Coordinated power management strategy for reliable hybridization of multi-source systems using hybrid MPPT algorithms

This research discusses the solar and wind sourcesintegration in aremote location using hybrid power optimization approaches and a multi energy storage system with batteries and supercapacitors. The controllers in PV and wind turbine systems are used to efficiently operate maximum power point tracking (MPPT) algorithms, optimizing the overall system performance while minimizing stress on energy storage components. More specifically, on PV generator, the provided method integrating the Perturb & Observe (P&O) and Fuzzy Logic Control (FLC) methods. Meanwhile, for the wind turbine, the proposed approach combines the P&O and FLC methods. These hybrid MPPT strategies for photovoltaic (PV) and wind turbine aim to optimize its operation, taking advantage of the complementary features of the two methods. While the primary aim of these hybrid MPPT strategies is to optimize both PV and wind turbine, therefore minimizing stress on the storage system, they also aim to efficiently supply electricity to the load. For storage, in this isolated renewable energy system, batteries play a crucial role due to several specific benefits and reasons. Unfortunately, their energy density is still relatively lower compared to some other forms of energy storage. Moreover, they have a limited number of charge-discharge cycles before their capacity degrades significantly. Supercapacitors (SCs) provide significant advantages in certain applications, particularly those that need significant power density, quick charging and discharging, and long cycle life. However, their limitations, such as lower energy density and specific voltage requirements, make them most effective when combined with other storage technologies, as batteries. Furthermore, their advantages are enhanced, result a more dependable and cost-effective hybrid energy storage system (HESS). The paper introduces a novel algorithm for power management designed for an efficient control. Moreover, it focuses on managing storage systems to keep their state of charge (SOC) within defined range. The algorithm is simple and effective. Furthermore, it ensures the longevity of batteries and SCs while maximizing their performance. The results reveal that the suggested method successfully keeps the limits batteries and SCs state of charge (SOC). To show the significance of system design choices and the impact on the battery's SOC, which is crucial for the longevity and overall performance of the energy storage components, a comparison in of two systems have been made. A classical system with one storage (PV/wind turbine/batteries) and the proposed system with HESS (PV/wind turbine system with batteries). The results show that the suggested scenario investigated with both wind and solar resources appears to be the optimum solution for areas where the two resources are both significant and complementary. The balance between the two resources seems to contribute to less stress on storage components, potentially leading to a longer lifespan. An economical study has been made, using the Homer Pro software, to show the feasibility of the proposed system in the studied area.

Optimal detection and classification of grid connected system using MSVM-FSO technique

This paper, a hybrid method, is proposed for protecting the hybrid photovoltaic (PV) and wind turbine (WT) system. The proposed protecting method is the hybrid wrapper of both the multiple support vector machine (MSVM) and firebug swarm optimization (FSO), commonly named as MSVM-FSO method. The proposed technique is diagnosing the appropriate fault occurring in the hybrid system. The main purpose of the proposed system is to assure the system with lower complexity for the fault diagnosis and detection (FDD) for improving the power quality (PQ) of hybrid method. Here, the MSVM approach is used to detect the fault conditions of grid-tied system. To evaluate the events of voltages, fault and the currents of hybrid systems are analyzed at the feeder of buses. The FSO categorizes the types of fault, which is occurred in grid-connected system. By then, the proposed method's performance is done in the MATLAB software and it is contrasted with different existing methods. From this, the proposed method provides accuracy as 99.7% and efficiency as 98%, which is high compared to existing methods.

Numerical investigation of sand erosion rate in a horizontal axis wind turbine

Renewable energy represents an important alternative solution for many energy problems nowadays and a tool for a healthier environment by reducing carbon footprints resulting from burning fossil fuels. However, more work needs to be done towards maximizing the energy produced from renewable energy methods and making sure that the infrastructure used stays in service for a longer duration. Sand erosion phenomena is responsible for the degradation of the wind turbine blades and hence the decrease in their performance and life. In the current research, a numerical study of both performance and sand erosion of a Small-Scale Horizontal Axis Wind Turbine (SS-HAWT) is carried out. This study introduces new sights of instantaneous and forecasted erosion rates within the blade of the wind turbines. Three-dimensional E216 airfoil blades of radius 0.5 m are established according to blade element momentum theory. Sand particles with different mass flow rates of 0.001, 0.002 and 0.003 kg/s and uniform diameters of 50, 100 and 200 μm have been selected as eroding particles under two different average air velocities of 8 m/s and 10 m/s. The results indicate that the performance of wind turbines is enhanced as the flow separation at the suction side is shifted to the trailing edge. Furthermore, the optimum tip speed ratio is about 5 at an air velocity of 8 m/s with a power coefficient of 0.432. In terms of erosion findings, V-shaped scars are reported near the leading edge of the blades. In addition, the instantaneous erosion rate grows exponentially with the tip speed ratio. Therefore, the yearly prediction of maximum erosion depth at the optimum operating conditions is obtained to be 5.7 mm/year in some spots of the turbine blades.

An efficient MPPT techniques for inter-harmonic reduction in grid connected hybrid wind and solar energy systems

In this work, the operation of photovoltaic system, wind turbine driven doubly fed induction generator along with battery has been observed. Also, a searching space minimization-based artificial bee colony scheme is developed for tracking the maximum power in a doubly fed induction generator-based system. To track maximum power in solar systems, an improved adaptive reference voltage approach has been presented. Several conventional and optimization-based techniques are used by DFIG and photovoltaic systems to get around the non-linearity features in the output parameters. Regarding DFIG, the artificial bee colony method based on searching space minimization can be used to solve the shortcomings of the perturb and observe algorithm. Because of its weather-sensitive nature, it can withstand sudden changes in wind speed. The suggested searching space minimization based artificial bee colony strategy uses a mechanism for determining the range of optimal rotor speed in order to track the maximum power point more quickly. The maximum power point tracking performance of the adaptive reference voltage technique is superior to that of current perturb and observed-based systems. However, a huge processing memory is required in order to track the maximum possible power point. This paper proposes an enhanced maximum power point tracking technique based on adaptive reference voltage that does not require a memory unit. Additionally, despite sudden changes in irradiation conditions, improved adaptive reference voltage can drift-free and reliably monitor the maximum power point. The new adaptive reference voltage technique uses temperature and radiation sensors to identify the region nearest to the maximum power point. This helps the system respond more quickly. The proposed system with searching space minimization based artificial bee colony and improved adaptive reference voltage schemes displays lower inter-harmonic content in grid current compared to perturb and observe scheme. The proposed scheme has been implemented in MATLAB & simulink atmosphere and OPAL-RT displayed satisfactory results.

Joint condition monitoring framework of wind turbines based on multi-task learning with poor-quality data

Effective condition monitoring can improve the reliability of the turbine and reduce its downtime. However, due to the complexity of the operating conditions, the monitoring data is always mixed with poor-quality data. Poor-quality data mixed in monitoring tasks disrupts long-term dependency on data, which challenges traditional condition monitoring methods to work. To solve it, a joint reparameterization feature pyramid network (JRFPN) is proposed. Firstly, three different reparameterization tricks are designed to reform temporal information and exchange cross-temporal information, to alleviate the damage of long-term dependency. Secondly, a joint condition monitoring framework is designed, aiming to suppress feature confounding between poor-quality data and faulty data. The auxiliary task is trained to extract the degradation trend. The main task fights against feature confounding and dynamically delineates the failure threshold. The degradation trend and failure threshold decisions are corrected for each other to make the final joint state inference. Besides, considering the different quality of the monitoring variables, a channel weighting mechanism is designed to strengthen the ability of JRFPN. The measured data proved that JRFPN is more effective than other methods.

Calibration method of the k-ω SST turbulence model for wind turbine performance prediction near stall condition

The present study intends to enhance the accuracy of the k-ω SST turbulence model for numerical wind turbine simulation in stall condition. In order to achieve this purpose a calibration approach is proposed, and is applied to NREL S826 NTNU wind rotor. This method consists in adjusting the two turbulence model coefficients: a1 and β*, which are found to be 0.8 and 0.45 respectively. Then the calculated power and thrust coefficients are compared to the experimental results. The power coefficient results revealed that the simulation relative error at the range of tip speed ratio between 3 to 6 where light stall occurs, is reduced from 17.89 % to 4.58 % by application of calibration. The effects of calibration on flow behaviour are implemented thereafter, by analysing pressure, and skin friction coefficients distribution along the blade. The limiting streamlines on the blade suction side are examined for more flow behaviour understanding. The effects on turbulent kinetic energy around the blade are also highlighted. The main important conclusions that can be made is that calibration reduce the separation zone on the blade suction side, and limits the vortex shedding strength, leading to improve the rotor efficiency and hence to improve the model accuracy.

4E analysis and tri-objective optimization of a landfill plant integrated with power-to-gas and leachate treatment

Management of waste is essential since waste production has increased drastically. Landfilling is prevalent in controlling and managing wastes, particularly municipal solid wastes. Tackling the environmental problems of landfill is the goal of this work. The outputs of the landfill are biogas and leachate, which are hazardous to the environment. This problem can be solved by using the power-to-gas system and leachate treatment plant. The leachate has the potential to produce biogas, and the CO2 in biogas can be converted to methane in the methanation unit of power to gas. For this, power-to-gas needs the electricity in the electrolyzer, which can be provided from the surplus electricity of available renewables (here solar photovoltaics and wind turbine). Energy, exergy, economic and environmental analyses are applied to the system, and tri-objective optimization by the genetic algorithm is performed to gain optimum results. The obtained exergy efficiency from the given data is 19.03%. Also, the energy efficiency, net electricity generation, methane production rate, total annual cost, and CO2 conversion are 19.51%, 4.24 MW, 176.63 kg/h, €1.8 million, and 82.42%, respectively. In the ideal point of tri-objective optimization, the exergy efficiency, total annual cost, and CO2 conversion become 26.16%, €1.31 million, and 96.57%, respectively.

Assessment of economic, energy, and exergy efficiencies using wind measurement mast data for different wind turbines

Today, increasing concerns about greenhouse gas emissions, climate change, and resource depletion from fossil fuels have drawn attention to wind energy. In this context, wind turbine technologies are constantly evolving to eliminate such concerns by using wind energy. The wind speed from the measurement mast at a height of 80 m was used in wind turbines of different capacities and was investigated. To assess the potential of the system that produces electricity from wind energy, it has been analyzed in terms of energy, exergy, and economic. The energy and exergy efficiencies of each wind turbine were analyzed with the wind speed and meteorological data. When the average monthly power calculated for each turbine is proportioned to the turbine capacity, the energy efficiency varies between 10 and 70%. Enercon_1500 and Enercon_3050 values are high, while Enercon_3500 and Enercon_2350 have low efficiency compared to other turbines. The annual total energy production is 12.19 GWh for the highest Enercon_4200 and 4.48 GWh for the lowest Enercon_1500. The exergy efficiencies range from 20 to 79% for selected wind turbines. In the last part of the study, monthly average electricity production costs were determined by using the turbines selected for the determined region. When compared in terms of unit electricity cost, the Enercon_1500 turbine is higher, while the Enercon_4200 is lower.

Wind turbine anomaly detection based on SCADA: A deep autoencoder enhanced by fault instances

An increasing number of deep autoencoder-based algorithms for intelligent condition monitoring and anomaly detection have been reported in recent years to improve wind turbine reliability. However, most existing studies have only focused on the precise modeling of normal data in an unsupervised manner; few studies have utilized the information of fault instances in the learning process, which results in suboptimal detection performance and low robustness. To this end, we first developed a deep autoencoder enhanced by fault instances, that is, a triplet-convolutional deep autoencoder (triplet-Conv DAE), jointly integrating a convolutional autoencoder and deep metric learning. Aided by fault instances, triplet-Conv DAE can not only capture normal operation data patterns but also acquire discriminative deep embedding features. Moreover, to overcome the difficulty of scarce fault instances, we adopted an improved generative adversarial network-based data augmentation method to generate high-quality synthetic fault instances. Finally, we validated the performance of the proposed anomaly detection method using a multitude of performance measures. The experimental results show that our method is superior to three other state-of-the-art methods. In addition, the proposed augmentation method can efficiently improve the performance of the triplet-Conv DAE when fault instances are insufficient.

Intelligent fault detection scheme for constant-speed wind turbines based on improved multiscale fuzzy entropy and adaptive chaotic Aquila optimization-based support vector machine

Timely and effective fault detection is essential to ensure the safe and reliable operation of wind turbines. However, due to the complex kinematic mechanisms and harsh working environments of wind turbine equipment, it is difficult to extract sensitive features and detect faults from acquired wind turbine signals. To address this challenge, a novel intelligent fault detection scheme for constant-speed wind turbines based on refined time-shifted multiscale fuzzy entropy (RTSMFE), supervised isometric mapping (SI), and adaptive chaotic Aquila optimization-based support vector machine (ACAOSVM) is proposed. In the first step, the RTSMFE method is used to fully extract features of the wind turbine system. The time-shifted coarse-grained construction technique and a refined computing technique are adopted in the RTSMFE method to enhance the capability of traditional multiscale fuzzy entropy for measuring the complexity of signals. Subsequently, an effective manifold learning approach, SI, is applied to obtain the important and low-dimensional feature set from the high-dimensional feature set. Finally, sensitive features are fed into the ACAOSVM classifier to identify faults. The proposed ACAO algorithm is used to optimize important parameters of the SVM, thereby improving its detection performance. Simulations and wind turbine experiments verified that the proposed RTSMFE outperforms existing entropy techniques in terms of complexity measurement and feature extraction. Furthermore, the proposed ACAOSVM classifier is superior to existing advanced classifiers for fault pattern recognition. Finally, the proposed intelligent fault detection scheme can more correctly and efficiently detect wind turbine single/hybrid faults than other recently published schemes.

A comparative examination of the aerodynamic performance of various seashell-shaped wind turbines

The seashell-shaped wind turbine (spiral wind turbine SWT), a brand-new form of the horizontal axis wind turbine, is intended for metropolitan use. SWTs have the additional advantage of being installed anywhere without considering their surroundings, as they do not need to be located facing the wind direction. The present work introduces various designs of the rotor of the seashell wind turbine to achieve the greatest performance. Two types of turbine spiral profiles (logarithmic and Archimedean) are investigated with changing the turbine opening angle (θ). Utilizing the turbulence model SST k-ω, the equations of Reynolds-averaged Navier-Stokes (RANS) are solved and hence the power coefficient (C_P) is calculated. A comprehensive comparison of the findings for both configurations indicates that the turbine of the Archimedean spiral profile with θ of 60° generates the best performance. The seashell wind turbine with the Archimedean profile at a θ of 60° has a maximum C_P = 0.266825 at λ = 2.5. The seashell wind turbine with the Archimedean profile has the best performance than traditional Archimedes wind turbines which were studied previously by other researchers. The maximum percentage increase in the C_P of the seashell turbine with the Archimedean profile compared to the conventional Archimedes turbine equals 14.52%.

Computational fluid dynamics investigations over conventional and modified Savonius wind turbines

Wind turbines are devices that convert the kinetic energy present in the wind into clean, sustainable, and effectively renewable energy that could be used to generate electricity. A Savonius wind turbine is a drag-based vertical axis wind turbine (VAWT) that is known to have low noise levels and good starting characteristics even at low wind speeds. Its disadvantage lies in its low efficiency or low coefficient of performance. Exploring ways to increase the coefficient of performance, numerical investigations were carried out on different modified Savonius VAWT configurations, having different curvatures, different overlap percentages, added mini blades, and fitted out with extended surfaces. These investigations were computationally executed on Ansys Fluent™ using the sliding mesh technique. Two-dimensional simulations, on a Bach blade curvature with zero overlap as well as a half-circle and a polynomial curvature with overlap, showed that for a wind speed of 5 m/s and a tip speed ratio of 0.8, the half-circle blade curvature having an overlap of 20% performs best, yielding the highest net (average) coefficient of moment, equal to 0.3065. Results also show that the addition of mini blades to this optimal configuration produces a slight improvement in the coefficient of moment. However, the addition of extended surfaces onto the blades caused the minimum coefficient of moment to be a substantial negative value and thus resulting in a much lower value for the turbine's average coefficient of moment.

Wind farm layout optimization through optimal wind turbine placement using a hybrid particle swarm optimization and genetic algorithm

The placement and configuration of wind turbines (WTs) are the key factors in determining the performance and energy output of a wind farm (WF). This involves considering various elements such as wind speed, wind direction, and the interspacing between turbines in the design process. To achieve an optimized and consistent wind farm layout optimization (WFLO) for maximum output power, a novel hybrid algorithm hybrid particle swarm optimization and genetic algorithm (HPSOGA), combining particle swarm optimization (PSO) and genetic algorithm (GA), is proposed. HPSOGA can effectively handle problems with multiple local optima, as PSO explores multiple regions and GA refines solutions found by PSO. The framework has two phases, where PSO improves initial parameters in the first phase, and parameters are adjusted in the second phase for improved fitness. The wake effect is analyzed using the Jenson-Wake model, and the objective function considers the total cost of WTs and the power output of the WF. The interspacing of WTs is evaluated by the rule of thumb. HPSOGA outperforms other methods such as GA, BPSO-TVAC, L-SHADE, BRCGA, and EO-PS, producing better results in terms of total output power generation. The simulation results validate the reliability of HPSOGA in WFLO.

Pile driving and drilling underwater sounds impact the metamorphosis dynamics of Pecten maximus (L., 1758) larvae

One of the biggest challenges of the 21st century is to reduce carbon emissions and offshore wind turbines seem to be an efficient solution. However, during the installation phase, high levels of noise are emitted whose impacts remain not well known, particularly on benthic marine invertebrates displaying a bentho-planktonic life-cycle. For one century, larval settlement and subsequent recruitment has been considered as a key topic in ecology as it determines largely population renewal. Whereas several recent studies have shown that trophic pelagic but also natural soundscape cues could trigger bivalve settlement, the role of anthropogenic noise remains poorly documented. Therefore, we conducted experiments to assess potential interacting effects of diet and pile driving or drilling sounds on the great scallop (Pecten maximus) larval settlement. We demonstrate here that pile driving noise stimulates both growth and metamorphosis as well as it increases the total lipid content of competent larvae. Conversely, drilling noise reduces both survival and metamorphosis rates. For the first time, we provide evidence of noise impacts associated to MREs installation on P. maximus larvae and discuss about potential consequences on their recruitment.

Monte Carlo modeling of tornado hazard to wind turbines in Germany

Using a Monte Carlo hazard modeling approach, this study assesses the damage and losses to wind turbines caused by tornadoes over Germany. The model combines observed climatology of tornadoes, exposure map of wind turbines and their capacities, vulnerability curve (first developed in this study), and costs of wind turbines to estimate the likelihood of financial losses of different magnitudes. The losses are presented in terms of aggregated and highest annual losses as a function of the return period. Deterministic modeling of the vulnerability function produced larger variability of the most severe losses (rare, but high-intensity events) compared to the pseudorandom sampling of the damage from the vulnerability curve. Doubling the number of current wind turbines results in smaller expected losses than installing fewer but more powerful units (repowerment). Tornadoes are most frequent in the summer months when the capacity factors of wind farms are the lowest and the electricity consumption is also lower than in the winter months. By repeatedly placing a tornado track of a fixed size over the same wind farm, we further investigated the sensitivity of our model to different parameters. The probability of tornadoes of different intensities hitting a wind turbine over Germany is also calculated.

Wind farms dry surface soil in temporal and spatial variation

Wind energy is renewable and clean; however, the long-term operation of wind turbines can affect local climates. Soil moisture affects ecosystem balance, so determining the impact of wind farms on soil moisture is important. However, there has been little research on this, and only the impacts of wind farms on climate and vegetation have been considered. This study focuses on wind farms located in the grasslands of China. We analyzed changes in soil moisture in different wind directions and seasons and then judged the impacts of wind turbine operation on soil moisture. Our research shows that the operation of wind turbines will cause significant drying of soil, and this drought effect differs significantly according to season and wind direction. Our results show that 1) the soil moisture within wind farms decreases most significantly, with a decrease of 4.4 % observed; 2) in summer and autumn, the declines in soil moisture in the downwind direction are significantly greater than those in the upwind direction, with the opposite occurring in spring. (3) Wind farms aggravate the soil drying in grassland areas, which may have impacts on grassland ecosystems. Therefore, when building wind farms, we need to better understand their impacts on the environment.

Wind farms dry surface soil in temporal and spatial variation

Wind farms have been proved to have potential impact on the ecology. As an important ecological factor, soil moisture has a great impact on the ecosystem. Therefore, it is of great significance to explore the effect of wind farms on soil moisture. At present, the remote sensing data can be used to calculate the soil moisture of wind farm conveniently, but its spatial resolution is poor. Moreover, the measured soil moisture can't express the spatial difference. Therefore, through the effective combination of remote sensing data and measured data, this method can accurately judge the impact of wind farm on soil moisture. This method investigated wind farms located in the grasslands of China. Remote sensing images and field data were used to explore the area and extent of influence of wind farms on grassland soil moisture. We use Landsat images and field measurements to derive a linear relationship between the soil moisture and the TVDI, which was calculated based on the land surface temperature and NDVI, was developed in this work. The correlation was used to reverse spatial distribution map of soil moisture before and after the construction of wind farms. The diurnal and seasonal variation of the influence of the wind farm on the grassland soil moisture was also judged.•This method of combining measurement and remote sensing provides a reference for analysing the influence of wind farms on soil moisture.•This method can be used for reference to compare the meteorological factors of different wind directions before and after the construction of wind farms.

A velocity-guided Harris hawks optimizer for function optimization and fault diagnosis of wind turbine

Harris hawks optimizer (HHO) is a relatively novel meta-heuristic approach that mimics the behavior of Harris hawk over the process of predating the rabbits. The simplicity and easy implementation of HHO have attracted extensive attention of many researchers. However, owing to its capability to balance between exploration and exploitation is weak, HHO suffers from low precision and premature convergence. To tackle these disadvantages, an improved HHO called VGHHO is proposed by embedding three modifications. Firstly, a novel modified position search equation in exploitation phase is designed by introducing velocity operator and inertia weight to guide the search process. Then, a nonlinear escaping energy parameter E based on cosine function is presented to achieve a good transition from exploration phase to exploitation phase. Thereafter, a refraction-opposition-based learning mechanism is introduced to generate the promising solutions and helps the swarm to flee from the local optimal solution. The performance of VGHHO is evaluated on 18 classic benchmarks, 30 latest benchmark tests from CEC2017, 21 benchmark feature selection problems, fault diagnosis problem of wind turbine and PV model parameter estimation problem, respectively. The simulation results indicate that VHHO has higher solution quality and faster convergence speed than basic HHO and some well-known algorithms in the literature on most of the benchmark and real-world problems.

A hybrid equilibrium algorithm and pattern search technique for wind farm layout optimization problem

The layout optimization-based model is a significant issue for increasing the utilization rate of the wind farm and minimizing its cost per unit of power. For the accurate and reliable wind farm layout optimization design, a novel algorithm based on the hybridization of equilibrium optimizer (EO) and pattern search (PS) technique, named EO-PS, is proposed in this paper. The proposed EO-PS operates in two phases. The first phase implements the EO to explore the search space and reach the promising regions by using an equilibrium pool of elite particles, which contributes to maintaining the diversity of solutions. The second phase integrates the PS to guide the searching towards better vicinities and achieve a high-quality solution by using its detecting and pattern movements to boost the exploitation ability of the proposed method in the last steps. The presented EO-PS algorithm is implemented to deal with single and multi-objective optimization aspects of wind farm layout optimization using different wind speed scenarios. Furthermore, the proposed algorithm is investigated on irregular land space in the Gulf of Suez-Red Sea in Egypt to achieve the optimal layout configuration of the wind farm, which is vital for possible practical planning trends. The comprehensive results and analyses have affirmed that the proposed EO-PS can achieve competitive performance compared to the other state-of-the-state methods, especially in terms of the solution' quality and reliability.

Wind and wind power characteristics of the eastern and southern coastal and northern inland regions, South Africa

The objective of this work is to understand the fluctuating nature of wind speed characteristics on different time scales and to find the long-term annual trends of wind speed at different locations in South Africa. The hourly average mean wind speed values over a period of 20 years are used to achieve the set objective. Wind speed frequency, directional availability of maximum mean wind speed, total energy, annual energy yield and plant capacity factors are determined for seven locations situated both inland and along the coast of South Africa. The highest mean wind speed (6.01 m/s) is obtained in Port Elizabeth and the lowest mean wind speed (3.86 m/s) is obtained in Bloemfontein. Wind speed increased with increasing latitudes at coastal sites (Cape Town, Durban, East London and Port Elizabeth), while the reverse trend was observed at inland locations (Bloemfontein, Johannesburg and Pretoria). Noticeable annual changes and relative wind speed values are found at coastal locations compared to inland sites. The energy pattern factor, also known as the cube factor, varied between a minimum of 1.489 in Pretoria and a maximum of 1.858 in Cape Town. Higher energy pattern factor (EPF) values correspond to sites with fair to good wind power potential. Finally, Cape Town, East London and Port Elizabeth are found to be good sites for wind power deployments based on the wind speed and power characteristics presented in this study.

[Infrasound - implications for human medicine]

Infrasound describes ubiquitous, low-frequency sound (< 20 Hz) in the environment with a long wavelength below the median hearing threshold, which can nevertheless be heard and tactilely perceived, depending on the sound pressure level and frequency spectrum. In nature, infrasound emissions usually occur only in the low-threshold range. Nevertheless, after strong and chronic exposure to usually artificially generated infrasound emissions, various effects on the ear and the body, sometimes questionably critical to health, can be observed. Correct measurement and assessment of infrasound sources is complex and controversial. Established guidelines are scarce. Innovative research areas include infrasound monitoring for evaluation of natural events and infrasound applications in medicine. In the future, it is hoped that new insights will be gained from infrasound research and that a more extensive classification in occupational medicine will be possible.

An open design for a low-cost open-loop subsonic wind tunnel for aerodynamic measurement and characterization

A wind tunnel is an essential device for aerodynamic modeling and measurement. High cost and relatively huge size with no open market design hinder the wind tunnel from being widely available in laboratory design for universities and small R&D companies, particularly in a developed country with limited research funding. Thus, most aerodynamic modeling and measurement are done by simulating through computer software which leads to high deviation as the nature of wind is unpredictable. This project aims to provide an open design for a relatively low-cost wind tunnel that universities and R&D companies can quickly adapt. An open design for an open-loop wind tunnel is presented in this article. The proposed wind tunnel design is specifically intended to help the researcher with the aerodynamic measurement with minimum cost for building, customizable design, and reliable measurement. The components, parts, and equipment use the widely available part, which can be obtained across the globe. Validation and characterization are done using software simulation and actual measurement through the device. The proposed design can meet the criteria for aerodynamic measurement and can help the researcher provide a better analysis by combining the actual measurement and software simulation.

Genetic algorithm optimized robust nonlinear observer for a wind turbine system based on permanent magnet synchronous generator

This paper presents an optimal control scheme for a Permanent Magnet Synchronous Generator (PMSG) coupled to a wind turbine operating without a position sensor. This sensorless scheme includes two observers: The first observer uses the flux to estimate the speed. However, an increase in the temperature or a degradation of the permanent magnet characteristics will result in a demagnetization of the machine causing a drop in the flux. The second observer is therefore used to estimate these changes in the flux from the speed and guaranties the stability of the system. This structure leads to a better exchange of information between the two observers, eliminates the problem of encoder and compensates for the demagnetization problem. To improve the precision of the speed estimator, the gain of the non-linear observer is optimized using Genetic Algorithm (GA) and the speed is obtained from a modified Phase Locked Loop (PLL) method using an optimized Sliding Mode Controller (SMC). Furthermore, to enhance the convergence speed of this observer scheme and improve the performance of the system a Fast Super Twisting Sliding Mode Control (FSTSMC) is introduced to reinforce the SMC strategy. A series of simulations are presented to show the effectiveness and robustness of proposed observer scheme.

Optimal designing of a hybrid renewable energy system connected to an unreliable grid based on enhanced African vulture optimizer

The present research addresses a new optimum configuration for a Hybrid Renewable Energy System (HRES). The structure includes fuel cell (FC), wind turbine (WT), and photovoltaic (PV) and its auxiliaries including the electrolyzer and the H₂ tank as the storage system. The primary mover in this study is the grid, while, the proposed HRES is used during energy deficiency from the grid side. The main target is to provide an optimum number of the HRES components to get the minimum loss of power supply probability (LOPSP) and Total Net Present Cost (TNPC). For achieving better optimal results, a Modified version of African vulture optimizer (IAVO) is proposed, validated, and used in the system. Final results are applied to a building in Ahvaz, Iran and they are put in comparison with different works of the studied works. The achievements indicated the primacy of the proposed system over the other algorithms.

Harris hawks optimization algorithm for model order reduction of interconnected wind turbines

This paper illustrates the investigation of higher-scale interconnected wind turbine to realize its analytical design and simulation performance in terms of reduced order model. The present research is aimed to visualize the efficacy of the propounded harris hawks optimization (HHO) approach for different reduced order transfer functions with reference to model order reduction studies. Compared to the higher-scale model, the applied methodology aims to obtain better results in terms of steadiness and computational effort. Moreover, to realize a higher-dimensional power system, the implementation of the HHO may proffer more effectiveness and robustness as opposed to the state-of-the-art optimization techniques. The applied methodology aims to obtain better results in terms of stability and computational effort. Furthermore, to realize a higher dimensional system, implementation of the HHO may offer more effectiveness and robustness as opposed to the state-of-the-art optimization techniques.

Fault-tolerant optimal pitch control of wind turbines using dynamic weighted parallel firefly algorithm

With steadily increasing interest in utilizing wind turbine (WT) systems as primary electrical energy generators, fault-tolerance has been considered decisive to enhance their efficiency and reliability. In this work, an optimal fault-tolerant pitch control (FTPC) strategy is addressed to adjust the pitch angle of WT blades in the presence of sensor, actuator, and system faults. The proposed scheme incorporates a fractional-calculus based extended memory (EM) of pitch angles along with a fractional-order proportional-integral-derivative (FOPID) controller to enhance the performance of the WT. A dynamic weighted parallel firefly algorithm (DWPFA) is also proposed to tune the controller parameters. The efficiency of the proposed algorithm is evaluated on the test functions adopted from 2017 IEEE congress on evolutionary computation (CEC2017). The merits of the proposed fault-tolerant approach are tested on a 4.8-MW WT benchmark model and compared to conventional PI and optimal FOPID approaches. Corresponding comparative simulation results validate the effectiveness and fault-tolerant capability of the proposed control paradigm, where it is observed that the proposed control scheme tends to be more consistent in the power generated at a given wind speed.

Condition monitoring of a wind turbine drivetrain based on generator stator current processing

As the wind turbine operates in harsh conditions, numerous of its components are critical and present an important downtime for maintenance. In this paper, we propose a fault diagnosis algorithm to detect and locate the defects affecting the generator rotor and the pinion of the gearbox lay shaft in a real 25 kW wind turbine drivetrain. The induction generator was used as a fault sensor for gear teeth damage. Through the use of the wavelet packet transform, and the local mean decomposition combined with the Fast Fourier Transform, the detection of gear meshing frequency in the stator current reflects teeth faults. Hence, the principal component analysis of the stator current gives a suitable classification for the gearbox states under different working stages. The obtained results have been significant, despite the use of a short duration and a low sampling frequency of the experimental data.

Design, modeling and control of a hybrid grid-connected photovoltaic-wind system for the region of Adrar, Algeria

The use of fossil energy for electricity production is an evident source of pollution, global warming and climate change. Consequently, researchers have been working to shift toward sustainable and clean energy by exploiting renewable an environmentally friendly resources such as wind and solar energies. On the other hand, energy security can only be achieved by considering multiple resources. Large-scale renewable energy power plants are a key solution for diversifying the total energy mix and ensuring energy security. This paper presents a contribution to diversify the energy mix in Algeria and help mitigate power shortages and improve grid performance. In particular, the paper aims at designing and modeling a large-scale hybrid photovoltaic-wind system that is grid connected. An innovative control approach using improved particle swarm optimized PI controllers is proposed to control the hybrid system and generate the maximum power from the available wind and solar energy resources. Furthermore, economic, environmental and feasibility studies of the project were conducted using HOMER software to assess the viability of the system and its contribution to reduce greenhouse gas emissions.

Efficiency and robustness of type-2 fractional fuzzy PID design using salps swarm algorithm for a wind turbine control under uncertainty

An accurate and efficient control of the Maximum Power Point Tracking (MPPT) and pitch angle for the Wind Turbine (WT) system can provide more energy while also protecting the material. Thus, considering these economic aspects, many controllers are proposed to guarantees their performances. The most studied strategy is the classical PID controller. However, its performance is limited due to the nonlinear model and the difficult wind speed form, which inflict many uncertainties. To solve these drawbacks, an advanced controller named Type-2 Fractional Order Fuzzy PID (T2-FOPID) controller is suggested in this paper to improve the exigence controls. Meanwhile, an efficient optimization technique named the Salps Swarm Algorithm (SSA) has been introduced in order obtaining the best controller parameters. The efficiency of the proposed controller is tested to the 10 kW-WT under various wind speed scenarios. The derived results explicitly indicate that the proposed strategy outperforms the PID controller and other controllers, in terms. the minimum of error, the overshoot and the settling time.

When and how to repower energy systems? A four strategies-based decision model

Repowering systems is a long-lasting managerial endeavor where decision-makers face maintenance and optimization problems. The decision time to repower an energy system is one of the most important matters in this field. Also, in the real-world, each component of the system has different versions available in the market, so choosing the best version of components can be one of the valuable and practical issues in repowering a system. Therefore, decision-makers need optimal repowering policies in order to generate the optimal combination of system's components as well as the optimal time to repower this system with respect to important concerns such as cost, availability and safety issues. This paper provides a first-step decision-making model based on four independent repowering strategies for energy systems. A case study from offshore wind turbine system is presented afterwards to demonstrate the effectiveness of the presented policies. This decision support tool deals with the optimal repowering time and the best combination of components based on cost, availability, and safety constraints.

An adaptive fractional stochastic resonance method based on weighted correctional signal-to-noise ratio and its application in fault feature enhancement of wind turbine

Stochastic resonance (SR) is an effective tool to enhance weak signal by utilizing noise to reach a certain synergistic effect, which has been widely studied in the field of weak signal detection. Currently, using SR to enhance the weak fault feature of wind turbine faces two challenges: First, it is difficult for SR to select the optimal system parameters, while the traditional adaptive method based on SNR needs to predict the precise frequency of the target signal. Second, the wind turbine load changes frequently, making the vibration and noise large. As a result, the traditional SR cannot effectively highlight the target fault feature by inducing a stable resonance phenomenon at the target frequency. To improve the ability of SR to enhance the weak fault feature of wind turbine under strong noise, this paper proposes an adaptive fractional SR method based on weighted correctional signal-to-noise ratio (WCSNR). Firstly, the proposed method considers the adiabatic approximation applicable condition in the SR system and combines characteristics of the expected output signal to construct the WCSNR evaluation index to quantify the system output response, so that the system can adaptively obtain optimal parameters without predicting the accurate frequency of the target signal. Then, the fractional-order theory is applied to the SR system to overcome the shortcoming that the integer-order SR cannot induce stable resonance phenomenon at the target frequency when enhancing the fault feature of wind turbine, and use WCSNR to search for the optimal fractional order to further enhance the weak fault characteristics. Simulation and engineering actual data analysis results verify the effectiveness and superiority of the proposed method in the fault feature enhancement of wind turbine. The analysis results show that compared with the traditional SR method, the method proposed in this paper can more effectively reduce the interference of background noise and accurately enhance the weak fault feature.

Principle Parameters and Environmental Impacts that Affect the Performance of Wind Turbine: An Overview

The share of wind-based electricity generation is gradually increasing in the world energy market. Wind energy can reduce dependency on fossil fuels, as the result being attributed to a decrease in global warming. This paper discusses and reviews the basic principle parameters that affect the performance of wind turbines. An overview presents the introduction and the background of energy consumption, following the order of the elaboration of wind turbines, including mathematical models, categories of wind turbines were critically discussed. Moreover, it also focuses on materials that are commonly considered for wind turbine manufacturing, and the process used to recycle them. The scale of recycling methods for fiberglass and thermoplastic is presented in the respective section. Various parameters that reduce the function of wind turbines are explained in depth. This review also discusses various environmental impacts of wind turbines. Future research studies are suggested in the conclusion section.

A distributed parallel firefly algorithm with communication strategies and its application for the control of variable pitch wind turbine

Firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by nature. Due to its superior performance, it has been widely used in real life. However, it also has some shortcomings in some optimization cases, such as low solution accuracy and slow solution speed. Therefore, in this paper, distributed parallel firefly algorithm (DPFA) with four communication strategies is presented to improve these shortcomings. The distributed parallel technique is implanted to divide the initial fireflies into several subgroups, and exchange the information based on communication strategies among subgroups after the fixed iteration. The communication strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. For verifying its performance, this paper compared DPFA with famous optimization algorithms, and experimental results show that DPFA has stronger competitiveness under the test suite of CEC2013. Furthermore, the proposed DPFA is also applied to the PID parameter tuning of variable pitch wind turbine, and conducted experiments show that DPFA outperforms other algorithms. It can smooth the power output and reduce the impact on the power grid when the wind speed fluctuates.

Wind speed pattern data and wind energy potential in Pakistan: current status, challenging platforms and innovative prospects

Energy is an essential parameter for the economic growth and sustainable development of any country. Due to the rapid increase in energy demand, depletion of fossil fuels and environmental concerns, many developing and developed countries are moving towards alternative renewable resources such as solar energy, wind energy and biomass. Wind energy as a renewable energy source is gaining a lot of significant attention. Wind energy is a sustainable solution to produce energy having potential benefits such as clean source, reduced toxic gases emission and environmental friendly protocol for operation. Pakistan is among the top countries facing the worst energy crisis due to different political and financial issues. Pakistan is blessed with a huge potential of wind energy having all the basic requirements such as windy regions and good wind speed for harnessing energy. Pakistan can utilize the potential of wind energy to reduce the problem of energy outrage in the country and also take steps towards green economy from conventional fuel economy. This critical review highlights the current status, potential and the steps taken in the past and present to overcome the energy shortage in Pakistan by employing wind energy. Outlook on wind speed data, deployment of wind energy, environmental effect of wind energy and its barriers in the adoption are discussed with recommendations and suggestions to utilize this clean energy in an effective way. Graphical abstract.

Prediction of power generation and rotor angular speed of a small wind turbine equipped to a controllable duct using artificial neural network and multiple linear regression

Wind power is one of the most popular sources of renewable energies with an ideal extractable value that is limited to 0.593 known as the Betz-Joukowsky limit. As the generated power of wind machines is proportional to cubic wind speed, therefore it is logical that a small increment in wind speed will result in significant growth in generated power. Shrouding a wind turbine is an ordinary way to exceed the Betz limit, which accelerates the wind flow through the rotor plane. Several layouts of shrouds are developed by researchers. Recently an innovative controllable duct is developed by the authors of this work that can vary the shrouding angle, so its performance is different in each opening angle. As a wind tunnel investigation is heavily time-consuming and has a high cost, therefore just four different opening angles have been assessed. In this work, the performance of the turbine was predicted using multiple linear regression and an artificial neural network in a wide range of duct opening angles. For the turbine power generation and its rotor angular speed in different wind velocities and duct opening angles, regression and an ANN are suggested. The developed neural network model is found to possess better performance than the regression model for both turbine power curve and rotor speed estimation. This work revealed that in higher ranges of wind velocity, the turbine performance intensively will be a function of shrouding angle. This model can be used as a lookup table in controlling the turbines equipped with the proposed mechanism.

Power system stability enhancement by damping and control of Sub-synchronous torsional oscillations using Whale optimization algorithm based Type-2 wind turbines

This paper is aimed to demonstrate the merits of a metaheuristic swarm-based optimization technique, WOA (Whale optimization algorithm), in alleviating the low-frequency torsional oscillations called SSR (Sub-synchronous resonance). The demonstration has been performed using the modified IEEE FBM (IEEE first benchmark model) aggregated with Type-2 WPP (Wind power plant). The Plant is further interlinked to the grid with series compensated lines. Use of WOA for the optimal tuning of the controller suggested in the literature to control one of the degrees of freedom, i.e., Pitch angle and external resistance connected to the rotor, has been demonstrated. The effectiveness of the proposed WOA based controller has been examined using a time-domain approach based on the dynamic response of the different segments of the test system using the Matlab software for the three different cases viz., with Type-2 WPP only, Type-2 WPP with the controller suggested in the literature and with the proposed WOA based controller. The eigenvalues, together with simulation results, reveal the potential of the proposed WOA based controller in damping the low-frequency torsional oscillations using Type-2 wind turbines.

Improving sustainability of electrolytic wastewater treatment processes by green powering

This work focuses on the evaluation of the impact of powering electrolytic wastewater treatment processes with grid or renewable energy on the sustainability of this electrochemical remediation technology. To face this goal, it was performed an inventory of three bench-scale plants made up by the same treatment technology but powered from different supplies: connected to grid and directly coupled with solar photovoltaic panels or a wind turbine. Results show that the powering mode can significantly affect the environmental risks of the treatment, not only in terms of electricity demand but also on the formation of intermediates, which are more important in the cases in which the intensity profile varied. A life cycle assessment (LCA) is carried out in order to quantify the environmental impacts of green powering electrolytic wastewater treatment processes. Ecoinvent 3.3 data base, AWARE, USEtox, IPPC and ReCiPe methodologies are used to quantify the environmental burden into 5 midpoint (water footprint, global warming 100a, ozone layer depletion, human toxicity, freshwater ecotoxicity) and 17 endpoint impact categories. All impact categories are higher in the case in which the supplied power cames from a electricity grid mix. For the removal of 0.1 g 2,4-dichlorophenoxyacetic acid (2,4D) per liter (functional unit) of treated wastewater releases 0.14 kg CO₂ eq. If the energy is provided by a wind turbine or a solar panel the processes emit 0.020 kg CO₂ eq and 0.019 kg CO₂ eq, respectively. A comparison of the impact based on the grid mix used in different countries is also made, which has pointed out the relevance of this input on the sustainability of the environmental electrochemical technologies.

Symptoms intuitively associated with wind turbine infrasound

In many countries, a certain proportion of individuals living in the vicinity of wind power areas have reported symptoms that they have intuitively associated with infrasound from wind turbines. While the reason for these symptoms remains under debate, this is the first study to describe the phenomenon by assessing the prevalence and severity of these wind turbine infrasound related symptoms as well as factors associated with being symptomatic. Four wind power areas in Finland assessed to have the most problems intuitively associated with wind turbine infrasound were selected for the study. The questionnaire was mailed to 4847 adults in four distance zones (≤ 2.5 km, > 2.5-5 km, > 5-10 km, > 10-20 km from the closest wind turbine), and 28% responded. In the closest distance zone, 15% of respondents reported having symptoms that they have intuitively associated with wind turbine infrasound. In the whole study area, the symptom prevalence was 5%. Many of the symptomatic respondents were annoyed by audible wind turbine sound and associated their symptoms also with vibration or electromagnetic field from wind turbines. One third of the symptomatic respondents rated their symptoms severe, and the symptom spectrum was very broad covering several organ systems. In multivariate models, many factors such as proximity to wind turbines, impaired health status, being annoyed by different aspects of wind turbines and considering wind turbines as a health risk were associated with having wind turbine infrasound related symptoms. Although causal relationships cannot be assessed based on a cross-sectional questionnaire study, it can be speculated that interpretations of symptoms are affected by many other factors in addition to actual exposure.

Using trace elements to identify the geographic origin of migratory bats

The expansion of the wind energy industry has had benefits in terms of increased renewable energy production but has also led to increased mortality of migratory bats due to interactions with wind turbines. A key question that could guide bat-related management activities is identifying the geographic origin of bats killed at wind-energy facilities. Generating this information requires developing new methods for identifying the geographic sources of individual bats. Here we explore the viability of assigning geographic origin using trace element analyses of fur to infer the summer molting location of eastern red bats (Lasiurus borealis). Our approach is based on the idea that the concentration of trace elements in bat fur is related through the food chain to the amount of trace elements present in the soil, which varies across large geographic scales. Specifically, we used inductively coupled plasma–mass spectrometry to determine the concentration of fourteen trace elements in fur of 126 known-origin eastern red bats to generate a basemap for assignment throughout the range of this species in eastern North America. We then compared this map to publicly available soil trace element concentrations for the U.S. and Canada, used a probabilistic framework to generate likelihood-of-origin maps for each bat, and assessed how well trace element profiles predicted the origins of these individuals. Overall, our results suggest that trace elements allow successful assignment of individual bats 80% of the time while reducing probable locations in half. Our study supports the use of trace elements to identify the geographic origin of eastern red and perhaps other migratory bats, particularly when combined with data from other biomarkers such as genetic and stable isotope data.

An ergonomics assessment of three simulated 120 m ladder ascents: A comparison of novice and experienced climbers

This study investigated the ergonomics of three simulated 120 m vertical ladder ascents and differences between novice (NC) and experienced climbers (EC). Seven EC and 10 NC undertook three 120 m climbs; comprising of four 30 m climbs. Ascending 120 m was reported as a high physical demand, supported by high peak HRs (~173 b.min^-1 across the three climbs) and V˙ O₂ (~3.1 L.min^-1 across the three climbs). Grip strength and endurance were significantly (p < 0.05) impaired by ascents. With multiple ascents, toe clearance was reduced (Climb 1 - 0.0515 m; Climb 3 - 0.046 m), and participants reached higher with their arms (shoulder angle: Climb 1 - 117°; Climb 3 - 136°). NC demonstrated less range of movement through the hips (NC - 46°; EC - 58°), and higher muscle activation in the upper body (NC - 60%; EC - 49%). Experience reduced cumulative climbing times (exercise + rest), whilst maintaining the same physiological demand as NC and maintained optimised movement patterns for longer.

A circular economy approach to green energy: Wind turbine, waste, and material recovery

Wind energy has been considered as one of the greenest renewable energy sources over the last two decades. However, attention is turning to reducing the possible environmental impacts from this sector. We argue that wind energy would not be effectively "green" if anthropogenic materials are not given attention in a responsible manner. Using the concept of the circular economy, this paper considers how anthropogenic materials in the form of carbon fibers can reenter the circular economy system at the highest possible quality. This paper first investigates the viability of a carbon-fiber-reinforced polymer extraction process using thermal pyrolysis to recalibrate the maximum carbon fiber value by examining the effect of (a) heating rate, (b) temperature, and (c) inert gas flow rate on char yield. With cleaner and higher quality recovered carbon fibers, this paper discusses the economic preconditions for the takeoff and growth of the industry and recommends the reuse of extracted carbon fibers to close the circular economy loop.

Adaptive hybrid intelligent MPPT controller to approximate effectual wind speed and optimal rotor speed of variable speed wind turbine

Operating wind power generation system at optimal power point is essential which is achieved by employing a Maximum Power Point Tracking (MPPT) control strategy. This literature focuses on developing a novel particle swarm optimization algorithm enhanced radial basis function neural network supported TSR based MPPT control strategy for Doubly Fed Induction Generator (DFIG) based wind power generation system. The proposed hybrid MPPT control strategy estimates the effective wind speed and estimates the optimal rotor speed of the wind power generation system to track the maximum power. The proposed controller extremely reduces the speed dissimilarity range of wind power generation system, which leads to rationalizing the pulse width inflection of DFIG rotor side converter. This in turn, increases the system's reliability and delivers an effective power tracking with reduced converter losses. Furthermore, by utilizing the proposed MPPT controller, the converter size can be reduced to 40%. Therefore, the overall cost of the system can be gradually decreased. To validate the performance of the proposed MPPT controller, an extensive simulation study has been carried out under medium and high wind speed conditions in MATLAB/Simulink. The obtained results have been justified using experimental analysis.

X-ray tomography data of White Etching Cracks (WEC)

This data article contains lab-based micro-computed tomography (μCT) data of cracks and crack networks in 4 different bearings, mainly from wind turbines, which formed the basis for the crack analysis reported in Danielsen et al. (Danielsen et al., 2019).

Real time modeling and control of a wind farm connected to a multi-bus network under faulty conditions

This paper describes the detailed modeling and simulation of an aerogenerators system based on DFIG using RT-LAB simulator. A stator flux oriented vector is applied to decouple the control of active and reactive powers generated by the DFIG, and other controllers are needed to keep the DC-link voltage and grid currents at desired values. In order to adjust appropriately the pitch angle and keep the wind power at rated value, a pitch control and MPPT are involved. The model of the studied was developed first in the MATLAB^®, then, it was rebuilt on RT-LAB to test its performance. Simulation results validate the capability of the platform to achieve rapid simulation of complex power systems. At the end, the details of the simulation results are presented, discussed, and concluded.

Evaluation of wind energy potential and trends in Morocco

In recent years, the use of wind energy has become increasingly attractive for the successful and economic development. Wind energy is one of the fastest-growing renewable energy technologies of electricity generation. Wind energy has proved its potential in combating environmental degradation while ensuring a renewable, efficient and clean energy source. Good wind sites can even be competitive with traditional energy sources. In this paper, we used statistical methods namely Weibull probability density function for evaluating the wind energy potential as a power source in Morocco's regions, in particular Taza and Dakhla cities. Various methods were explored as wind variability, power density, standard deviation, Moroccan and WAsP methods for calculating the Weibull parameters using mean wind speed data measured at one-hour intervals. The wind data have been extracted at the height of 50 m and over a three-year period 2015–2017. Furthermore, the variations of monthly and annual wind speed are studied and the power and energy densities are evaluated. The monthly values of the Weibull shape parameter are on average 5.01 m/s at Taza and 9.04 m/s at Dakhla. The results obtained showed that the highest values of wind potential occur during March, July, September and December in Dakhla and during the December to March in Taza.

A new modified sliding window empirical mode decomposition technique for signal carrier and harmonic separation in non-stationary signals: Application to wind turbines

Modern control applications justify the need for improved techniques capable of coping with the non-stationary nature of measured signals while being able to monitor systems in real-time. Empirical Mode Decomposition (EMD) is known for its efficiency in time domain analysis of multi-component signals through Intrinsic Mode Functions (IMFs) extraction. Recent years witnessed the introduction of Sliding Window EMD (SWEMD) capable of analyzing signals in real time applications. However, complex signals require several sifting iterations while a rather increased number of IMFs might result in impracticality for on-line applications. This paper introduces a new modified faster SWEMD capable of extracting harmonics from non-stationary signals in real-time operation. The method uses the traditional EMD properties in the first pass for a small number of sifting processes. In addition, a new section is added to the algorithm based on inflection point tracking of the residue derivative from the first pass is added, in order to track low frequency waves and render the analysis faster. The method is validated for non-stationary signals with and without added colored noise and applied on measured turbine side angular velocity for harmonic extraction in wind turbines as an application. The proposed method may well be used for fault detection and disturbance rejection in mechanical systems.

A new rooted tree optimization algorithm for indirect power control of wind turbine based on a doubly-fed induction generator

A new control strategy based on the root tree optimization (RTO) is presented in order to reduce the chattering phenomena in active and reactive powers, and to minimize the harmonic currents which appear mostly at the level of the rotor side converter (RSC), in a doubly-fed induction generator (DFIG). The root tree optimization is used to adjust the parameters (K_p,K_i) of PI controller (RTO-PI). Simulation results are presented to demonstrate the effectiveness of the new proposed technique. Besides, the system associated with this metaheuristic algorithm can effectively give better dynamic and steady performance. The results with the RTO-PI controller show more performances than the results compared with the classical PI.

Health effects of wind turbines on humans in residential settings: Results of a scoping review

INTRODUCTION

As the global number of wind turbines has increased steadily in recent years, as has the number of studies about putative health effects in residential settings, it is the review purpose to give an overview of the characteristics and methodologies of the scientific literature around the topic in order to identify research gaps and to derive implications for research and practice. Additionally, study findings from higher-quality observational studies as well as results that seem to be of interest for the scientific and political debate are presented.

METHODS

The scoping review was conducted following systematic review methods. Comprehensive literature searches were carried out in several databases, and with extensive hand searches. All review steps were carried out in parallel by two reviewers or by one reviewer and in duplicate checked by another reviewer. The following important methodological criteria were investigated: Reporting, ethical aspects, generalization, selection bias, information bias, confounder bias. Findings from observational studies without a selection bias, information bias, and confounder bias are presented.

RESULTS

84 articles, that varied significantly in methods and outcomes assessed, met the inclusion criteria. Multiple cross-sectional studies reported that wind turbine noise is associated with noise annoyance, which is moderated by several variables such as noise sensitivity, attitude towards wind turbines, or economic benefit. Wind turbine noise is not associated with stress effects and biophysiological variables of sleep. Results on the impact of wind turbine noise on sleep disburbance, quality of life, and mental health problems differed among cross-sectional studies. There were few studies that addressed the potential impact of turbine noise on clinically apparent health outcomes. There were also few studies on visual risk factors or infrasound exposure. No literature was identified regarding low-frequency noise, electromagnetic radiation, and ice throw.

CONCLUSIONS

There is an extensive and diverse body of evidence around health impacts of wind turbines in residential settings, that increased sharply since 2010, showing particularly noise consequences concerning increased noise annoyance with its complex pathways; no relationship between wind turbine noise and stress effects and biophysiological variables of sleep; and heterogeneous findings concerning sleep disturbance, quality of life, as well as mental health problems. Research gaps concern the complex pathways of annoyance, the examination of clinically apparent health outcomes in comparison with non-exposed residents, an objective investigation of visual wind turbine features, the interaction between all wind turbine exposures, and epidemiological observational studies on field low-frequency and infrasound from wind turbines. Future research needs thorough high-quality and prospective study designs.

The influence of wind turbine visibility on the health of local residents: a systematic review

PURPOSE

The health effects of visible wind turbine features on residents were investigated. Further, it was examined, if visual annoyance has an influence on residents' health, and if wind turbine visibility impacts residents' health independently of or in combination with acoustical aspects.

METHODS

Medical databases, Google Scholar, public health institutions, and reference lists were searched systematically (PROSPERO registry number: CRD42016041737). Two independent reviewers screened titles/abstract and full texts, extracted data, and critically appraised the methodology of included studies. Study findings were analyzed qualitatively and quantitatively.

RESULTS

Seventeen studies from 19 publications of varying methodological quality were included (two cohort studies, fifteen cross-sectional studies). The pooled prevalence of high annoyance due to altered views and shadow flicker was 6% each. The results of other health effects were inconsistent, with some indications showing that direct wind turbine visibility increases sleep disturbance. Annoyance by direct visibility, shadow flicker, and blinking lights was significantly associated with an increased risk for sleep disorders. One study indicated reactions to visual wind turbine features may be influenced by acoustical exposures.

CONCLUSIONS

In interpreting the results, the differing methodological quality of the included studies needs to be considered. Direct and indirect wind turbine visibility may affect residents' health, and reactions may differ in combination with noise. Further, annoyance by wind turbine visibility may interact as mediator between visual exposures and the health of local residents. To confirm the results, more high-quality research is needed.

Identification of Griffon Vulture's Flight Types Using High-Resolution Tracking Data

Being one of the most frequently killed raptors by collision with wind turbines, little is known about the Griffon vulture's flight strategies and behaviour in a fine scale. In this study, we used high-resolution tracking data to differentiate between the most frequently observed flight types of the Griffon, and evaluated the performance of our proposed approach by an independent observation during a period of 4 weeks of fieldwork. Five passive flight types including three types of soaring and two types of gliding were discriminated using the patterns of measured GPS locations. Of all flight patterns, gliding was classified precisely (precision = 88%), followed by linear and thermal soaring with precision of 83 and 75%, respectively. The overall accuracy of our classification was 70%. Our study contributes a baseline technique using high-resolution tracking data for the classification of flight types, and is one step forward towards the collision management of this species.