Performance Of Wind Turbine Research Articles

Performance of a Savonius vertical axis wind turbine installed on a forward facing step

This study investigates the effects of installing a Savonius turbine on a forward-facing step to increase the power efficiency. Since the flow is disturbed by the step, the turbine may benefit from the accelerated flow found on top of the step. The flow field is solved using computational fluid dynamics in a 3D computational domain. The finite volume method is used to solve the Reynolds average Navier–Stokes governing equations and the SST k-ω turbulence equations. This simulation includes both rotating turbine and the step. The novelty of this work is to study this configuration with a power law wind velocity distribution as inlet velocity, which is more realistic in common applications, and to investigate a different turbine. The study shows that the Savonius turbine generates up to 73% more power when installed on a 5 m step. However, in case of a very high step, or a different turbine no improvement has been observed.

Research on the Blades and Performance of Semi-Submersible Wind Turbines with Different Capacities

With the gradual increase in the maturity of wind energy technology, floating offshore wind turbines have progressively moved from small-capacity demonstrations to large-capacity commercial applications. As a direct component of wind turbines used to capture wind energy, an increase in the blade length directly leads to an increase in blade flexibility and a decrease in aerodynamic performance. Furthermore, if the floater has an additional six degrees of freedom, the movement and load of the blade under the combined action of wind and waves are more complicated. In this work, two types of semi-submersible wind turbines with different capacities are used as the research objects, and the load and motion characteristics of the blades of these floating offshore wind turbines are studied. Through the analysis of the simulation data, the following conclusions are drawn: with the increase in the capacity of the wind turbine, the flexible deformation of the blade increases, the movement range of the blade tip becomes larger, the blade root load increases, and the power fluctuation is more obvious. Compared with the bottom-fixed wind turbine, the flexible blade deformation of the floating offshore wind turbine is smaller; however, the blade root load is more dispersed, and the power output is more unstable and lower.

Potential and Limitations of Anomaly Detection via Tower-Radar Monitoring of Wind Turbine Blades in Regular Operation with Convolutional Networks

The authors have already presented a first analysis of a field study on radar- based structural health monitoring of rotor blades on two operational wind turbines. This analysis will now be extended with measurements from a third operational wind turbine used as a case study for structural monitoring of individual rotor blades over different time intervals and blade modifications. More precisely, a sensor box with a 35 GHz FMCW radar and a highspeed camera was positioned at 90m height on the turbine tower, collecting radar data for structural information and videos for automatic ground truth labelling. After two months of collecting data, each individual rotor blade of that turbine was differently modified and measured again. The radar-facing surface of one rotor blade was partially coated with a microwave absorber foil, while an equivalent foil was applied to the backside of the second rotor blade. The third rotor blade was intentionally left unchanged for the sake of comparability. This study investigates potential limitations like general dataset complexity in radar-based anomaly detection for operational wind turbines. Furthermore, the discriminative ability of a convolutional neural network for classifying moving rotor blades is discussed when encountering feature drift through changing environmental and operational conditions, i.e. metadata like nacelle orientation, rotor blade pitch and rotation speed.

Stochastic estimation of wake-induced loads in wind farms using conformalized graph neural networks

In the evolving landscape of sustainable energy, wind power has become a cornerstone in the transition towards renewable energy sources. With significant advancements in turbine technology and simulation methods, wind farms are now a cost-effective alternative to traditional power plants. Nevertheless, optimizing the performance and lifespan of wind turbines remains challenging, especially when it comes to predicting and managing the cumulative load on turbine structures. Wake effects, which result from the complex aerodynamic interplay between turbines, reduce energy efficiency and increase mechanical stress on turbine components. A precise assessment of wake-induced loads is therefore vital for estimating the Remaining Useful Lifetime (RUL) of turbines. To address these challenges, we introduce a novel approach using Graph Neural Networks (GNNs) in combination with Conformal Predictors to model wind farms and evaluate wake-induced loads while estimating uncertainty. GNNs are particularly adept at capturing the complex interactions in a wind farm due to their ability to model graph-structured data. This enables a more accurate representation of the aerodynamic interactions between turbines. Alongside the GNN framework, we employ Conformal Predictors for uncertainty estimation. Conformal Predictors provide statistically valid prediction sets based on past data and minimal assumptions, allowing us to estimate uncertainty bounds with improved confidence. The fusion of GNNs with Conformal Predictors offers a robust framework for predicting turbine loads, while reliably quantifying the uncertainty associated with these predictions.

Unsupervised Data-Driven Approach for Scour Damage Detection in Offshore Monopile Foundations

Scour erosion around monopile foundations significantly impacts the structural integrity and operational performance of offshore wind turbines (OWTs). To deal with this problem, this paper proposes an innovative unsupervised data-driven methodology to detect foundation scour in OWTs. The proposed method employs acceleration data collected from the tower. A numerical model of the NERL 5 MW OWT installed in medium-dense sand is developed using OpenFAST software. The study employs the novelty detection method, where only data obtained from the healthy conditions are used for training and establishing a base model. In addition, the influence of operational conditions, e.g. different wind and wave parameters is also evaluated. The process consists of three steps: (a) data pre-processing to extract time domain features and isolate environmental variations, (b) training an unsupervised anomaly detection algorithm by employing the extracted features and (c) conducting real-time anomaly detection to assess the scour damage. The study results highlight the robustness and efficiency of the proposed method in detecting scour damage around monopiles under different environmental conditions. Keywords: Monopile, scour, unsupervised, Machine learning, damage detection, SHM.

An unsupervised damage detection method for an operating wind turbine blade

Wind turbine blades are normally exposed to significant dynamic loads over their lifetime, including extreme conditions. This can lead to several types of damages such as cracking and delamination. Monitoring the structural condition of the blades to detect damages before they become catastrophic can be important. In recent years, the concept of data-driven structural health monitoring (SHM) approaches for wind turbine blade damage detection has become popular. These methods employ features collected from time series obtained from monitored wind turbine blades to identify damages. However, selecting an optimum number of features, which can reduce computational cost and minimize the effects of environmental and operational conditions, is still a challenge. In this paper, a data-driven approach is developed combining a feature extraction strategy and un-supervised anomaly detection. Monitoring data collected from several accelerometers mounted on a blade of an operating Vestas V27 wind turbine in healthy and damaged scenarios are used in this regard. Several time-domain, temporal, and time-frequency domain features are employed for the purpose of anomaly detection using unsupervised learning methods. Isolation Forest and One-class Support Vector Machine are used to train for anomaly detection. A threshold of detection is established to distinguish between the healthy and damaged states. Results show that the proposed approach can successfully identify the damage cases, but environmental and operational conditions still have some effect when it comes to identifying the extent of the damage.

Collision-Damage Analysis of a Floating Offshore Wind Turbine Considering Ship-Collision Risk

As the number of offshore wind-power installations increases, collision accidents with vessels occur more frequently. This study investigates the risk of collision damage with operating vessels that may occur during the operation of an offshore wind turbine. The floater used in the collision study is a 15 MW UMaine VolturnUS-S (semi-submersible type), and the colliding ships are selected as multi-purpose vessels, service operation vessels, or anchor-handling tug ships based on their operational purpose. Collision analysis is performed using ABAQUS and substantiation is performed via a drop impact test. The collision analyses are conducted by varying the ship velocity, displacement, collision angle, and ship shape. By applying this numerical model, the extent of damage and deformation of the collision area is confirmed. The analysis results show that a vessel with a bulbous bow can cause flooding, depending on the collision conditions. For damage caused by collision, various collision angles must be considered based on the internal stiffener arrangement. Additionally, the floater can be flooded with relatively small collision energy when the colliding vessel has a bulbous bow.

Wind Turbine Performance Evaluation Method Based on Dual Optimization of Power Curves and Health Regions

The wind power curve serves as a critical metric for assessing wind turbine performance. Developing a model based on this curve and evaluating turbine efficiency within a defined health region, derived from the statically optimized power curve, holds significant value for wind farm operations. This paper proposes an optimized wind power curve segmentation modeling method based on an improved PCF algorithm to address the inconsistency between the function curve and the wind power curve, as well as the issues of prolonged curve modeling training time and susceptibility to local optima. A health region optimization method based on data increment inflection points is developed, which enables the delineation of the health performance evaluation region for wind turbines. Through the aforementioned optimization, the performance evaluation method for wind turbines is significantly improved. The effectiveness of the performance evaluation method is validated through experimental case studies, combining the wind power curve with the rotational speed stability, power characteristic consistency coefficient, and power generation efficiency indicators. The proposed modeling technique achieves a precision level of 0.998, confirming its applicability and effectiveness in practical engineering scenarios.

Wind power forecasting based on a novel gated recurrent neural network model

Accurate wind power forecasting is crucial for upgrading the operation and maintenance of wind turbines and the effectiveness in wind power dispatching. In this work, density-based spatial clustering of applications with noise (DBSCAN) is adopted to exclude outliers, and is examined to yield top-quality data filtering for neural networks when implemented on supervisory control and data acquisition (SCADA) data from a 7-MW wind turbine. Apart from features, such as wind and rotor speeds, time is a vital feature involved with synchronous or asynchronous events. Thus, a model-agnostic vector representation for time, Time2Vec (T2V), is used. This can be embedded with neural network architectures, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The novel neural network model, T2V-GRU, concatenates T2V into vanilla GRU, replacing the notion of time vector by T2V. Forecasting results are evaluated by mean absolute error (MAE), root mean squared error (RMSE), and accuracy. In comparison to T2V-LSTM, LSTM and GRU, the proposed T2V-GRU model in combination with DBSCAN refinement has superior forecasting results for the 7-MW turbine, yielding maximum forecasting accuracy (93.12 %) and minimal prediction errors in terms of MAE (286.10 kW) and RMSE (489.54 kW), while simultaneously reducing the computational cost and time.

The Effect of Deflector Angle on the Performance of Turbine Combination Darrieus-Savonius

The Savonius and Darrieus are vertical-axis wind turbines. This turbine has resistance on one side of the blade that makes it difficult for the shaft to rotate resulting in reduced efficiency. However, the vertical shaft turbine type provides the advantage of being able to accept wind speeds from any direction without an additional tail. Savonius and Darrieus turbines show different characteristics in terms of their performances. Savonius turbine has a high power coefficient (CP) in the range of low tip speed ratio (TSR), when its TSR increases the CP will fall. In contrast to Darrieus, high CP is achieved if the TSR is also high but the CP achievement will fall at a low TSR. If it is compared to Savonius, Darrieus has higher power efficiency although it is difficult to self-start. In contrast to Savonius, Darrieus has a better self-starting ability but a lower efficiency. This study aims to improve the performance of vertical shaft wind turbines by creating a new design combination of Darrieus and Savonius turbines with deflectors to produce CP achievement on a wide TSR. The combination of the Darrieus-Savonius turbine is to improve efficiency to make self-start easier. The research method uses numerical simulation by employing CFD Ansys software. On the airfoil with a deflector angle of 70 deg, it shows that there is an increase in speed in several parts of the Darrieus blade airfoil. The increase in speed causes the decrease of static pressure in the area. The pressure difference between the sides of the airfoil surface causes a force. The direction of the force causes the turbine shaft to rotate. The deflector acts as a directional headwind, increasing the local flow velocity to counter the resistance on one side of the rotor blades The average torque produced at an angle of 70 deg is 0.5 Nm. Whereas, at an angle of 90 deg, the average torque generated is lower by around 0.15 Nm.

Vibration control and energy harvesting of offshore wind turbines installed with TMDI under dynamical loading

This study investigates the performance of multiple tuned mass-damper-inerter with multiple electromagnetic motor (TMDI-EM) for the dual objectives of enhancing energy harvesting and dynamic performance in offshore wind turbines (OWTs). OWTs, inherently susceptible to dynamic sensitivity under lateral loads such as wind and waves, necessitate effective solutions for vibration mitigation while harnessing energy from dynamic excitations. The TMDI-EM configuration integrates an electromagnetic motor (EM) as a shunt damper between a secondary mass and an inerter element in series. The scalable inertance of the inerter element allows for adaptability in practical device implementations. Genetic Algorithm (GA) is employed to find optimum parameters for the TMDI-EM system. The research focuses on the dynamic response of OWTs under real-world conditions, where wind and wave forces act as correlated random excitations. Parametric analyses assess the available energy for harvesting at the EM and the displacement variance of the OWT structure as the inertance of the TMDI-EM varies. The study employs methods to optimize the TMDI-EM parameters using Genetic Algorithm, ensuring the system's optimal performance for both enhanced energy harvesting and dynamic response. The findings indicate that lightweight TMDI-EMs, representing only 1.5 % of the OWT structure's mass, demonstrate improved vibration suppression and energy harvesting performance with increasing inertance. This study contributes into the potential integration of TMDI-EMs to enhance the dynamic performance and energy harvesting capabilities of offshore wind turbines under dynamical loading conditions. The use of Genetic Algorithm ensures the identification of optimal parameters for the TMDI-EM system, enhancing its practical applicability.

The Effect of Nacelle Shape and Blade Geometry on the Performance of Small Scale Wind Turbine

The Effect of Nacelle Shape and Blade Geometry on the Performance of Small Scale Wind Turbine

An experimental investigation of a solar chimney integrated with a bladeless wind turbine for sustainable energy harvesting

The current study explores the potential of combining small-scale solar chimney systems with blade-less wind turbines for sustainable energy solutions in Egypt through mathematical modelling, numerical simulation, and experimental work. The integration of a vortex bladeless turbine and a conventional horizontal axis wind turbine in a hybrid energy harvester solar chimney system (VBSCS) was evaluated, as well as the effect of changing the absorber material on improving the system's performance. Shredded rubber improves heat absorption in solar chimney collectors, optimizing energy conversion and providing a sustainable, eco-friendly solution while addressing challenges and cost-effectiveness in implementing this hybrid solution. The mathematical model predicted the highest power at 0.38 W, while the CFD model yielded 0.298 W. Experimental results showed power outputs of 0.18 and 0.25 W for the SCS with concrete and shredded rubber absorbers, respectively. In addition, the VBSCS with a concrete absorber produced 0.29 W. The determined percentage improvement over the SCS with a concrete absorber was 61.1 % for the VBSCS and 38.9 % for the SCS with a black rubber absorber. These findings demonstrate the delicate interplay of design aspects and material choices that influence the performance of both bladeless wind turbines and solar collector systems.

Thermal error prediction and reliability analysis of the main shaft bearing at wind turbines

During the operation of the gearless wind turbine, the phenomenon of heat generation in the main shaft bearing is inevitable and further affects the assembly preload. It is crucial to determine the effect of the thermal error on bearing assembly preload. In this paper, a reliability analysis method for main shaft bearing is proposed. Firstly, a finite element model for the thermal analysis of wind turbines is established based on heat transfer theory, and the thermal error of the preload is calculated. Subsequently, a reliability analysis of the main shaft bearing is conducted through Quasi-Monte Carlo simulation (QMCS) considering the influence of uncertainty factors. To further improve the computational efficiency, a surrogate model based on stochastic configuration network (SCN) is established to analyze the reliability and sensitivity of the bearing. Finally, the numerical example shows that the proposed model has high accuracy and applicability.

UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings

Maintenance planning is crucial for efficient operation of wind turbines, particularly in harsh conditions where degradation of critical components, such as bearings, can lead to costly downtimes and safety threats. In this context, prognostics of degradation play a vital role, enabling timely interventions to prevent failures and optimize maintenance schedules. Learning systems-based vibration analysis of bearings stands out as one of the primary methods for assessing wind turbine health. However, data complexity and challenging conditions pose significant challenges to accurate degradation assessment. This paper proposes a novel approach, Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion (UBO-EREX), which combines Extreme Learning Machines (ELM), a lightweight neural network, with Recurrent Expansion algorithms, a recently advanced representation learning technique. The UBO-EREX algorithm leverages Bayesian optimization to optimize its parameters, targeting uncertainty as an objective function to be minimized. We conducted a comprehensive study comparing UBO-EREX with basic ELM and a set of time-series adaptive deep learners, all optimized using Bayesian optimization with prediction errors as the main objective. Our results demonstrate the superior performance of UBO-EREX in terms of approximation and generalization. Specifically, UBO-EREX shows improvements of approximately 5.1460 ± 2.1338% in the coefficient of determination of generalization over deep learners and 5.7056% over ELM, respectively. Moreover, the objective search time is significantly reduced with UBO-EREX with 99.7884 ± 0.2404% over deep learners, highlighting its effectiveness in real-time degradation assessment of wind turbine bearings. Overall, our findings underscore the significance of incorporating uncertainty-aware UBO-EREX in predictive maintenance strategies for wind turbines, offering enhanced accuracy, efficiency, and robustness in degradation assessment.

The impact of local wind and spatial conditions on geometry blade of wind turbine

AbstractThe research addresses the pressing need to understand wind conditions for optimal wind energy utilization, a crucial aspect of achieving global energy sustainability. While previous studies have focused on assessing wind energy potential and terrain roughness, gaps persist in understanding how wind and spatial conditions affect blade geometry. Therefore, this study aims to investigate the impact of local wind conditions on wind turbine blade geometry, integrating nonuniform wind inflow due to wind shear into the theoretical model. Through the paper, it explores blade design for varying inflow conditions and analyses the relationship between terrain type and blade designs. A novel approach to wind turbine blade design, utilizing a theoretical model that combines the local momentum theorem with insights from propeller vortex theory was adopted. Through the analysis, the authors demonstrate that variations in terrain significantly influence blade geometry, underscoring the necessity for tailored turbine designs based on local conditions. The findings reveal discrepancies in blade geometry across different turbine capacities and terrain types, offering novel insights into wind turbine performance optimization. By tailoring blade geometry to specific locations enhances resource utilization, offering insights for energy policymakers. The research provides validated methods for optimizing turbine geometric parameters, opening avenues for increased local investment in wind energy and promoting distributed energy development. Illustrated with wind plants examples, the paper concludes by outlining future research directions in this field.

A comprehensive approach to wind turbine power curve modeling: Addressing outliers and enhancing accuracy

This research presents a comprehensive approach to improving the accuracy of wind turbine power curve (WTPC) modeling. The WTPC is a critical tool for monitoring wind turbine performance and estimating wind power potential, but current models have limited ability to capture the complex relationship between wind speed and power output. To address these limitations, this study implements a dual-pronged refinement of WTPC modeling. First, an innovative data preprocessing technique is introduced, using a 97 % confidence interval around a KNN-estimated WTPC constructed using the Laplace distribution to meticulously eliminate prominent outliers. Second, a novel WTPC modeling approach based on quantile regression is adopted, accounting for the asymmetric error characteristics in the loss function. Four distinct quantile regression models are developed, including three tree-based algorithms - decision trees, random forests, and gradient boosting - and a deep learning-based quantile regression neural network. Comparative analysis against ten established parametric and nonparametric techniques confirms the superiority of the proposed models, with the decision tree quantile regression model achieving the lowest validation errors. The proposed techniques are validated on two real-world datasets from operational wind turbines in Turkey and China, demonstrating significant improvements in WTPC modeling accuracy compared to conventional methods. Overall, this study successfully presents a comprehensive modeling approach that addresses outliers and leverages quantile regression to significantly enhance WTPC accuracy.

Wind turbine end-of-life options based on the UN Sustainable Development Goals (SDGs)

The installed wind power capacity is rapidly growing worldwide, and large volumes of waste materials would need to be treated due to the decommissioning of wind turbine systems in the next years. The purpose of the present work is to evaluate the sustainability performance of decommissioning options for wind turbines and suggest policies to improve the sustainability of the wind turbine end-of-life phase by jointly applying Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) methodologies. Unlike most of the relevant literature, which focuses mainly on technical aspects of wind turbine decommissioning, the proposed approach considers all three dimensions of sustainability using Sustainable Development Goals (SDGs) as a guide to identify economic, environmental, and social indicators. The methodology is applied to a representative case study of a wind turbine operating in the Greek territory using real data. Repurposing was found to be the most sustainable end-of-life alternative for composite waste. In contrast, the waste treatment option of the foundation concrete contributes substantially to the sustainability performance of the examined scenario. The sustainability performance of these technologies could be enhanced by the operation of dedicated concrete and composite materials recycling facilities close to wind parks, following cross-sectoral approaches. The results also indicate that the social aspects of the sustainability framework are equally important and should be considered when strategies towards more sustainable waste management of wind turbine systems are designed.

Impact of incoming turbulence intensity and turbine spacing on output power density: A study with two 5MW offshore wind turbines

Turbulence intensity in offshore environments has significant effects on the performance of offshore wind turbines. To maximize output power of offshore wind turbines within a limited space, i.e., achieving maximum output power density, it is essential to investigate the influences of incoming turbulence intensity and turbine spacing on output power density. The Large Eddy Simulation coupled Actuator Line Method (LES-ALM) is used in this study to simulate two utility-scale NREL-5 MW wind turbines in different turbulent environments and turbine spacings. The optimal incoming turbulence intensity and turbine spacing are identified using an active learning method. The determined optimal parameters act as a benchmark for the impact analysis. The findings indicate that the increase of turbine spacing initially results in a considerable rise in the output power density of the two NREL-5 MW wind turbines, followed by a slight downward trend across various turbulent environments. The increase of the incoming turbulence leads to a steady rise of the output power density when the turbine spacing is less than 5.1D (D is the rotor diameter), while a rise followed by a decrease in the output power density is observed for the turbine spacing exceeding 5.1D. Notably, when the turbine spacing is 5.9D and the reference value of turbulence intensity is 15.2%, the output power density reaches its global maximum.

Aerodynamic analysis of vertical axis wind turbines at various turbulent levels: Insights from 3D LES simulations

This study provides novel insights into the aerodynamic performance of vertical axis wind turbines (VAWTs) in diverse turbulent environments using 3D large eddy simulations (LES). The research aims to understand how turbulence affects VAWT aerodynamics across different operational conditions, including varying wind speeds, tip speed ratios (TSRs), and turbulence intensities. Validation against wind tunnel test data from literature enhances the reliability of the findings. The study utilizes the consistent discrete random flow generation (CDRFG) technique to create homogeneous turbulence in the approaching flow. Through a detailed examination of the rotor aerodynamics and flow fields, critical insights are gained into the impact of turbulence on VAWT performance. The results highlight that local wind speed fluctuations influence the onset of dynamic stall, with turbulence significantly affecting lift forces, while having negligible effects on drag. Notably, fluctuations in the one-cycle power coefficient CP,sgl are primarily attributed to variations in lift. Furthermore, under turbulent conditions, substantial improvements in power performance are observed as wind speed increases from 5 m/s to 10 m/s, with minimal further enhancements at higher wind speeds. These findings contribute valuable insights for optimizing turbine design and operation, particularly in regions characterized by varying wind speeds and turbulence.