoming wind speed and yaw misalignment on the aerodynamic loads and structural behaviors of the wind turbine. The findings indicate that gravity plays a significant role in blade structural responses for a parked wind turbine. Additionally, higher wind speeds elevate aerodynamic forces, leading to more pronounced fluctuations in root forces and tip deflections. The increase in wind speed triggers and exacerbates high-frequency edgewise vibrations of the blade, further leading to fluctuations and instability in the loads. The study also reveals that yaw misalignment causes the AOA changes significantly over time, and generates intermittent fluctuations in lift and drag, leading to extra blade vibrations with the maximum amplitude exceeding 10% of the blade length. This research enhances the current understanding of the operational safety of offshore wind turbines, particularly under extreme conditions. It aims to provide valuable insights and guidance for researchers and developers in designing and monitoring the performance of offshore wind turbines, ensuring their resilience and efficiency in challenging environments.

Adaptive fuzzy coordinated control design for wind turbine using gray wolf optimization algorithm

Due to the randomness and intermittency of wind speed, the actual output power curve of a wind turbine (WT) deviates greatly from the theoretical power curve, thereby reducing the power generation capacity of the WT. An adaptive fuzzy coordinated control (AFCC) of WT is presented in this study to improve the power generation of WT. Firstly, a multi-objective optimization model (MOOM) for WT output power, generator speed and pitch angle is established, and its optimal solution set is used as the input eigenvector of a novel effective wind speed soft sensor (NEWSSS) model, which is modeled with kernel extreme learning machine (KELM). Secondly, a novel improved gray wolf optimization (NIGWO) algorithm is presented by improving the convergence factor and adaptive weights, which is used to solve MOOM and optimize the parameters of KELM. A variable pitch control (VPC) is designed by estimating the effective wind speed. Finally, an adaptive fuzzy control (AFC) is presented for WT. Based on the AFC and VPC, an AFCC for pitch angle and generator torque is designed for WT. The high measuring precision of NEWSSS and the good robustness and dynamic performance of AFCC are demonstrated by the simulation results.

Wind Turbine Bearing Failure Diagnosis Using Multi-Scale Feature Extraction and Residual Neural Networks with Block Attention

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced fault diagnosis method named the two-stream feature fusion convolutional neural network (TSFFResNet-Net). Firstly, the proposed method combines the advantages of one-dimensional convolutional neural networks (1D-ResNet) and two-dimensional convolutional neural networks (2D-ResNet). It transforms one-dimensional vibration signals into two-dimensional images through the empirical wavelet transform (EWT) method. Then, parallel convolutional kernels in 1D-ResNet and 2D-ResNet are used to extract multi-scale features, respectively. Next, the Convolutional Block Attention Module (CBAM) is introduced to enhance the network’s ability to capture key features by focusing on important features in specific channels or spatial areas. After feature fusion, CBAM is introduced again to further enhance the effect of feature fusion, ensuring that the features extracted by different network branches can be effectively integrated, ultimately providing more accurate input features for the classification task of the fully connected layer. The experimental results demonstrate that the proposed method outperforms other traditional methods and advanced convolutional neural network models on different datasets. Compared with convolutional neural network models such as LeNet-5, AlexNet, and ResNet, the proposed method achieves a significantly higher accuracy on the test set, with a stable accuracy of over 99%. Compared with other models, it shows better generalization and stability, effectively improving the overall performance of rolling bearing vibration signal fault diagnosis. The method provides an effective solution for the intelligent fault diagnosis of wind turbine rolling bearings.

Maximum power point tracking control of wind turbines based on speed hysteresis loop to reduce drive-train loads

By adding compensation torque based on the optimal torque, the torque compensation control method expands the unbalanced torque at varying wind speeds, thereby improving the maximum power point tracking (MPPT) performance of wind turbines (WT). However, while improving the wind energy capture efficiency, such methods will lead to drastic fluctuations of generator torque, resulting in significantly increased drive-train loads. To solve this problem, based on the amplitude-frequency characteristics analysis of the WT system transfer function, it is found rotor speed information can not only reflect the main trend of wind speed changes, but also has the characteristics of not being easily affected by the high-frequency component of wind speed. Therefore, setting compensation torque according to it can effectively alleviate the above phenomenon. On this basis, the MPPT control of WT based on speed hysteresis loop to reduce drive-train loads is proposed. The speed information is used to replace the torque information to set the compensation torque amplitude term, and the speed hysteresis loop is introduced to improve the design of the compensation torque symbol term, which can significantly decreased drive-train loads without compromising on power capture. Finally, the effectiveness of the proposed method is verified by the experiments.

Damage evolution simulation and lifetime prediction for composite blades under continuous droplet impacts

Offshore wind power generation is a promising technology in renewable energy applications due to its high reserves of wind energy in sea areas. To improve the energy transformation efficiency, the blade length of the offshore wind turbines has become larger and larger, and it has made rain erosion be one of the most frequent failures during the turbine operation. As the natural rainfall is stochastic in spatial and time domains, it is difficult to depict the damage evolution process caused by rain impact exactly. Therefore, regular and continuous droplet impact simulation and experiments present an alternative methodology for this issue. With the composite structure of the blade and deformability of the liquid droplet in consideration, a fluid solid interaction model will be established to investigate the impact response and subsequent damage evolution. In which, the Smooth Particle Hydrodynamics (SPH) model is utilized to depict the constitutive relationship within the droplet, and Finite Element Method (FEM) is used to construct the Representative Volume Element (RVE) model of the blade leading edge. The impact process is simulated first to obtain the impact pressure distribution at the contact center and velocity field in the droplet. Furthermore, the stress wave propagation in the blade multilayer structure can be analyzed. Owing to the multiaxial fatigue feature of the continuous droplet impact, the continuum damage mechanics is integrated with the fatigue criterion and the Jump-in-Cycle procedure is used to simulate the high-cycle fatigue process. The damage factor distribution on the blade coating surface and its influence on mechanical properties are analyzed. Thereafter, the droplet impact fatigue life can be accumulated based on Miner’s linear rules. The theoretical achievements are validated by experimental data provided by Rain Erosion Testing (RET), which shows a good agreement between each other. As a result, V-N curves and D-N curves, i.e. quantitative relationship between droplet falling conditions and impact fatigue life, are established. The achievements in this study can provide an effective tool for rain erosion mechanism analysis and life prediction in industrial applications.

Flexible design of renewable hydrogen production systems through identifying bottlenecks under uncertainty

Renewable hydrogen production technology within the energy sector stands as a vital avenue towards decarbonization in process industries. However, for systems characterized by high penetration of renewable energy, the challenges posed by uncertain parameters impacting stable production present significant obstacles to the large-scale application of hydrogen production from renewable energy. To address the issues, this work proposed a comprehensive four-step strategy for the bottleneck identification and optimization of the renewable hydrogen production systems (RHPS), which encompasses four models including system configuration optimization, flexibility analysis, bottleneck identification, and debottlenecking models. The effectiveness of the proposed strategy is substantiated through a case study. The results indicate that positive fluctuations on supply side and negative fluctuations on demand side contribute to bolstering the flexibility of RHPS. When the RHPS possesses sufficient flexibility, the critical fluctuation amplitude for photovoltaic power is −36 %, whereas for wind turbine power, it stands at −32 %. It highlights that when the photovoltaic power fluctuates, the ability of RHPS to maintain its own stability is stronger than when the wind turbine power fluctuates. Furthermore, when the fluctuations of supply side are positive, the rate of change in critical fluctuation amplitudes on the supply side is 4.63 times than that of the demand side. This underscores that the power decline in the supply side has a more pronounced detrimental effect on the stability of RHPS than an increase in the demand side. The proposed strategy for optimizing RHPS offers valuable theoretical insights for the system design and retrofitting under uncertainty.

Sensorless DFIG System Control via an Electromagnetic Torque Based on MRAS Speed Estimator

The main goals of this research are to develop a method for obtaining the rotor position and speed in a doubly fed induction generator (DFIG) without using sensors in a variable-speed wind turbine installation. The considered method is based on the Model Reference Adaptive System (MRAS). According to this method, electromagnetic torque is used as an error variable for the adaptation process in order to refine the estimate. A good assessment is very important when trying to put into place any strategy that can control the behavior of a DFIG. This method of estimation functions by comparing the actual performance of the DFIG with that of a reference model and adjusting the system parameters to reduce any mismatch between the two. One notable advantage of this developed estimator is its stability across a broad range of speeds. Additionally, it is designed to exhibit resilience in the face of uncertainties in machine parameters. The proportional integral (PI) gains for the MRAS estimator are determined via pole placement. To assess and validate the entire DFIG model and the sensorless estimation method, comprehensive simulations are carried out using MATLAB/Simulink.

A framework for technico-environmental optimization of small wind turbines

Small Wind Turbines (SWTs) can provide energy access and participate to energy transition by enabling local electricity production at a low environmental cost. However, there is a lack of comprehensive approaches to address the challenge of reducing the electricity footprint of SWT systems. In this article, a technico-environmental framework is proposed to enable the optimization of the environmental footprint of the electricity provided by a SWT system. The compromise between energy harvest and environmental impacts is studied by establishing and coupling life-cycle and energy models taking into account the system configuration (grid-tied, off-grid), design parameters (tower height, battery sizing), the site characteristics (roughness length, wind resource), and the user behaviour aspects (load-shifting). For considered scenarios, the results indicate that the tower height that minimizes the footprint of the provided electricity is identical for grid-tied and off-grid systems. It is in the range of 12–24 m for roughness lengths (0.1–0.5 m) and heavily depends on roughness length and wind resource. Interestingly, increasing the load shifting from 20% to 80% could help reduce by a factor two the footprint of the provided electricity in grid-tied configuration. Following these results, some recommendations are proposed regarding the scaling of the tower and the load shifting. Some perspectives are also given to improve the technico-environmental framework. The data and the source codes used in this article are provided on a public repository.

A preliminary analysis of long-term self-reported sleep disturbance attributed to wind turbines and modelled outdoor nightly average wind turbine sound pressure level

The World Health Organization (WHO) Environmental Noise Guidelines provide source-based annual average nighttime sound pressure level (Lnight) recommendations. For non-aircraft sources the recommendation for self-reported high sleep disturbance (HSD) is the Lnight associated with an absolute prevalence of 3%HSD. The Guideline Development Group (GDG) reported that no Lnight recommendation could be provided for wind turbines because the evidence available was inadequate. In the current study, outdoor wind turbine Lnight at each dwelling was calculated following international standards. Questionnaires were completed between May and September 2013 by individuals aged 18-79y (606 males, 632 females), randomly selected from households 0.25 to 11.22 kilometers from operational wind turbines. Calculated Lnight ranged from <25 dB(A) to 46 dB(A). When the source of sleep disturbance was unspecified, the mean prevalence of HSD was 13.3% overall and unrelated to Lnight (p = 0.53). As Lnight increased, the prevalence of identifying wind turbines as one of the causes of sleep disturbance increased from 0% below 25 dB(A) to 3.8% (95% CI 1.9% to 7.4%) between 40-46 dB(A) (p = 0.01) . The WHO's 3%HSD threshold was estimated to occur for wind turbine Lnight between 38-39 dB(A).

Acoustic evaluation: microphone array measurements on a multi-megawatt individual pitch control wind turbine

As rotor diameters of wind turbines approach 200 m, aerodynamic noise becomes a significant issue, especially for onshore turbines. The rapid expansion of wind energy has led to increased concerns regarding the noise generated by wind turbines particularly in proximity to residential areas. This study investigates the noise emissions from a full-scale multi-megawatt individual pitch control (IPC) onshore wind turbine, with rotor diameter of 158 m and a hub height of 120 m, across 12 scenarios-six upstream and six downstream. The wind turbine under investigation operates under both IPC and non-IPC modes. Utilizing a microphone array and beamforming algorithm techniques, we conducted comprehensive acoustic measurements to analyses the noise sources and their characteristics. Our findings indicate that turbine operating in IPC mode produces higher sound pressure levels dB (A-weighted) as compared to the turbine under non-IPC modes. This observation emphasizes the need for a balanced approach in wind turbine operations, considering both load reduction and noise mitigation.

Wind turbine tower dynamic behavior investigation

The vibration characteristics of wind turbine may affect the stability, performance and life-time of the system. This research used the theoretical and experimental approaches to predict and investigate the wind turbine dynamic behavior. Three finite element models which were wind turbine with flow, wind turbine structure only, tower only model and tower and base model, were used to calculated the wind turbine's natural frequencies. The actual measurements were used to validation the model correctness. It is expected used the deformation mode under different external forces, base conditions for further identify the critical factors for the structural health conditioning of the wind turbine tower.

Investigation of wind turbine unsteady aerodynamics and trailing-edge noise characteristics under wake steering

In wake steering strategy, the shaft of an upstream wind turbine is yawed to deflect the wake and decrease the wake-swept area of the downstream wind turbine rotor. This intentional yaw misalignment increases the output power of the downstream wind turbine at the expense of some loss of upstream wind turbine power, consequently increasing the total wind farm power. However, the asymmetric non-uniform inflow into the downstream wind turbine due to the deflected wake causes unsteady aerodynamic loads and affects the wind turbine noise characteristics. In this study, the unsteady vortex lattice method coupled with the curled wake model is used to predict the unsteady aerodynamics of a downstream wind turbine in wake steering. Based on the unsteady aerodynamic results, the trailing-edge noise characteristics are predicted by a semi-empirical method. The impact of wake steering on trailing-edge noise level and amplitude modulation is evaluated. The results show that the asymmetric non-uniform inflow due to wake steering increases the amplitude modulation of the downstream wind turbine.

Eolization: A project on spatial auralisation of wind turbine noise

The increase of onshore wind farm projects in Europe has heightened concerns regarding the noise emitted by wind turbines and the potential nuisance to nearby residents. To address this issue, advanced tools are required for accurately assessing and predicting the noise levels and perceived annoyance from wind turbines. Physics-based auralization emerges as a promising approach for recreating with fidelity wind turbine noise in controlled environments. To enhance the immersive experience, this study proposes to extend the current methodologies with the spatial representation of wind turbine noise using ambisonics. In this work, the sound emitted by each turbine is computed using an extended source model. The propagation is simulated with a parabolic equation method accounting for the flow surrounding the wind turbines. Finally, the signal is auralized and spatialized using spherical harmonic decomposition. This methodology provides a complete framework for predicting and reproducing the three-dimensional time-varying sound field around the listener. Overall, these improvements offer valuable insights into the spatial characteristics of wind turbine noise.

Underwater sound produced by operational floating wind turbines

Deployment of offshore wind turbines is growing rapidly. Conventional "fixed bottom" wind turbines are limited to water depths of ~60 m, so deployment of offshore turbines to greater water depths requires floating systems. Both fixed bottom and floating turbines produce operational noise that impacts the marine environment. While the operational noises from fixed bottom turbines are relatively well understood, floating turbines are comparatively novel technologies and there is uncertainty as to the noise they will produce and its impact on the marine environment. Results from acoustic surveys of two floating wind farms off the Scottish Coast are presented; Kincardine wind farm which uses semi-submersible floating systems; and Hywind which uses a floating spar structure. The acoustic signature of each consists of continuous tonal noise related to drivetrain vibration and transient broadband events related to the mooring systems. A machine learning algorithm was used to discriminate between drivetrain noise and mooring noise. Sound propagation modelling was used to determine the source levels for turbines at each wind farm allowing a comparison of the noise signature of different floating structures.

Long term wind turbine sound monitoring

In the DLR Windpark Wiwaldi, consisting of two wind turbines, extensive measurements of sound and meteorology have been conducted. In the context of long-term wind turbine sound monitoring, the data from such measurements are used to understand and decipher physical effects. This kind of data suffers sometimes for reliability, because of many changing conditions and situations around the microphone. Therefore, in three different phases the sound of various sources were captured. Before the turbines were installed, initial measurements were made to identify background noise, followed by additional measurements to assess the impact of installing weather masts on the sound field. Ultimately, the wind turbines were operated reporting the exact state of the turbines (such as rotation speed, pitch, yaw, etc.) to relate to the associated sound levels. This presentation illustrates how all these measurements are used to create a consistent picture of the noise situation around the wind park depending on specific weather situation and discusses the need of accuracy. We document the limitations and uncertainties associated with the measurements and show these limitations when interpreting and reporting the results to ensure users in understanding the reliability and confidence level of such data.

Experimental verification of the variability of amplitude modulation (AM) from wind turbines in relation to its assessment for annoyance

The analysis of the influence of amplitude modulation (AM) on the annoyance of noise generated by wind turbines (WT) is still the subject of numerous studies. However, there is still a lack of widely accepted quantitative methods that would determine the degree of influence of modulation phenomena on noise perception. The study analyzed the variability of the depth of AM over time within different frequency-ranges. The research was based on long-term measurements at selected measurement locations. Long-term measurement ensured consideration of diverse propagation conditions. The collected data were segmented into time intervals, which included basic AM parameters - depth, modulation frequency, and frequency band in which the AM was observed, as well as wind speed and direction. Statistical analysis of the gathered dataset was used to assess the regularity of distinguished AM parameters in correlation with WT noise energy values and recorded non-acoustic parameters. A significant variability of AM depth was observed depending on the frequency band, masking level, and position relative to the wind direction. By adopting a criterion for assessing the annoyance of WT noise (according to AMWG) with a threshold depth of AM not less than 2 dB, it was noticed in over 30% of the analyzed intervals.

Automatic estimation of the wind turbine noise with recurrent neural networks

There is growing interest in the development of renewable energies, particularly wind power. However, wind turbines generate noise that can affect the sound environment of nearby residents. This study focuses on the isolation of wind farm noise (WFN) from the surrounding ambient noise. Our method is based on a Recurrent Neural Network (RNN) Architecture that captures temporal dependencies in the acoustic signal. This proposal is compared to Non-Negative Matrix Factorization (NMF) that has shown first promising results on a previous study on simulated sound scenes. Our approach relies on Long Short Term Memory (LSTM) RNN, conducted using an end-to-end trained model and a Gated Recurrent Unit (GRU). The training and testing dataset is constructed by superimposing measured background noise with synthesized wind turbine noise, effectively simulating realistic environmental conditions. This study aims to reduce the acoustic impact of wind turbines on communities and attempt to control turbine operation using advanced machine learning techniques.

Noise propagation variations over long distances over water

This paper investigates the short temporal variations of noise propagation over a water surface for long distances. Analysis is formed on the results of a measurement campaign for downwind noise propagation over water for elevated sound sources. Noise propagation over water for elevated noise sources is specifically relevant for offshore wind turbines, near shore wind turbines or wind turbines on land close to large water bodies. The measurement setup is a height-adjustable sound source placed at three different heights 81 m, 50 m and 30 m above ground and microphones positioned downwind at shore and ~100 m inland at three different distances over water from the sound source ~3 km, ~5 km and ~7 km. The meteorological conditions wind speed, wind direction, atmospheric stability, temperature, humidity, etc. were monitored continuously at both ends of the setup, utilizing both a tall met mast, a wind profiler and sonic anemometers at multiple heights. The results are used for improving auralisations of noise from offshore wind turbines and offshore wind farms. In addition to this a semi-analytical model for propagation over water is tested.

Start-Up and Fault-Ride-Through Strategy for Offshore Wind Power via DRU-HVDC Transmission System

The diode-rectifier unit (DRU)-based high-voltage direct current (HVDC) transmission system offers an economical solution for offshore wind power transmission. However, this approach requires offshore wind farms to establish a strong grid voltage. To meet this requirement while fulfilling the dynamic characteristics of the DRU, this paper proposes an advanced grid-forming (GFM) control strategy for offshore wind turbines connected to DRU-HVDC. The strategy incorporates a P-U controller and a Q-ω controller based on reactive power synchronization. Furthermore, a novel virtual power-based pre-synchronization method and an adaptive virtual impedance technique are integrated into the proposed GFM control to improve system performance during wind turbine (WT) integration and low-voltage ride-through (LVRT) scenarios. The virtual power-based pre-synchronization method reduces voltage spikes during the integration of new wind turbines, while the adaptive virtual impedance technique effectively suppresses fault currents during low-voltage faults, enabling faster recovery. Simulation results validate the effectiveness of the proposed GFM control strategy, demonstrating improved start-up and LVRT performance through the pre-synchronization and adaptive virtual impedance methods.

The "effects of wind turbine sound on working memory" (a pre-registration plan)

Although research on the effects of wind turbine sound (WTS) over mental health factors is increasing, very little attention has been given to the potential effects of WTS over subtle cognitive processes, such as concentration or memory. The "brainwave entrainment hypothesis" is rooted in neuropsychological research demonstrating that brain oscillations are modulated by external rhythms. In addition, perceived annoyance to wind turbines seems to be a phenomenon in which both visual and psychological variables interact with acoustic stimulation. However, annoyance is a concept rarely operationalised in the literature. Our study aims to experimentally assess the effects of WTS on working memory. Working memory is recognised as a basic process, playing a crucial role in unconscious everyday processes (e.g., perceiving an object's trajectory despite input interruptions when blinking), as well as deliberate processes (e.g., carrying out arithmetic operations). By exploring variations in performance during a memory task, while being exposed to WTS samples, we could highlight acoustic characteristics of the WTS stimuli that interfere-or not-with cognitive processes. These data, combined with within-block ratings of annoyance, can provide us with index of annoyance directly related to the acoustic properties present in WTS recordings.