Wind Machines Research Articles

Computational Fluid Dynamics in Wind Energy: Modeling and Optimization for Turbine Design

Renewable energy sources are hard to come by, but wind power has become one of the most popular options because it can make a lot of clean electricity. For wind energy to be used properly, however, turbines need to be designed in a way that takes into account the surrounding environment. To model and improve wind turbine systems, computational fluid dynamics (CFD) has become very important. It lets engineers simulate complicated fluid flow processes and improve turbine performance. When it comes to wind energy, this study mainly talks about how CFD can be used to model and improve rotor designs. Experts can learn more about the complicated fluid dynamics around wind turbines by using CFD models. For example, they can learn how the rotor blades interact with the wind flow moving in. Calculative fluid dynamics (CFD) makes it easier to guess things like power output, efficiency, and structure loads for wind turbines by accurately modeling turbulence, boundary layers, and other airflow effects. An important other topic this paper covers is how to make wind machine designs work better. To find the best setups for capturing energy while using the least amount of materials and spending the least amount of money on upkeep, engineers can combine computational fluid dynamics (CFD) with optimization methods. Blade shape, tower height, turning angle, and rotor speed are just some of the design factors that this optimization process looks at. We can make very efficient and cost-effective wind turbine systems that are perfect for each spot by using CFD-driven optimization methods and running models and analyses over and over again. For better confidence and accuracy in turbine design predictions, this study talks about how to combine CFD models with actual confirmation. Engineers can make sure that CFD models work in a wide range of situations by comparing modeling results with readings taken in the real world. It also helps to improve the models. This paper emphasizes how important CFD is to the progress of wind energy technology by giving a thorough look at its uses in designing turbines and their optimization, proof, and development. Potential for improving the efficiency and long-term viability of wind power generation stays high as long as more study and new ideas are put into CFD-driven methods.

Life cycle assessment of active spring frost protection methods in viticulture: A framework to compare different technologies

In viticulture, the risk of spring frost is mainly due to earlier budbreak, increasing the vulnerability of buds and green organs to freezing temperatures. Active Spring Frost Protection Methods (ASFPMs) aim to mitigate this risk by increasing the temperature in the bud area. ASFPMs are often seen as highly labour-intensive and resource consuming practices. ASFPM technologies are diverse and influenced by different external drivers, affecting differently their application strategies and the required equipment for efficiency. This study proposes a framework for analysing and comparing ASFPMs’ potential environmental impacts using Life Cycle Assessment (LCA) methodology. We illustrate this proposal with an example: Winter cover, wind machine, sprinkler and anti-frost candles comparison in Loire Valley conditions. We modeled the attributional LCAs with Impact world + characterisation method using Abribalyse 3.1 and Ecoinvent 3.8 databases. Application and climatic scenarios were elaborated to set conditions of ASFPMs use. The overall combination of attributional LCAs and external scenarios designs a context-specific LCA. Required time of application for each ASFPM to protect 1 ha during frost hours was determined using linear regression of ASFPM application time in function of total seasonal frost hours based on a recent decade (2013–2023). Sensitivity analysis consisted in varying frost hours theoretically with a step of 1 unit, using the lowest and highest frost hour numbers from 2013 to 2023 as boundaries. Overall, the ranking between ASFPM environmental scores changes based on the theoretical frost duration. The implementation of context-specific elements allowed for the development of system boundaries in attributional LCA, enabling the analysis and comparison of different types of technologies. The framework of this study showed its relevance in the context of ASFPM technologies through a concrete example in Loire Valley viticulture. Future research may consider other contextual elements and ASFPM technologies. This framework could be used in different fields of study to analyse and compare contrasted technologies in terms of environmental impacts.

Reconstruction of missing wind data based on limited wind pressure measurements and machine learning

In structural health monitoring (SHM), wind field monitoring sometimes suffers from data loss owing to monitoring device failure, which inevitably creates barriers to subsequent data analysis and data mining. To this end, a novel strategy for reconstructing missing wind field data based on machine learning (ML) utilizing limited wind pressure measurements is proposed in this paper. Several ML algorithms, including decision tree, random forest, gradient boosting regression tree, support vector regression, Gaussian process regression, and backpropagation neural network, are employed to characterize potential relationships between wind pressure information (including time series and statistical parameters of wind pressures) and wind field information (e.g., wind direction and wind speed). Moreover, the effect of input information (including the type of input variables as well as the number and location of pressure transducers providing input data) on reconstruction performance and efficiency is investigated. Field measured records from an SHM system in a 600-m-high supertall building during typhoons are utilized to validate the feasibility and robustness of the proposed strategy. The results show that the presented strategy can effectively reconstruct missing wind field information in the SHM of the skyscraper during typhoons. Compared with the time series of wind pressures, selecting statistical parameters of wind pressures as input variables can effectively improve the performance and efficiency of reconstruction models. Choosing appropriate input information (e.g., using multiple input variables, adopting data from a larger number of pressure transducers, and utilizing data from pressure transducers closer to an anemometer) is beneficial for enhancing the performance of reconstruction models.

Heat Transfer Process of the Tea Plant under the Action of Air Disturbance Frost Protection

Wind machines based on the air disturbance method are progressively employed to mitigate frost damage within the agricultural machinery frost protection. These devices are utilized during radiative frost nights to disrupt near-surface thermal inversion through air mixing. Despite this application, the fundamental mechanisms underlying these mixing processes are not well comprehended. In this research, numerical simulations were conducted using COMSOL Multiphysics software version 6.0 to simulate the flow and heat transfer processes between the thermal airflow and both the tea canopy and stems. The results indicated that due to obstruction from the canopy cross-section, the airflow velocity on the contact surface rapidly increased. As the airflow further progressed, the high-speed region of the airflow gradually approached the canopy surface. Turbulent kinetic energy increased initially on the windward side of the canopy cross-section and near the top interface. On the windward side of the canopy, due to the initial impact of the thermal airflow, rapid heating occurred, resulting in a noticeable temperature difference between the windward and leeward sides within a short period. In the interaction between airflow and stems, with increasing airflow velocity, fluctuations and the shedding of wake occurred on the leeward side of the stems. The maximum sensible heat flux at the windward vertex of the stem increased significantly with airflow velocity. At an airflow velocity of 2.0 m/s, the maximum heat flux value was 2.37 times that of an airflow velocity of 1.0 m/s. This research utilized simulation methods to study the interaction between airflow and tea canopy and stems in frost protection, laying the foundation for further research on the energy distribution in tea ecosystem under the disturbance of airflow for frost protection.

Multi-stage exponential model based on subspace clustering distribution for bearing remaining useful life prediction

As one of the key components in rotating machinery, the rolling element bearing has been widely used in actual production, such as wind turbines, vehicles and machine tools. A bearing’s remaining useful life (RUL) is an important indicator for its performance assessment, which is related to maintenance and production safety. To overcome the insensitivity of the conventional health indicator (HI) on bearing degradation assessment, this study proposes a subspace clustering method based on manifold learning to evaluate the evolution of health status, which describes the degenerate distribution via a two-class model and realizes the identification of the degradation of each stage. Motivated by the inconsistent degradation process in the application of actual bearing, this study proposes a multi-stage degradation identification criterion in an adaptive way, which can effectively identify different degradation rates of bearing. Based on the different degradation states, a multi-stage degradation exponential model is established to accurately predict the RUL. The effectiveness of the proposed method is validated through open datasets. The experimental results prove that the proposed method can effectively identify different degradation rates and accurately give the boundary time of the multi-stage degradation. The RUL prediction accuracy is significantly improved compared with traditional HI.

Dynamic Modelling of Wind Farms: a Comparative Study Between Two Modelling Approaches.

World’s energy needs have encouraged wind power sources. An increasing number of wind farms are being connected to the power grid and accurate wind farm models need to be developed to study the dynamic behaviour of them. This paper reports an investigation to compare two different modelling approaches of a real wind farm. This study is applied to the dynamic model of a real wind farm located at El Perdón, in Navarre, (Northern Spain). It consists of 30 Squirrel Cage Induction Machine (SCIM) wind generators of 660 kW and a nominal voltage of 690V. The wind farm feeds three local loads at 66 kV and is connected through two transformers to the network of 220KV.

CFRP drive shaft damage identification and localization based on FBG sensing network and GWO-BP neural networks

Carbon Fiber Reinforced Plastic (CFRP) has the advantages of light weight, high strength, small coefficient of thermal expansion, good vibration damping performance, etc., the use of its production drive shaft can reduce weight, improve the intrinsic frequency, transmission efficiency and precision, and has been used in helicopters, heavy duty machine tools, wind turbines, ships and other high-end equipment. Once the damage occurs, it will cause adverse effects such as reduction of mechanical efficiency of the system, degradation of transmission system performance, and decrease of reliability and safety. The existing non-destructive testing technology for carbon fiber reinforced plastic has limitations such as inconvenient use, complex equipment, and no real-time online monitoring. To address this problem, this paper adopts Fiber Bragg Gratings (FBG) sensors to obtain real-time strain field data of CFRP drive shaft, and uses the linear and nonlinear mapping ability of Back Propagation Neural Network (BP neural network) to identify and locate the damage of CFRP drive shaft. At the same time, the GWO optimization algorithm is used to optimize the BP neural network to construct a CFRP drive shaft damage identification and localization system based on FBG sensing network and GWO-BP neural network. The experiments show that the CFRP drive shaft damage identification and localization system constructed in this paper can obtain the CFRP drive shaft strain field information in real time and accurately identify the damage and its location.

Controlling PMSG Wind Turbine Connected to Three Level Neutral Point Clamped Inverter Based on MPC

Wind energy generation systems are facing increasing importance in terms of harmonic distortion control and power quality standards. In recent years, variable speed has also risen rapidly due to advancements in power electronics. The three key elements of a wind power conversion system (WECS) are electrical generators, machine-side converters, and grid-side converters. It should be noted that the permanent magnet synchronous generator is one of the most often used types of electrical generators due to its special qualities. When wind energy is converted to the proper electrical power, it may be injected into the utility grid using either synchronous generators or grid-side converters. Therefore, an investigation of several topologies and control strategies is required to determine which is the best for the machine-side converter. The use of model predictive control (MPC) for a surface mount permanent magnet synchronous machine wind turbine is the main topic of this work. The machine's electrical model is the one that was utilized. The power converters are selected considering the intricate and highly nonlinear aerodynamic system of the wind turbines. MPC is a promising control technique despite being a relatively recent application in power electronics. In addition, MATLAB and Simulink are used to build the MPC code and offer a simplified model of the turbine.

Design and optimization of intelligent orchard frost prevention machine under low-carbon emission reduction

In this paper, a self-heating small frost prevention machine is designed according to the agricultural frost prevention demand in mountainous and hilly areas. Firstly, the frost prevention machine's overall structure and key components are meticulously designed and modeled using UG 3D modeling software, tailored to the specific characteristics of the stepped planting environment in the mountainous area. At the same time, the flow field of the frost prevention machine is calculated and analyzed using jet diffusion theory and thermodynamic theory. The frost prevention machine's wind speed and temperature attenuation models are established, and subsequently fine-tuned and validated using Fluent to optimize its attenuation performance and operational range. To enhance frost prevention performance, a multi-objective bionic optimization strategy is applied to systematically optimize the structural and working parameters of the machine. Moreover, an adaptive mutation hybrid frog jump algorithm is introduced to further enhance the optimization accuracy. The simulation experimental results show that structural improvements can reduce axial speed and temperature attenuation loss by approximately 45%, while slightly increasing the machine's power consumption. On the other hand, optimizing working parameters enhances wind resistance and reduces overall power consumption. The actual experimental results show that the optimized frost prevention machine achieves a frost prevention coverage radius of 10 m within the orchard, even under maximum ambient wind speeds of 2 m/s, and it dynamically adapts to environmental changes. Additionally, the synergy of multiple frost prevention machines, powered by solar panels, enhances the overall frost prevention efficiency, leading to improved economy and reduced energy consumption.

Modeling Northern Highbush Blueberry Cold Hardiness for the Pacific Northwest

Freezing temperatures in fall, winter, and spring can cause damage to multiple perennial fruit crops including northern highbush blueberry (Vaccinium corymbosum). Predictive modeling for lethal temperatures allows producers to make informed decisions about freeze mitigation practices but is lacking for northern highbush blueberry grown in the Pacific Northwest. If buds are hardier than air temperatures, unnecessary use of propane heaters and/or wind machines is costly. In contrast, use of heaters and/or wind machines during freezing, damaging temperatures can minimize crop damage and potential yield loss. The objective of this study was to model cold hardiness across multiple cultivars of northern highbush blueberry grown in various regions in Washington, USA, and to generate predictive cold hardiness models that producers in the Pacific Northwest could use to inform freeze mitigation. Multiple years of experimental cold hardiness data were collected on four cultivars of northern highbush blueberry grown in western and eastern Washington, USA. Freeze chambers were used to reduce bud temperatures systematically, after which buds were dissected and bud survival was assessed. A generalized linear mixed model with a binomial response and logit link was fit to each cultivar to characterize the relationship between bud survival, freezer temperature, recent air temperatures, and growing degree days from fall acclimation to late winter/spring deacclimation. Model simulation was performed to obtain marginal-scale lethal temperature estimates. Model error estimation was performed using cross validation. Results show cultivar-specific cold hardiness models can be generated, and model development and use can help growers make more informed decisions regarding freeze protection that also minimizes costly applications of freeze protection when unnecessary. Furthermore, such models can be adapted to other blueberry growing regions and cultivars experiencing similar climactic conditions.

Analysis of the spatial variability of temperature with the aim of improving the location of wind machines

Analysis of the spatial variability of temperature with the aim of improving the location of wind machines

Numerical and Experimental Analysis of Horizontal-Axis Wind Turbine Blade Fatigue Life.

Horizontal-axis wind turbines are the most popular wind machines in operation today. These turbines employ aerodynamic blades that may be oriented either upward or downward. HAWTs are the most common non-conventional source of energy generation. These turbine blades fail mostly due to fatigue, as a large centrifugal force acts on them at high rotational speeds. This study aims to increase a turbine's service life by improving the turbine blades' fatigue life. Predicting the fatigue life and the design of the turbine blade considers the maximum wind speed range. SolidWorks, a CAD program, is used to create a wind turbine blade utilizing NACA profile S814. The wind turbine blade's fatigue life is calculated using Morrow's equation. A turbine blade will eventually wear out due to several forces operating on it. Ansys software is used to analyze these stresses using the finite element method. The fatigue study of wind turbine blades is described in this research paper. To increase a turbine blade's fatigue life, this research study focuses on design optimization. Based on the foregoing characteristics, an improved turbine blade design with a longer fatigue life than the original one is intended in this study. The primary fatigue parameters are the length of a chord twist angle and blade length. The experimental data computed with the aid of a fatigue testing machine are also used to validate the numerical results, and it is found that they are very similar to one another. By creating the most effective turbine blades with the longest fatigue life, this research study can be developed further. The most effective turbine blades with the longest fatigue life can be designed to further this research investigation.

Grape Cold Hardiness Prediction via Multi-Task Learning

Cold temperatures during fall and spring have the potential to cause frost damage to grapevines and other fruit plants, which can significantly decrease harvest yields. To help prevent these losses, farmers deploy expensive frost mitigation measures, such as, sprinklers, heaters, and wind machines, when they judge that damage may occur. This judgment, however, is challenging because the cold hardiness of plants changes throughout the dormancy period and it is difficult to directly measure. This has led scientists to develop cold hardiness prediction models that can be tuned to different grape cultivars based on laborious field measurement data. In this paper, we study whether deep-learning models can improve cold hardiness prediction for grapes based on data that has been collected over a 30-year time period. A key challenge is that the amount of data per cultivar is highly variable, with some cultivars having only a small amount. For this purpose, we investigate the use of multi-task learning to leverage data across cultivars in order to improve prediction performance for individual cultivars. We evaluate a number of multi-task learning approaches and show that the highest performing approach is able to significantly improve over learning for single cultivars and outperforms the current state-of-the-art scientific model for most cultivars.

An evaluation of the temperature inversion conditions to determine the usefulness of wind machines for frost protection in Mendoza, Argentina

An evaluation of the temperature inversion conditions to determine the usefulness of wind machines for frost protection in Mendoza, Argentina

Wind machines for frost damage mitigation: A quantitative 3D investigation based on observations

Wind machines have been increasingly used for frost damage mitigation in the agricultural community. During radiative frost nights, wind machines are used to erode near-surface thermal inversion by air mixing. The underlying mixing processes remain poorly understood. A full picture of warming effects caused by air mixing requires measurements with wide coverage and high resolution. Our study aimed to quantify the magnitude and area of warming by air mixing and identify the characteristic mixing processes downwind and upwind. We installed 9 km of fiber optic cables in a 6.75 ha orchard block, creating two horizontal planes and three vertical profiles. Quasi-3D temperature responses with spatial sampling and temporal resolution of 25 cm and 10 s, respectively, were obtained before and during machine operation. We found a 50% reduction of the local inversion strength (8 K) over 0.42 ha at 1 m and 0.46 ha at 2 m height. The warming area for a 30% reduction extends to 2.81 and 2.52 ha, respectively. As the propeller rotates 360°, the weak background wind substantially impacts the air mixing processes downwind and upwind. When jets blow along with background wind, the warming plumes arrive earlier than the jet due to horizontal advection from earlier warmed sections. The warming plumes consequently accumulate downwind and penetrate deep into the canopy. In contrast, in upwind direction, wind drag resistance causes warming plumes arrive later than the jet. Quadrant analysis reveals that flux transport during the machine operation is dominated by sweeping and ejection motions. Intermittent downdrafts of warm air and updrafts of cool air result in efficient vertical heat exchange. This feature makes wind machines highly effective in raising canopy airspace temperature to mitigate frost damage.

Comprehensive Analysis of the Impact of the Icing of Wind Turbine Blades on Power Loss in Cold Regions

Blade icing often occurs on wind turbines in cold climates. Blade icing has many adverse effects on wind turbines, and the loss of output power is one of the most important effects. With the increasing emphasis on clean energy around the world, the design and production of wind turbines tend to be large-scale. So this paper selected the 15 MW wind turbine provided by NREL (American Renewable Energy Laboratory) to study the influence of blade icing on output power. In this paper, a multi-program coupled analysis method named CFD-WTIC-ILM (CFD: Computational fluid dynamics; WTIC: Wind Turbine Integrated Calculation; ILM: Ice loss method) was proposed to analyze the whole machine wind turbine. Firstly, Fensap-ice was used to simulate the icing of the wind turbine blades, and then the icing results were input into WTIC for the integrated calculation and analysis of the wind turbine. Then, the WTIC calculation results were used to simulate SCADA (supervisory control and data acquisition) data and input into ILM to calculate the power loss. Finally, this paper analyzed the comprehensive influence of icing on output power. The calculation results show that the ice mainly accumulates on the windward side of the blade. Icing has a great influence on the aerodynamic characteristics of the airfoil, leading to a significant decrease in the power curve. The rated wind speed is pushed from 10.59 m/s to 13 m/s. The power loss of the wind turbine in the wind speed optimization stage is as high as 37.48%, and the annual power loss rate caused by icing can reach at least 22%.

Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review

As grid-connected wind farms become more common in the modern power system, the question of how to maximize wind power generation while limiting downtime has been a common issue for researchers around the world. Due to the complexity of wind turbine systems and the difficulty to predict varying wind speeds, artificial intelligence (AI) and machine learning (ML) algorithms have become key components when developing controllers and control schemes. Although, in recent years, several review papers on these topics have been published, there are no comprehensive review papers that pertain to both AI and ML in wind turbine control systems available in the literature, especially with respect to the most recently published control techniques. To overcome the drawbacks of the existing literature, an in-depth overview of ML and AI in wind turbine systems is presented in this paper. This paper analyzes the following reviews: (i) why optimizing wind farm power generation is important; (ii) the challenges associated with designing an efficient control scheme for wind farms; (iii) a breakdown of the different types of AI and ML algorithms used in wind farm controllers and control schemes; (iv) AI and ML for wind speed prediction; (v) AI and ML for wind power prediction; (vi) AI and ML for mechanical component monitoring and fault detection; and (vii) AI and ML for electrical fault prevention and detection. This paper will offer researchers and engineers in the wind energy generation field a comprehensive review of the application of AI and ML in the control methodology of offshore and onshore wind farms so that more efficient and robust control schemes can be designed for future wind turbine controllers.

Robust intelligent fault diagnosis strategy using Kalman observers and neuro-fuzzy systems for a wind turbine benchmark

A wind turbine (WT) is an electromechanical system that often operates under a wide range of production conditions. These electrical systems are nowadays expanding rapidly, and they have considerable importance due to their efficiency as renewable energy sources. This led to proposing an innovative and efficient solution with intelligent systems to maintain and ensure the safe and stable operation of these dynamic systems. Maintenance tasks are based on the development of high-performance diagnostic tools, which consist in detecting and locating correctly and upstream the various failures affecting this wind machine. Where, the condition monitoring and supervision systems must rely on reliable fault diagnosis techniques in order to: avoid breakdowns and unscheduled shutdowns, improve their operation, and increase their energetic performances. In order to ensure adequate maintenance actions for the wind system, the purpose of this article is to propose and develop a robust and intelligent fault diagnosis structure. In this work, Kalman filters (KF) as state estimators are used to observe the output states of the sub-systems in order to generate the appropriate residuals evaluated by predetermined thresholds. Adaptive and hybrid network-based fuzzy inference systems (ANFIS) have been employed for the evaluation and classification stages of the detected faults to minimize the degradation of the wind turbine. All possible faults of wind turbine systems, sensors, and actuators are tested and investigated in all parts: pitch angle systems, drive, and generator with converter. The developed fault detection and identification structure are tested on a horizontal WT benchmark model using different scenarios and faults. The simulation results show the ability of the proposed and developed diagnostic methodology to detect the faults occurring efficiently and correctly in the machine. Thus, by using this robust diagnostic strategy, the condition monitoring system can maintain and ensure stable and safe operation to generate sufficient electrical power.

Plant–atmosphere heat exchange during wind machine operation for frost protection

To mitigate spring frost damage, fruit farmers use wind machines to mix warm overlying air down to the vegetation. Up to this point, studies on wind machine efficiency have focused on air temperatures. The temperature of different plant organs during operation remains unknown, while critical for the actual degree of frost damage. With Distributed Temperature Sensing we measured vertical in-canopy air temperature profiles in a pear orchard in the Netherlands and thermistors were installed to determine the plant tissue temperatures. We found that to optimize wind machine operation, it is important to consider two effects of a wind machine: (1) mixing of stratified air above and into the canopy layer and (2) erosion of the leaf boundary layer to facilitate plant–air heat exchange. We show how foliage reduces plume penetration to the ground with distance to the wind machine. Due to this blocking at least 15 rotations (∼ 75 min) are needed for optimal mixing. Leaf temperatures lag behind air temperatures, due to strong radiative cooling. We found that over the rotation cycle of a wind machine the temperature difference between leaf and air is variable as convective warming repeatedly dominates over radiative cooling. This is different for flowers and shoots due to different heat capacities. Thin flower petals store little heat and are almost in direct equilibrium with air temperature changes. Shoots, with their higher heat capacity and lower surface/volume ratio, store more heat during the day that is slowly released at night. This discrepancy between plant and air temperature should be considered for frost damage prediction.

Wind Power Prediction Based on Machine Learning and Deep Learning Models

Wind power is one of the sustainable ways to generate renewable energy. In recent years, some countries have set renewables to meet future energy needs, with the primary goal of reducing emissions and promoting sustainable growth, primarily the use of wind and solar power. To achieve the prediction of wind power generation, several deep and machine learning models are constructed in this article as base models. These regression models are Deep neural network (DNN), k-nearest neighbor (KNN) regressor, long short-term memory (LSTM), averaging model, random forest (RF) regressor, bagging regressor, and gradient boosting (GB) regressor. In addition, data cleaning and data preprocessing were performed to the data. The dataset used in this study includes 4 features and 50530 instances. To accurately predict the wind power values, we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization (SFS-PSO) to optimize the parameters of LSTM network. Five evaluation criteria were utilized to estimate the efficiency of the regression models, namely, mean absolute error (MAE), Nash Sutcliffe Efficiency (NSE), mean square error (MSE), coefficient of determination (R2), root mean squared error (RMSE). The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99% in predicting the wind power values.