Wind Turbine Nacelle Research Articles

Thin-walled FRP-concrete-steel tubular tower with a top mass block subjected to lateral impact loading: Experimental study and FE analysis

Thin-walled hollow steel tube towers (HSTs), frequently utilized as a wind turbine tower, are facing challenges such as corrosion and local buckling in their service life. To enhance the anti-corrosion capacity and local buckling resistance, an FRP tube and an annular concrete layer can be employed to protect the HST, thus forming a novel member: thin-walled FRP-concrete-steel tubular towers (TW-FCSTs). Using a horizontal vehicle impact system, this study investigated the impact behavior of five large-scale TW-FCSTs, each with a top mass block to mimic the rotor and nacelle of wind turbine. All specimens were designed with a large diameter (300 mm) and a large void ratio (0.73 or 0.82). The experimental results revealed that: (1) Under lateral impact loading, TW-FCSTs displayed a global flexural failure mode, accompanied by localized concavity damage; (2) The inertial effect was enlarged by the top mass block, affecting the dynamic response of TW-FCSTs; (3) the increase of steel thickness led to higher energy dissipation but lower local deformation; (4) the increase of void ratio resulted in larger local deformation but smaller lateral global displacement. Finally, based on LS-DYNA, FE models were utilized to simulate the dynamic responses of TW-FCSTs with a top mass block.

Three-Phase Single-Stage AC-DC Converter Using Series–Series Compensation Circuit in Inductive-Power-Transfer-Based Small Wind Power Generation System

In this paper, a three-phase single-stage AC-DC converter for an IPT-based small wind power generation system (WPGS) with an S-S compensation circuit is proposed. It applies a three-phase single-stage AC-DC converter to improve the input power factor (PF), efficiency, and reliability in small WPGSs. Also, inductive power transfer (IPT) was applied to compensate for brush wear in the nacelle of small and medium-sized wind turbines while ensuring electrical safety. In conditions of the three-phase Permanent Magnet Synchronous Generator (PMSG) voltage (80~260 Vrms) for the wind turbine and the load (150~1000 W), it was verified that the desired output voltage below 3% can be controlled through the fixed link voltage (VLink) control without wireless communication. A 1 kW prototype was built and tested to demonstrate its applicability to the rotation of small and medium-sized wind turbine nacelles instead of brushes and slip rings.

Detection and validation of common noctule bats (Nyctalus noctula) with a pulse radar and acoustic monitoring in the proximity of an onshore wind turbine.

This paper presents the results of bats detected with marine radar and their validation with acoustic detectors in the vicinity of a wind turbine with a hub height of 120 m. Bat detectors are widely used by researchers, even though the common acoustic detectors can cover only a relatively small volume. In contrast, radar technology can overcome this shortcoming by offering a large detection volume, fully covering the rotor-swept areas of modern wind turbines. Our study focused on the common noctule bats (Nyctalus noctula). The measurement setup consisted of a portable X-band pulse radar with a modified radar antenna, a clutter shielding fence, and an acoustic bat detector installed in the wind turbine's nacelle. The radar's detection range was evaluated using an analytical simulation model. We developed a methodology based on a strict set of criteria for selecting suitable radar data, acoustic data and identified bat tracks. By applying this methodology, the study data was limited to time intervals with an average duration of 48 s, which is equal to approximately 20 radar images. For these time intervals, 323 bat tracks were identified. The most common bat speed was extracted to be between 9 and 10 m/s, matching the values found in the literature. Of the 323 identified bat tracks passed within 80 m of the acoustic detector, 32% had the potential to be associated with bat calls due to their timing, directionality, and distance to the acoustic bat detector. The remaining 68% passed within the studied radar detection volume but out of the detection volume of the acoustic bat detector. A comparison of recorded radar echoes with the expected simulated values indicated that the in-flight radar cross-section of recorded common noctule bats was mostly between 1.0 and 5.0 cm2, which is consistent with the values found in the literature for similar sized wildlife.

The wind turbine nacelle oil-level detection algorithm based on improved YOLOv5n-Seg

Abstract To enable the detection of oil-level information within the oil pot in wind turbine nacelles by inspection robots and address the issues of large model parameters and slow inference speed in the original YOLOv5n-Seg model, this study improved the Backbone and Neck of the YOLOv5n-Seg model using the improved RepVGG module and depthwise separable convolution, respectively. It reduced the number of output channels of each layer network in the improved YOLOv5n-Seg model’s Backbone and Neck to half of its original size. Compared to the original YOLOv5n-Seg model, the improved YOLOv5n-Seg model has significantly optimized parameters and inference speed. These improvements are of significant importance for enhancing the operational efficiency of inspection robots. Additionally, the study designed an oil-level detection algorithm based on the improved YOLOv5n-Seg model. The error between the oil level information detected by the algorithm and the manually detected oil level information does not exceed 7%, which can effectively replace manual oil level detection.

An improved statistical wake meandering model

A new statistical wake meandering (SWM) model is proposed that improves on existing models in the literature. Compared to the existing SWM models, the proposed model has a closed description that does not require simulations to create look-up tables while maintaining applicability to a wide range of flow conditions. The proposed SWM model is compared to the predictions of the Dynamic Wake Meandering (DWM) model and to wind speed measurements from a scanning Doppler lidar mounted on the nacelle of a utility-scale wind turbine for validation. The results show that the proposed model has a similar performance as the DWM model for the effect of wake meandering on the mean velocity deficit and the turbulence intensity, while being significantly faster to compute.

Nacelle modeling considerations for wind turbines using large-eddy simulations

Two setups are used to investigate differences between modeling a wind turbine nacelle by means of an actuator-line model (ALM) and a wall-model (WM) using large-eddy simulations. One advantage of the ALM is that it requires a lower mesh refinement, making it less computationally costly. In the first setup, the nacelle is in standalone configuration and the ALM results show a much lower turbulence intensity and a significantly slower wake recovery when compared to the WM cases. In the second setup, the nacelle is in a rotor-nacelle assembly configuration and many variations of the ALM are tested in order to match the results from the experiment addressed in the OC6 task phase III. Contrary to previous findings that the nacelle might affect the turbine loads, this study shows that the improved match with the experiment stems from the increased mesh refinement in the nacelle region rather than the actual presence of the nacelle. Nevertheless, the wake profiles in the near-wake show a very good agreement between the ALM and WM, regardless of the refinement in the nacelle region. These cases also show a higher wake deficit than not using any nacelle at all.

Framework for Design Validation of Control Algorithms used on Mechanical Hardware-in-the-Loop Nacelle Test Rigs

Full scale multi-MW Wind Energy Converter (WEC) nacelles including the mechanical and electrical components as well as the main and converter control systems, are tested at the Dynamic Nacelle Testing Laboratory (DyNaLab) at Fraunhofer IWES. A Hardware-in-the-Loop environment emulates the missing rotor and accurately applies torque to the nacelle via a motor and the test rig drivetrain. In a current nacelle test campaign, several structural changes have been made to reduce the necessary adaptor tolerances of the test rig interface and to include the nacelle hub. These changes can potentially impact the system’s dynamic response and controller performance. This paper presents the current research with a focus on improving the control algorithms for accurate application of the rotor torque by implementing a simulative validation framework. The objectives of this framework include addressing the impact of modifications to the test rig drivetrain, variations in the nacelle adaptor, and communication delays between real-time simulation models, the test rig, and the WEC controller prior to performing tests on the real system. The methodology shown therefore involves three steps: controller design using a simplified multi-mass torsional model of all components necessary for testing, validation with a high-fidelity virtual test bench model and testing the resulting controller performance during the commissioning phase for the actual test. Overall, this approach is expected to reduce the commissioning duration, enhance the performance of the test rig, and identify the limitations in achieving stable torque control before the application to the real wind turbine nacelle on the test rig.

Vibration anomaly detection of wind turbine based on temporal convolutional network and support vector data description

Due to the complex working conditions and harsh environment, wind turbines often encounter abnormalities, resulting in great operation and maintenance difficulties. As nacelle vibration signals reveal the structure's dynamic characteristics and the interaction between components, vibration anomaly detection has a strong potential. However, vibration anomaly detection is challenging due to the complex characteristics and non-stationarity of high dynamic nacelle vibration signals. To solve this problem, this paper proposed a semi-supervised vibration anomaly detection approach for wind turbines, combining deep learning and one-class classification. Firstly, a healthy behavior model (HBM) for predicting wind turbine nacelle vibration based on the temporal convolutional network (TCN) is developed. To use all available information, a Hilbert spectrum fusion technology (HSFT) was proposed to enhance model performance. Then, based on the support vector data description (SVDD) algorithm, we established a one-class classifier of vibration prediction residual and realized the vibration anomaly detection. The proposed approach can be trained on the healthy dataset to provide accurate detection of different abnormal types. The effectiveness of the proposed anomaly detection approach was verified on simulated and actual monitoring datasets.

Enhancing the performance of hybrid wave-wind energy systems through a fast and adaptive chaotic multi-objective swarm optimisation method

Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy converters (WECs) on a single floating foundation. The design of such systems involves multiple parameters and performance measures, making it a complex, multi-modal, and expensive optimisation problem. This paper proposes a novel, robust and effective multi-objective swarm optimisation method (DMOGWA) to provide a design solution that best compromises between maximising WEC power output and minimising the effect on wind turbine nacelle acceleration. The proposed method uses a chaotic adaptive search strategy with a dynamic archive of non-dominated solutions based on diversity to speed up the convergence rate and enhance the Pareto front quality. Furthermore, a modified exploitation technique (Discretisation Strategy) is proposed to handle the large damping and spring coefficient of the Power Take-off (PTO) search space. To evaluate the efficiency of the proposed method, we compare the DMOGWA with four well-known multi-objective swarm intelligence methods (MOPSO, MALO, MODA, and MOGWA) and four popular evolutionary multi-objective algorithms (NSGA-II, MOEA/D, SPEA-II, and PESA-II) based on four potential deployment sites on the South Coast of Australia. The optimisation results demonstrate the dominance of the DMOGWA compared with the other eight methods in terms of convergence speed and quality of solutions proposed. Furthermore, adjusting the hybrid wave-wind model’s parameters (WEC design and PTO parameters) using the proposed method (DMOGWA) leads to a considerably improved power output (average proximate boost of 138.5%) and a notable decline in wind turbine nacelle acceleration (41%) throughout the entire operational spectrum compared with the other methods. This improvement could lead to millions of dollars in additional income per year over the lifespan of hybrid offshore renewable energy platforms.

Design and realization of a pre-production platform for wind turbine manufacturing

Addressing the challenge of rehearsing large-scale equipment assembly, particularly for oversized components like wind turbine towers, hubs, nacelles, and blades, which often face quality issues such as substandard workmanship and wide tolerances during on-site assembly, a system has been developed for simulating wind turbine assembly. This system enables digital wind turbine assembly by creating virtual and production process models and employing intelligent database analysis. It resolves the problem of the absence of pre-production for large-scale equipment, meets the batch production needs of the wind turbine manufacturing industry, enhances the safety of wind turbine operations, improves operator assembly skills, and boosts production efficiency. This platform has already been implemented in the wind turbine manufacturing industry, yielding significant economic benefits.

A novel interpolation-MLP coupled reconstruction method for turbulent natural convection thermal environment reconstruction of MW-class offshore wind turbine nacelles

Localized overheating in the nacelle due to heat loss in high-power offshore wind turbines of 10 MW or more, where localized overheating affects the operation of other components, it is particularly important to visualize the thermal environment in the nacelle accurately and in real time in order to control the start-up and shutdown of cooling methods. However, the complexity of the high turbulence intensity (Rayleigh number (Ra) > 109) thermal environment in the nacelle of high-power units leads to significant errors in existing visualization techniques. Therefore, this paper presents a novel coupled interpolation-MLP reconstruction technique based on a data-driven deep neural network. Based on the interpolation method as primary reconstruction, a secondary reconstruction neural network model is obtained by training the thermal environment parameters (static pressure P, tangential velocity U, normal velocity V and temperature T) of specific computing nodes in the nacelle. The model is used to predict the values of thermal environment parameters at each time point for computing nodes with large gradients. An improved PSO algorithm is used to arrange the positions of a defined number of sensors to save sensor resources during the measurement process and to and ensure computing stability. The above improvements to the traditional interpolation reconstruction algorithms have led to an increase in the accuracy of the thermal environment reconstruction.

Virtual Model Development of the Load Application System of a Wind Turbine Nacelle Test Bench for Hybrid Test Applications

Abstract The existing nacelle testing methods require continuous improvements to satisfy the ever-increasing demands for testing modern wind turbines. One way to achieve this goal is to use advanced simulation techniques to undertake hybrid testing, in which experiments and simulations are combined to push the boundaries of nacelle testing even further. To do so requires the development of a virtual model of the test bench featuring the true test bench dynamics and functionalities. This contribution presents the development of a virtual model of the complete nontorque load application system of a nacelle test bench at Fraunhofer IWES. The model development methodology is explained and the impact of different levels of modeling depth of the hydraulic system model is investigated. It is concluded that modeling of friction and valve dynamics is necessary as they have significant influence on the generated loads. These findings can help in the development of virtual models of nacelle test benches and pave the way for performing hybrid testing for wind turbine nacelles.

On the polarimetric backscatter by a still or quasi-still wind turbine

Abstract. Wind turbines negatively affect the performance of weather radars, especially when located in the proximity of a radar site. In March 2019, MeteoSwiss performed a measurement campaign by deploying a mobile X-band radar in Schaffhausen. It proved to be useful for mapping and characterizing the maximum power returns by three wind turbines observed using standard scanning strategies. In March 2020, the campaign was repeated using a more sophisticated scan strategy: ∼ 100 min special sessions of fixed pointing an antenna towards the nacelle of the closest wind turbine (WT) located within a range of 7766 m from the radar, interleaved every 2 h by a scanning protocol identical to that of the March 2019 campaign. Polarimetric radar signatures were derived every 64 ms using 128 radar pulses transmitted every 0.5 ms (pulse repetition frequency (PRF) = 2000 Hz). A thorough overview of the polarimetric signatures of the WT in still or quasi-still conditions has been obtained based on 30 000 polarimetric measurables acquired over 32 min on the first day of the campaign (4 March 2020). During the first 2 min with zero rotor speed, the co-polar correlation coefficient between the orthogonal polarization states, ρHV, was persistently equal to 1, similarly to the signature of a bright scatterer observed by a non-rotating antenna. The changes between two consecutive values of the differential reflectivity and radar reflectivity factor were either 0 dBz or ±0.5 dBz. Due to the absence of precipitation, one could assume that the standard deviation of the differential phase shift, which was as small as 3.0∘, can be entirely attributed to the variability of the differential backscattering phase shift. There were two 10 min periods during which the rotor moved less than 1 revolution. It is worth noting that this slow movement could be associated with a change in the blade pitch angle and the nacelle orientation, which caused extreme changes in the radar reflectivity factor. For instance, two pairs of 64 ms consecutive values reached 78.5 dBz, which is the absolute maximum reached in the whole campaign (4–21 March 2020).

Monitoring method for detecting and localizing overheat, smoke and fire faults in wind turbine nacelle

This study presents a monitoring method that utilizes 3D object classification to accurately detect mechanical and electrical components of a wind turbine by combining a geometric and statistic feature extractor (GSFE) with a multiview approach. The proposed monitoring method also detect outlier after executing object detection to localize overheat faults in these components with fused Optical or Infrared/LiDAR measurements. The proposed method hasthree key characteristics. First, the proposed outlier detection allocates two extremes of normal and faulty clusters by using 2D object classification/detection model or measuring thestandard deviation of temperature with sensor fusing measurements. Specifically, the outlier detection with sensor fusing measurements extracts the position coordinates andtemperature data to localize overheat faults, effectively detecting an overheat component. Second, the GSFE utilizes a group sampling approach to extract the local geometric feature information from neighboring point clouds, aggregating normal vectors and standard deviation. This method ensures the high accuracy of object classification. Third, a multi-view approach focuses on updating local geometric and statistic features through a graph convolution network, improving the accuracy and robustness of object classification. The proposed outlier detection is verified through overheat/fire field tests. The effectiveness of the proposed 3D object classification method is also validated by using a virtual wind turbine nacelle CAD dataset and a public CAD dataset named ModelNet40. Consequently, the proposed method is practical and effective for monitoring a fire and overheat component because it can accurately detect critical components with only a few virtual datasets because gathering bigdata for training a neural network is extremely difficult.

An Acoustic Emission IoT Device for Wind Turbine Rotor Blade Condition Monitoring

This paper discusses the development and implementation of a wind turbine rotor blade condition monitoring system based on the acoustic emission non-destructive method. The condition monitoring system’s core is an acoustic emission IoT device capable of detecting active damage on the blades using micro-electro-mechanical acoustic sensors. The signals detected by the sensors are pre-processed in the IoT device and transmitted, via a Wi-Fi access point in the wind turbine nacelle, or via cellular network, to a datadriven application residing in the cloud. The data processing is completed in the cloud, where the information extracted is used to calculate proprietary quantities that are displayed and tracked. These proprietary quantities are related to the structural health of the wind turbine rotor blades and are used to rank the blades in a wind farm according to their condition. This ranking helps the wind farm site operator to prioritize inspection and plan maintenance. This condition monitoring system based on the acoustic emission IoT device has been implemented in several wind farms across North America and Europe.

Definition of an ice index for wind turbines in cold climate

Icing losses encountered in a wind farm are very hard to assess as they can vary by a factor of three from year to year. This inter-annual variation is a critical issue from the assessment phase to the operation of wind turbines in cold climate. To mitigate the effects of this issue, a proposed solution is to define an ice index to yield a reference for icing conditions based on historical values. Knowing that icing frequency has an inter-annual variation 5 times greater than the wind speed, the definition of an ice index is long overdue. This paper proposes a simple and accessible method for defining an ice index based on cloud base height measured at a weather station. A similar strategy is already used in the industry for other purposes such as estimating the long-term average icing frequency in icing maps. The novelty of the proposed method lies in the description of the variation in icing frequency rather than its long-term average. The ice index proposed in this paper is shown to be representative of the icing losses in 6 wind farms located in eastern Canada. Moreover, it was also shown that the ice index is strongly correlated to the icing measurements made on wind turbine nacelle. In this paper, the yearly and monthly ice indexes, from September 2007 to December 2021, are shared. These indexes are based on the meteorological data from Valcartier weather station near Quebec City. This definition of an ice index directly responds to a critical need of the wind industry in cold climate, a common reference for icing frequency. Ice indexes could be publicly shared each month in cold climate areas to guide wind farm owners and operators in their decisions and analysis of performances. Such an index could also be used to asses the long-term variation of the ice index and the effect of climate change on icing frequency and impacts on the wind energy production in cold climate.

Accurate wind energy harvest assessment using a comprehensive ultra-large-wind-turbine emulation

A comprehensive 5 MW wind turbine emulator's design and implementation are designed and investigated thoroughly in this paper. A separately excited DC motor set under the control of a current-controlled drive forms the basis of the suggested emulation. The emulation algorithms that simulate the features of the 5 MW wind turbine are run on a digital signal processor (DSP). These algorithms regulate the DC motor's current, which in turn regulates the torque. The results obtained demonstrate that the system was successful in accurately simulating the transient and steady-state characteristics of the 5 MW wind turbine. The emulator system was initially tested and validated with simulation results using MATLAB/Simulink code attached to both the FAST and TurbSim models. Furthermore, step and stochastic wind profiles were used to experimentally verify and test it. Nearly all of the power that is available in the wind turbine system is successfully obtained using the maximum power point tracking technique, which is used to find the best operating point. The created wind turbine emulator can be used to precisely assess energy harvesting by examining various mechanical and electrical properties of the Permanent Magnet Synchronous Generator (PMSG) vastly used in ultra-large wind turbine nacelles.

Numerical simulation of turbulent natural convection heat transfer in an MW-class offshore wind turbine nacelle based on a multi-feature acquisition meshing technique

In order to improve the computational accuracy of numerical simulations of complex thermal environments in MW-class offshore wind turbine nacelles, this paper improves the numerical simulation method for 10 MW offshore wind turbine nacelles. Firstly, the model of the wind turbine generator system (WTGS) inside the nacelle is designed and the model is simplified. Secondly, a multi-featured acquisition meshing is performed for the turbulent thermal behavior in the nacelle, using a combination of mesh geometry parameters, mesh accuracy parameters, mesh irrelevance parameters and detection verification parameters to construct a structural mesh topology. The modified SST k-ω turbulence model is used to solve the momentum and energy equations of the nacelle model. The complex physical phenomena of air recirculation, boundary layer separation and thermal stratification in the nacelle are verified based on the calculated results and compared with the experimental data, which are in good agreement with the experimental results. This paper has certain application value and guidance significance in the field of engineering such as the design of offshore wind turbine nacelle structure and cooling system design.

Prediction of aerodynamic performance of NREL offshore 5‐MW baseline wind turbine considering power loss at varying wind speeds

AbstractIn this study, a computational fluid dynamics (CFD) model was developed to simulate the aerodynamic performance of the National Renewable Energy Laboratory (NREL) offshore 5‐MW baseline wind turbine with single rotor and full wind turbine. Using statistical methods, the relation between pitch angle and performance when the speed is above the rated wind speed was analyzed; furthermore, other published data were compiled to establish the functional equations of power, thrust with various inflow wind speeds, and pitch angles. In addition, according to shape optimization based on parametric modeling, the two‐dimensional cross‐section of the wind turbine blade can be defined through a metasurface approach, and the three‐dimensional surface modeling of the wind turbine blade, nacelle, and tower is completed using the nonuniform rational B‐splines (NURBS) interpolator. In terms of aerodynamic simulation, the unstructured polygon mesh was used herein to discretize the space and simulate the highly curved and twisted surfaces of the blade. In this study, the compact and accurate mesh form obtained through a grid independence test will be used to analyze the distribution of the pressure coefficient, shear stress coefficient, and limited streamline on the blade surface at various inflow wind speeds and pitch angles to understand the differences between different turbulence models and the causes of power and thrust attenuation at high inflow wind speeds. In addition, the phenomena of blade‐tip vortices, dynamic stall, blade loading, and the interaction between nacelle and tower were collectively explored.

On a new methodology for testing full load responses of wind turbine drivetrains on a test bench

Today’s nacelle test benches are facing several challenges regarding meeting the increasing demands of modern wind turbines, which have been growing rapidly in operational range, size, and complexity. These challenges include reproducing the demanded extreme loads, dynamic load bandwidth, power capacity, and cost of testing. This contribution presents a new testing approach to tackle some of the aforementioned challenges faced by existing nacelle test benches. The method is demonstrated in a case study involving experimental measurements and simulations of a multi-megawatt wind turbine drivetrain recently tested at the DyNaLab of Fraunhofer IWES. By combining high-fidelity simulation models and partial load tests, the proposed approach has shown high potential for representing the full load response of a wind turbine nacelle. The proposed testing methodology has potential for resolving some of the challenges being faced by modern test benches in terms of obtaining full load responses on a nacelle test bench with a possibility of reducing the cost of testing.