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The rapid development of the wind industry is accompanied by increasing environmental impacts. Currently, there is a lack of research on the impacts of offshore wind farm (OWF) on tropical cyclone (TC) intensity, including the mechanisms involved. This research is carried out by using a coupled and an uncoupled numerical model to investigate the impact of OWF on an autumn TC in the northeastern South China Sea. The results show that the wind speed deficit caused by OWF leads to an increase in surface pressure on the inflow side. This causes the surface pressure in the TC periphery to increase by advection, even if the TC is some distance away from the OWF. The increase in pressure gradient from the periphery to the TC center enhances the TC secondary circulation, thereby intensifying the TC. When the TC enters the OWF, the above mechanisms weaken and the ocean dominates the TC intensification. This is because the reduction in wind speed caused by the OWF results in a weaker sea surface current velocity, which weakens the flow of upstream cold water into the OWF, warming the sea surface temperature (SST) within the OWF. This implies that the horizontal gradient of the local SST is an important factor to be considered in the development of OWF. Sensitivity experiments indicate that OWF can also intensify other types of TC, and that higher cut‐out wind speeds lead to stronger intensification effects. These results also provide a new perspective on TC intensity forecasts. Plain Language Summary: The rapid development of the offshore wind industry has significant impacts on the surrounding environment. There is limited research on the impact of offshore wind farms on the tropical cyclone (TC) intensity. This study indicates that wind farms in the northern South China Sea intensify an autumn TC that makes landfall in China. Wind farms intensify the TC by increasing the surface pressure in the TC periphery when the TC is far from the wind farm. When the TC is close to the wind farm, it is the warming sea water within the wind farm that intensifies the TC. Tropical cyclone can also be slightly intensified when the number of turbines is small. These results help to understand the impact of offshore wind farms on TCs and to improve TC intensity forecasts. Key Points: The tropical cyclone (TC) intensifies before approaching offshore wind farms due to increased peripheral pressure caused by wind farmsThis intensification within wind farms is mainly due to the reduced transport of cold water upstream associated with the wind speed deficitHigher cut‐out wind speeds can lead to a more pronounced intensification of the TC before it enters wind farms [ABSTRACT FROM AUTHOR]

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting. [ABSTRACT FROM AUTHOR]

A comparison and evaluation of the AEP(Annual Energy Production) of a wind farm were conducted in this study with a feasibility study and using the actual operation data from the S wind farm on Jeju Island from January 2020 to December 2022. The free wind speed data were selected from the data measured from a nacelle anemometer, the correlation equation between wind speed and AEP was obtained, and the annual average wind speed for the past 20 years was predicted using the MCP method. As a result, comparing the AEP from the operation data with that estimated in the feasibility study, we found that the AEP was reduced by approximately 2.40% in 2020 and 12.14% in 2021, and increased by 6.76% in 2022. The wind speeds over the 20-year lifetimes of the wind turbines were obtained, and the AEP that could be generated at the S wind farm indicated that it could be used for operation. In the future, the S wind farm will operate at between 25% and 30% availability for the remaining 17 years of operation. If the availability falls below 25%, there will be a need to check the reasons for the deterioration of wind turbine performance and the frequency of failures. [ABSTRACT FROM AUTHOR]

Wind farms with type IV wind turbines from various manufacturers are being massively put into operation. These wind turbines comply with the requirements of the grid codes of the countries where they are designed and/or manufactured, but do not factor in the specific features of the distribution networks of other countries to which they are connected. The study at issue involves a comparative analysis of the requirements of grid codes of different countries for the stable operation of wind turbines under standard disturbances. The low voltage ride through (LVRT) characteristic makes it possible to prevent wind turbine shutdowns in case of short-term voltage dips of a given depth and duration. The calculations of transient processes indicate that wind turbines may not meet the requirements of the grid code of a particular country for their stable operation. As a result, standard disturbances will block the reactive current injection and the wind turbine will be switched off. This is often caused by the relay protection devices with a time delay of 1–2 s, which are used in distribution networks and implement the functions of long-range redundancy. Excessive shutdowns of wind turbines lead to emergency rises in the loads for the generating units of conventional power plants, aggravating the post-accident conditions and disconnecting consumers of electricity. This article presents a method for checking the LVRT characteristic settings for compliance with the technical requirements for wind turbines. To prevent wind turbine outages, one should either change the configuration of the LVRT characteristic, upgrade the relay protection devices in the distribution network adjacent to the wind farm, or implement group or individual technical solutions at the wind farm. The performance of the proposed technical solutions is confirmed by the calculations of transient processes. [ABSTRACT FROM AUTHOR]

Flow modifications induced by wind turbine rotors on the incoming atmospheric boundary layer (ABL), such as blockage and speedups, can be important factors affecting the power performance and annual energy production (AEP) of a wind farm. Further, these rotor‐induced effects on the incoming ABL can vary significantly with the characteristics of the incoming wind, such as wind shear, veer, and turbulence intensity, and turbine operative conditions. To better characterize the complex flow physics underpinning the interaction between turbine rotors and the ABL, a field campaign was performed by deploying profiling wind LiDARs both before and after the construction of an onshore wind turbine array. Considering that the magnitude of these rotor‐induced flow modifications represents a small percentage of the incoming wind speed (≈3%), high accuracy needs to be achieved for the analysis of the experimental data and generation of flow predictions. Further, flow distortions induced by the site topography and effects of the local climatology need to be quantified and differentiated from those induced by wind turbine rotors. To this aim, a suite of statistical and machine learning models, such as k‐means cluster analysis coupled with random forest predictions, are used to quantify and predict flow modifications for different wind and atmospheric conditions. The experimental results show that wind velocity reductions of up to 3% can be observed at an upstream distance of 1.5 rotor diameter from the leading wind turbine rotor, with more significant effects occurring for larger positive wind shear. For more complex wind conditions, such as negative shear and low‐level jet, the rotor induction becomes highly complex entailing either velocity reductions (down to 9%) below hub height and velocity increases (up to 3%) above hub height. The effects of the rotor induction on the incoming wind velocity field seem to be already roughly negligible at an upstream distance of three rotor diameters. The results from this field experiment will inform models to simulate wind‐turbine and wind‐farm operations with improved accuracy for flow predictions in the proximity of the rotor area, which will be instrumental for more accurate quantification of wind farm blockage and relative effects on AEP. [ABSTRACT FROM AUTHOR]

The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models. [ABSTRACT FROM AUTHOR]

Wind farms are a clean and efficient source of renewable energy. However, they cause negative impacts on raptors. Here, we present a review of the existing scientific literature on the effects of wind farms on raptors' ecology with a particular interest in the potential solutions. After collecting 216 studies, we found a consensus in the literature that raptors exhibit avoidance behaviors, and that the abundance of raptors decreases after wind farm installation, although it might recover over time. The position of wind farms on mountaintop ridges poses a particular danger to large soaring raptors, as they rely on orographic uplift to gain altitude. Adult mortality significantly affects population dynamics, particularly in endangered species, but young inexperienced individuals show a higher collision risk. The combination of different methods including field monitoring, GPS telemetry and systematic search for carcasses is an adequate approach to further investigate the problem and solutions. Shutdowns on demand, the installation of deterrents, turbine micro‐sitting and the repowering of wind farms have been suggested as potential solutions, although results are contradictory and case‐specific. Furthermore, it is essential to report the potential occurrence of conflicts of interest in scientific papers, as they can influence the interpretation of the results. Finally, from a future perspective, it is crucial to assess the effectiveness of solutions to mitigate the negative effects of wind farms to promote raptor conservation. This becomes increasingly relevant in the context of renewable energy development and increasing energy demand worldwide. [ABSTRACT FROM AUTHOR]

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

A common retrofitting method for wind farms is the replacement of low-power turbines with high-power ones. The determination of the optimal replacement sequence for the purpose of maximizing revenue is a significant challenge. This paper employs a combination of FLORIS and a sequencing algorithm to simulate the power output resulting from the replacement of 1.5 MW small turbines with 5 MW large turbines. This study demonstrates that the optimal strategy for maximizing the overall power output is to replace the turbines in the first column. When the turbines situated in the first column have already undergone replacement or are unable to be replaced due to the characteristics of the terrain, it would be prudent to prioritize those in the final column. In the case of staggered arrangements, priority should be given to diagonal points that do not have turbines situated behind them. In the case of replacing the same number of large wind turbines, the preferred replacement option has a minimal impact on the power output of the existing small wind turbines, with an estimated reduction of 0.67%. This effectively enhances the economic efficiency of wind farm renovation. [ABSTRACT FROM AUTHOR]

Due to the widespread penetration of wind energy, large-capacity wind turbines (WTs) are becoming more conspicuous. In this paper, a linear control-oriented large-capacity wind turbine (WT) model is redesigned based on the 15 MW WT in openFAST. Compared with the original model, the redesigned model is more efficient and conducive to the design of linear controllers. To verify the validity of the redesigned WT model, we design a distributed control scheme with deloading operation of the wind farm (WF) to fairly share the load. Simulations are performed to verify the effectiveness of the application in the WF. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For optimizing the operational efficiency of a wind farm in complex terrain, the wake study of wind turbines and the detailed analysis of the wind data are essential. In this research, The Manjil wind farm located in the Alborz Mountains with special topography and distinct wind regimes has been studied. OpenFOAM has been applied for computational fluid dynamics simulations of two wind speeds at different reference heights as case studies. Then, a comparison between the simulation results and the available supervisory control and data acquisition dataset was made. The result assumed that the velocity of the wake is recovered up to 85% after ten times the diameter of the turbine. In this distance, there is a 14.5% and 15.5% speed reduction on average for two simulations, with velocity on the hub height of 9 and 17 m/s respectively. The analysis of raw wind data shows that a high potential of wind energy is available in summer and there is almost no wind in wintertime, which causes some challenges for developing the wind farms under these conditions. [ABSTRACT FROM AUTHOR]

Recent advancements in wind farm (WF) construction have prompted designers to focus on optimizing WF performance and minimizing the negative effect of wake interference on power output. One proven technique to reduce wake influence and improve power generation is wake steering, which involves adjusting the yaw angles of wind turbines (WTs). The objective of this study is to evaluate the optimization of WF using machine learning (ML), specifically an artificial neural network (ANN) approach. In the first phase of the study, an ANN power model is selected to standardize wake loss and optimize power. In the next phase, a genetic algorithm (GA) will be employed to determine the best yaw angles for the wind direction based on data collected from the ANN power model. The ANN power model might not account for maintenance tasks around the WF. The results of the optimization demonstrate that energy is maximized between wind directions of 166° to 178°. This research achieved a.97 optimized overall power ratio, compared to non-optimized scenarios, using an optimal yaw angle technique in all directions. The findings of the study demonstrate that ANN-based optimization, combined with standardized wake degradation and suitable yaw angle direction, is an efficient method for WF optimization. [ABSTRACT FROM AUTHOR]

Abstract Flow modifications induced by wind turbine rotors on the incoming atmospheric boundary layer (ABL), such as blockage and speedups, can be important factors affecting the power performance and annual energy production (AEP) of a wind farm. Further, these rotor‐induced effects on the incoming ABL can vary significantly with the characteristics of the incoming wind, such as wind shear, veer, and turbulence intensity, and turbine operative conditions. To better characterize the complex flow physics underpinning the interaction between turbine rotors and the ABL, a field campaign was performed by deploying profiling wind LiDARs both before and after the construction of an onshore wind turbine array. Considering that the magnitude of these rotor‐induced flow modifications represents a small percentage of the incoming wind speed ( ≈3%), high accuracy needs to be achieved for the analysis of the experimental data and generation of flow predictions. Further, flow distortions induced by the site topography and effects of the local climatology need to be quantified and differentiated from those induced by wind turbine rotors. To this aim, a suite of statistical and machine learning models, such as k‐means cluster analysis coupled with random forest predictions, are used to quantify and predict flow modifications for different wind and atmospheric conditions. The experimental results show that wind velocity reductions of up to 3% can be observed at an upstream distance of 1.5 rotor diameter from the leading wind turbine rotor, with more significant effects occurring for larger positive wind shear. For more complex wind conditions, such as negative shear and low‐level jet, the rotor induction becomes highly complex entailing either velocity reductions (down to 9%) below hub height and velocity increases (up to 3%) above hub height. The effects of the rotor induction on the incoming wind velocity field seem to be already roughly negligible at an upstream distance of three rotor diameters. The results from this field experiment will inform models to simulate wind‐turbine and wind‐farm operations with improved accuracy for flow predictions in the proximity of the rotor area, which will be instrumental for more accurate quantification of wind farm blockage and relative effects on AEP.

Abstract The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models.

With the large-scale integration of wind power into the grid, the inertia and disturbance resistance of the power system are gradually declining, leading to an increasingly prominent frequency stability issue. An adaptive frequency support control strategy for wind farms based on rotor kinetic energy is proposed. Firstly, a participation factor is proposed, which determines the degree of wind farm participation in frequency support based on the average rotor speed of the wind farm and adaptively adjusts the inertia response parameters and frequency droop control parameters of wind farms. Based on the real-time available rotor kinetic energy of wind turbines, the frequency modulation power of wind farms is dynamically allocated. The frequency support ability of wind turbines with more rotor kinetic energy is fully utilized and excessive participation of low-speed wind turbines is avoided in frequency modulation. After the frequency support is completed, a staged rotor speed recovery method is used to restore the rotor speed of the wind turbines. Simulation results show that the proposed strategy can adaptively adjust the active output power based on the operating status of the wind farms and the available rotor kinetic energy of the wind turbines while achieving effective support for the system frequency and avoiding secondary frequency drop.

Wind power has been developing rapidly as a key measure to mitigate human-driven global warming. The understanding of the development and impacts of wind farms on local climate and vegetation is of great importance for their rational use but is still limited. In this study, we combined remote sensing and on-site investigations to identify wind farm locations in Inner Mongolia and performed landscape pattern analyses using Fragstats. We explored the impacts of wind farms on land surface temperature (LST) and vegetation net primary productivity (NPP) between 1990 and 2020 by contrasting these metrics in wind farms with those in non-wind farm areas. The results showed that the area of wind farms increased rapidly from 1.2 km2 in 1990 to 10,755 km2 in 2020. Spatially, wind farms are mainly clustered in three aggregation areas in the center. Further, wind farms increased nighttime LST, with a mean value of 0.23 °C, but had minor impacts on the daytime LST. Moreover, wind farms caused a decline in NPP, especially over forest areas, with an average reduction of 12.37 GC/m². Given the impact of wind farms on LST and NPP, we suggest that the development of wind farms should fully consider their direct and potential impacts. This study provides scientific guidance on the spatial pattern of future wind farms.

Reducing the cost of operating and maintaining wind farms is essential for the economic viability of this renewable energy source. This study applies hyper-heuristics to design a maintenance policy that prescribes the best maintenance action in every possible situation. Genetic programming is used to construct a priority function that determines what maintenance activities to conduct and the sequence of maintenance activities if there are not enough resources to do all of them simultaneously. The priority function may take into account the health condition of the target turbine and its components, the characteristics of the corresponding maintenance work, the workload of the maintenance crew, the working condition of the whole wind farm and the possibilities provided by opportunistic maintenance. Empirical results using a simulation model of the wind farm demonstrate that the proposed model can construct maintenance policies that perform well both in training and test scenarios, which shows the practicability of the approach. [ABSTRACT FROM AUTHOR]

The study addresses the challenge of bird collisions with wind turbines by developing an autonomous risk assessment method. The research uses data from the stereoscopic Bird Protection System (BPS) to anticipate potential collision threats by analysing flight parameters and distance from turbines. The danger factor depends on the flight characteristics of the identified bird species and the parameters of the wind turbine control system. The paper proposes an online quantitative risk assessment model that operates in real time, with the aim of minimising unnecessary turbine shutdowns while improving bird conservation. The model is validated through field data from bird flights. The findings suggest that adaptive management of turbine operations based on real-time bird flight data can significantly reduce collision risks without compromising energy production efficiency. The research underscores the balance between ecological considerations and the economic viability of wind energy, proposing an adaptive strategy that reduces unnecessary turbine stoppages while ensuring the safety of avian species.

Wind farm wakes have a significant impact on momentum and turbulence fluxes within the atmospheric boundary layer， thereby influencing the local climate and environment.Mesoscale numerical models incorporating wind farm parameterizations are powerful tools for studying the climate and environmental impacts of wind farms.In this study， the wind speed and turbulence kinetic energy profiles of the Fitch wind farm parameterization scheme in the WRF mesoscale model are evaluated in the turbine and wake regions using high-resolution Large Eddy Simulations （LES） as “true values” and a method based on the relation derived from classical momentum theory is proposed to correct the grid-inflow wind speed.The method takes into account the blocking effect caused by the grid equivalent thrust and the corrected wind speed is closer to the free-stream wind speed.Results show that the difference between the grid-inflow wind speed from the original Fitch parameterization scheme and the LES is significant and sensitive to the model horizontal resolution.The Fitch-new parameterization with corrected grid-inflow wind speed reduces the relative error in absolute value between grid-inflow and free-stream wind speed to less than 1% across different horizontal resolutions （1000 m， 500 m， and 250 m）.The spatially averaged thrust and output power are consistent with LES results.The Fitch-new parameterization improves the simulated wind speed deficit in the wake zone of wind turbine， especially in the grid with turbine under high resolution.Although the simulated increase in turbulent kinetic energy and its vertical distribution in the wake zone is improved compared to the original Fitch scheme， there are certain issues that require further investigation.

ObjectivesConsidering the difference of primary frequency regulation ability of wind turbines under different wind conditions in wind farms, the evaluation method of frequency regulation capability of wind turbines was optimized on the basis of sag control and inertia control strategy.MethodsAn improved multi-unit frequency modulation reference power cooperative control strategy was proposed.ResultsThe introduction of multi-unit cooperative control method can effectively improve the distribution of frequency modulation reference power among units, thus effectively adjust the degree of each unit participating in the primary frequency regulation of the system. An improved cooperative control strategy was introduced on the basis of inertia control and sag control. According to the operation state of the unit under the actual wind condition, the reference power of the unit which can effectively participate in the primary frequency modulation was evaluated. The introduction of frequency modulation capability coefficient can realize the energy distribution of the reference value of frequency modulation power among units.ConclusionsThe cooperative control strategy can effectively protect the speed of wind turbine and improve the frequency response of power grid.

As the major demand for the development and utilization of new energy, the large-scale development of wind power is a key support in achieving the strategic goal of “cabron peaking and carbon neutrality” for China. The problem of safe and stable operation of wind farms caused by external grid faults has become one of the key bottlenecks restricting the large-scale, clustered, and intelligent development of wind power. This paper mainly focuses on the voltage surge condition of the power grid. First, it analyzes the transient characteristics of high voltage ride-through (HVRT) of the doubly-fed induction generator-wind turbine, permanent magnet synchronous generator-wind turbine, and wind farms. Then, it summarizes the corresponding HVRT and post-fault voltage recovery coordinated optimal control strategies based on the different control areas, and it classifies and compares the working principles and advantages and disadvantages of various control strategies. Afterwards, it recapitulates the principle, advantages and disadvantages, and effects of the existing HVRT control method for large-scale wind farms, and analyzes the differences between the single wind turbine and the large-scale wind farms from the perspective of control structure. Finally, it discusses the development trend and potential research hotspots of wind farm voltage intelligent safety control in the future, aiming to provide reference for improving the large-scale application of wind power and the safe operation of power grids in China.

Fast and accurate analysis of the short-circuit characteristics of large wind farms has important engineering application value, and the short-circuit characteristics of wind farms under the influence of the wake effect vary greatly. Therefore, it is necessary to establish a wind farm short-circuit fault time equivalence model. A wind farm short-circuit fault dynamic equivalence method considering the effect of wake effect is proposed. First, the wake effect factor is defined to reflect the degree of the unit affected by the wake effect. Then, the wake effect factor is used as the grouping basis to reduce the variability of operating state of the units within the group under the influence of the wake effect. A positive- negative- zero-sequence network equivalence method is analyzed to improve the effectiveness of the equivalence model in asymmetric short-circuit faults. An equivalence method suitable for zero-sequence network is proposed and a platform is built for verification. The simulation results show that the dynamic short-circuit fault equivalence model proposed can accurately reflect the active and reactive short-circuit output characteristics of wind farms under the influence of the wake effect.

As the grid-connected capacity of a large number of wind farms continues to increase, their short-circuit current will have a great impact on protection. Therefore, accurate short-circuit current calculation is beneficial for correct protection action evaluation. Considering the issues related to the diversity of wind turbine types in wind farms and the inability to accurately simulate the failure transient process in previous studies, this research focuses on investigating the impact of control strategies during faults on the transient process of wind turbines. In this paper, the single-machine equivalent model of doubly-fed induction generators (DFIG) and permanent magnet synchronous machine (PMSM) is established by considering the types of wind turbines, and the calculation method of short-circuit current of hybrid wind farms is further proposed by using the classification aggregation equivalent effect method. The accuracy of short-circuit current calculation proposed in this article was verified on RTDS, and a power grid fault analysis method suitable for wind farm connection was proposed based on the characteristics of equivalent circuits of DFIG and PMSM during fault steady-state.

Modeling the atmospheric boundary layer (ABL) in the area of a wind farm using computational fluid dynamics (CFD) methods allows us to study the characteristics of air movement, the shading effect, the influence of relief, etc., and can be actively used in studies of local territories where powerful wind farms are planned to be located. The operating modes of a wind farm largely depend on meteorological phenomena, the intensity and duration of which cause suboptimal operating modes of wind farms, which require the use of modern tools for forecasting and classifying precipitation. The methods and approaches used to predict meteorological phenomena are well known. However, for designed and operated wind farms, the influence of meteorological phenomena on the operating modes, such as freezing rain and hail, remains an urgent problem. This study presents a multi-layered neural network for the classification of precipitation zones, designed to identify adverse meteorological phenomena for wind farms according to weather stations. The neural network receives ten inputs and has direct signal propagation between six hidden layers. During the training of the neural network, an overall accuracy of 81.78%, macro-average memorization of 81.07%, and macro-average memorization of 75.05% were achieved. The neural network is part of an analytical module for making decisions on the application of control actions (control of the boundary layer of the atmosphere by injection of silver iodide, ionization, etc.) and the formation of the initial conditions for CFD modeling. Using the example of the Ulyanovsk wind farm, a study on the movement of air masses in the area of the wind farm was conducted using the initial conditions of the neural network. Digital models of wind turbines and terrain were created in the Simcenter STAR-CCM+ software package, version 2022.1; an approach based on a LES model using an actuating drive disk model (ADM) was implemented for modeling, allowing calculation with an error not exceeding 5%. According to the results of the modeling of the current layout of the wind turbines of the Ulyanovsk wind farm, a significant overlap of the turbulent wake of the wind turbines and an increase in the speed deficit in the area of the wind farm were noted, which significantly reduced its efficiency. A shortage of speed in the near and far tracks was determined for special cases of group placement of wind turbines. [ABSTRACT FROM AUTHOR]

The wake effect reduces the wind speed at downstream wind turbines (WTs), making it necessary for the central controller to collect wind power generation data from each WT. However, wind farms (WFs) face a more complex problem in maintaining the voltage stability at the WT terminal while following the transmission system operator (TSO) instructions due to the information collection as well as the possible data loss during transmission. Therefore, this study proposes a coordinated control method for WTs and multiple power sources based on model predictive control under wake disturbance conditions, aiming to reduce the average voltage deviation in WT terminals and go close to the rated voltage and ensure effective compliance with TSO commands in large-scale WFs. Accordingly, the Jensen wake model was utilized to accurately calculate the available active and reactive power limits for each WT. Energy storage systems and static Var generators were modeled to coordinate and maintain the voltage in all WT terminals within the feasible range, providing peak shaving and valley filling support to reduce wind energy waste and shortfall, thereby enhancing the economic and operational reliability of WF. Further, the effectiveness of the proposed method was validated in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

In order to address the issue of premature attenuation of the lifespan of some wind turbines due to uneven fatigue among wind turbines in wind farms, an active power optimal scheduling strategy based on fatigue distribution was proposed. This method took active power distribution as the research object, comprehensively considered the influence of fatigue load and wake effect, combined weight redistribution was carried out according to the power prediction information of wind turbines and the power reference value of fatigue coefficient, and adaptive genetic algorithm was used to find the optimal weight coefficient. The simulation results demonstrate that the proposed method can track superior dispatching instructions, while achieving uniform fatigue levels among wind turbines in the field, reducing fatigue load, and decreasing the operation and maintenance costs of the wind farm. [ABSTRACT FROM AUTHOR]

A calculation method for optimizing the LCOE and the cost of subsequent PTX production from wind power is presented, based on Weibull parameters and cost functions. The PTX products methanol, ammonia and synthetic diesel are considered, as well as hydrogen, which is converted into an easily transportable form by hydrogenation of a liquid organic hydrogen carrier (LOHC). Haru‐Oni in Chile and Tarfaya in Morocco were chosen as examples of production sites with excellent wind conditions. These are compared with three domestic sites with very good to very moderate wind conditions in the order Dornum (North German coast), Senftenberg (Lausitz) and Ludwigshafen (Rhine Graben). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Drainage & Irrigation Machinery Engineering / Paiguan Jixie Gongcheng Xuebao is the property of Editorial Department of Drainage & Irrigation Machinery Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When offshore wind farms are connected to a hydrogen plant with dedicated transmission lines, for example, high-voltage direct current, the fluctuation of wind speed will influence the efficiency of the alkaline electrolyzer and deteriorate the techno-economic performance. To overcome this issue, firstly, an additional heating process is adopted to achieve insulation for the alkaline solution when power generated by wind farms is below the alkaline electrolyzer minimum power threshold, while the alkaline electrolyzer overload feature is used to generate hydrogen when wind power is at its peak. Then, a simplified piecewise model-based alkaline electrolyzer techno-economic analysis model is proposed. The improved economic performance of the islanded green hydrogen system with the proposed operation strategy is verified based on the wind speed data set simulation generated by the Weibull distribution. Lastly, the sensitivity of the total return on investment to wind speed parameters was investigated, and an islanded green hydrogen system capacity allocation based on the proposed analysis model was conducted. The simulation result shows the total energy utilization increased from 62.0768% to 72.5419%, and the return on investment increased from 5.1303%/month to 5.9581%/month when the proposed control strategy is adopted. [ABSTRACT FROM AUTHOR]

South America is a region that stands out worldwide for its biodiversity of ecosystems, cultural heritage, and potential considering natural resources linked to renewable energies. In the global crisis due to climate change, South American countries have implemented actions to carry out a progressive energy transition from fossil energies to renewable energies and contribute to the planet's sustainability. In this context, South American countries are implementing green strategies and investment projects linked to wind farms to move towards achieving the sustainable development goals for the year 2030 of the UN agenda and achieving low-carbon economies for the year 2050. This article studies the advances in wind energy implementation in South America, highlighting progress and experiences in these issues through a review of the scientific literature considering the year 2023. The methodology applied in this article was carried out through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the generation of scientific maps. As a result, this article presents the main developments, lessons learned/gaps, and future sustainable prospects on the road to 2050. According to the results, renewable wind energy infrastructure was applied in South America during the global climate change crisis era. Different levels of development in on-shore wind farms have been reached in each country. Also, a promising future exists for off-shore wind energy considering the highest potential. Finally, this article concludes that implementing emerging technologies like the production of green hydrogen and synthetic e-fuels looks like a synergetic clean energy solution combined with wind energy, which may transform the region into a world-class sustainable territory. [ABSTRACT FROM AUTHOR]

The research reported in this article focuses on a comparison of two different approaches to setting error bounds, or prediction intervals, on short-term forecasting of solar irradiation as well as solar and wind farm output. Short term in this instance relates to the time scales applicable in the Australian National Electricity Market (NEM), which operates on a five-minute basis throughout the year. The Australian Energy Market Operator (AEMO) has decided in recent years that, as well as point forecasts of energy, it is advantageous for planning purposes to have error bounds on those forecasts. We use quantile regression as one of the techniques to construct the bounds. This procedure is compared to a method of forecasting the conditional variance by use of either ARCH/GARCH or exponential smoothing, whichever is more appropriate for the specific application. The noise terms for these techniques must undergo a normalising transformation before their application. It seems that, for certain applications, quantile regression performs better, and the other technique for some other applications. [ABSTRACT FROM AUTHOR]

This paper explores an interesting approach to wind energy technology, focusing on a novel type of drag-driven vertical-axis wind turbines (VAWTs). Studied geometries employ rotor-shaped cross-sections, presenting a distinctive approach to harnessing wind energy efficiently. The rotor-shaped cross-section geometries are examined for their aerodynamic efficiency, showcasing the meticulous engineering behind this innovation. The drag-driven turbine shapes are analyzed for their ability to maximize energy extraction in a variety of wind conditions. A significant aspect of these turbines is their adaptability for diverse applications. This article discusses the feasibility and advantages of utilizing these VAWTs in fence configurations, offering an innovative integration of renewable energy generation with physical infrastructure. The scalability of the turbines is highlighted, enabling their deployment as a fence around residential properties or as separators between highway lanes and as energy-generating structures atop buildings. The scientific findings presented in this article contribute valuable insights into the technological advancements of rotor-shaped VAWTs and their potential impact on decentralized wind energy generation. The scalable and versatile nature of these turbines opens up new possibilities for sustainable energy solutions in both urban and residential settings, marking a significant step forward in the field of renewable energy research and technology. In particular, it was shown that among the proposed rotor geometries, the five-blade rotor was characterized by the highest efficiency and, working in a palisade configuration with a spacing of 10 mm to 20 mm, produced higher average values of the torque coefficient than the corresponding Savonius turbine. [ABSTRACT FROM AUTHOR]

With the increasing penetration of wind power into the grid, its intermittent and fluctuating characteristics pose a challenge to the frequency stability of grids. Energy storage systems (ESSs) are beginning to be used to assist wind farms (WFs) in providing frequency support due to their reliability and fast response performance. However, the current schemes mostly focused on the sole operation of either WT or ESSs for frequency support, lacking in economy and reliability. To address these issues, this work proposes a sliding mode consensus control (SMCC) method for ESSs to assist the frequency response of WTs. First, a coordination strategy for the WTs and ESS is proposed to reinforce the frequency support capability of a WT–ESS by determining the optimal capacity of ESSs. Meanwhile, a novel SMCC is proposed to achieve proportional power sharing among ESSs and increase the total power output of the entire WFs during wind energy shortages. Moreover, an event triggered communication mechanism is utilized to reduce the information exchanges among ESSs. Subsequently, a Lyapunov stability analysis is conducted to prove the stability and finite time consensus of the proposed method. Simulation case studies are conducted in IEEE 39-bus system to validate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Copyright of Safety & Security Sciences Review / Biztonságtudományi Szemle is the property of Obuda University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper describes a new method for determining the energy efficiency of the life cycles of wind farms by the aggregated data of energy costs. The rationale for the use of aggregated data to determine energy costs during the life cycle of the wind farms is given. The classification of wind turbines and wind farms elements by the parameters and technical characteristics of the elements with subsequent division into groups for which aggregated data of energy costs are determined is given. Within the framework of the method have been developed an algorithm for determining energy costs on the production of elements of wind turbines and wind farms and formulas for their calculation during the life cycle of wind farms. In order to test the method, energy cost was calculated during the life cycle for two wind farms with wind turbines that differ parameters and technical characteristics of the elements and their energy efficiency was determined. It has been shown that the use of a hydrogen storage unit as part of a wind turbine makes it possible to efficiently use energy during down periods and increase the efficiency of the installation by 25–30%. It is noteworthy that a wind-hydrogen farm allows not only to accumulate excess energy during lean periods, but also to save the resource of wind turbines. When the energy reserve in the hydrogen battery reaches close to full, part of the wind turbines of the wind-hydrogen farm can be automatically temporarily stopped in a given order. This technology can provide an extension of the warranty service life for each wind turbine. [ABSTRACT FROM AUTHOR]

A wind farm composed of doubly-fed wind turbines (DFWTs) based on the virtual synchronous generator (DFWTs-VSG) control strategy exacerbates the fatigue load on the main shaft of the DFWT-VSGs in the wind farm when responding to the frequency variation of the power system. The central controller of the wind farm can reduce the main shaft fatigue load of each DFWT-VSG by reasonably allocating the required damping coefficient of each DFWT-VSG while engaging in power system frequency response. In this study, a damping coefficient allocation method considering the main shaft fatigue load is proposed. First, a discretization equation that quantifies the relationship between the damping coefficient and its main shaft torque in DFWT-VSG is constructed. Then, based on this discretization equation, the minimization of the sum of main shaft torque fluctuation from all DFWT-VSGs is taken as the objective function in the central controller, and the constraints of the damping coefficient are set based on the support capacity of the wind farm and the operating state of each DFWT-VSG. Finally, the required damping coefficient of each DFWT-VSG is allocated in real-time based on the fmincon algorithm in the central controller. Simulation results verify the superiority of the proposed damping coefficient allocation method. [ABSTRACT FROM AUTHOR]

With global shifts towards sustainable energy models, the urgency to address rising fossil fuel prices, military conflicts, and climate change concerns has become evident. The article aims to identify the development of wind energy in Poland. This study introduces an integrated methodology for enhancing renewable energy capacities by selecting new construction sites for onshore wind farms across Poland. The proposed methodology utilises a hybrid model incorporating multiple criteria decision-making methods, such as the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), alongside the semiautomated spatial analysis method using QGiS software (v. 3.32 Lima). The model considers economic, social, and environmental criteria and limitations, offering a comprehensive approach to the decision-making process. It was found that wind farms occupy 460.7 km2 in Poland, with a 250 m buffer around each turbine and a total power capacity of 5818 MW. The results show that an additional 7555.91 km2 of selected areas, 2.34% of the country's area, theoretically offer significant opportunities for wind energy development. The spatial analysis identifies potential sites with promising opportunities for domestic and international renewable energy investors. The study's findings contribute towards achieving national and EU renewable energy targets while offering a replicable framework for informed spatial planning decisions in other regions. [ABSTRACT FROM AUTHOR]

This study proposes an enhanced Kepler Optimization (EKO) algorithm, incorporating fractional-order components to develop a Proportional-Integral-First-Order Double Derivative (PI–(1+DD)) controller for frequency stability control in multi-area power systems with wind power integration. The fractional-order element facilitates efficient information and past experience sharing among participants, hence increasing the search efficiency of the EKO algorithm. Furthermore, a local escaping approach is included to improve the search process for avoiding local optimization. Applications were performed through comparisons with the 2020 IEEE Congress on Evolutionary Computation (CEC 2020) benchmark tests and applications in a two-area system, including thermal and wind power. In this regard, comparisons were implemented considering three different controllers of PI, PID, and PI–(1+DD) designs. The simulations show that the EKO algorithm demonstrates superior performance in optimizing load frequency control (LFC), significantly improving the stability of power systems with renewable energy systems (RES) integration. [ABSTRACT FROM AUTHOR]

For large-scale constructed wind farms, reducing wake loss and improving the overall output power are the main objectives for their optimal operation. Therefore, a wind farm power maximization method based on a multi-strategy improved sparrow search algorithm (MS-ISSA) is proposed in this paper. Integrating the wake propagation mechanism of wind turbines and the characteristics of the classic Jensen wake model, the Jensen-Gaussian wake model and wake superposition model were constructed to accurately calculate the wind farm wake distribution. The constructed Jensen-Gaussian wake model and wake superposition model can accurately describe the non-uniform distribution characteristics of wake velocity. The Sin chaotic model, Cauchy distribution, and hyperparameter adaptive adjustment strategy were used to improve the sparrow search algorithm (SSA), and the optimization ability, convergence speed, and stability of the SSA were improved. Accordingly, considering the maximum output power of the wind farm as the optimization target and axial induction factor as the optimization variable, a coordinated optimization model for wind turbines based on MS-ISSA was proposed to realize the coordinated optimal operation of wind turbines with reduced wake loss. Considering the Danish Horns Rev wind farm as the research object, the results of optimization using particle swarm optimization algorithm, whale optimization algorithm, basic sparrow search algorithm, and MS-ISSA were calculated and analyzed. The calculation results revealed that under different incoming wind conditions, the MS-ISSA exhibited better optimization results than the other optimization algorithms. [ABSTRACT FROM AUTHOR]

This article provides a detailed evaluation of cutting-edge artificial intelligence (AI) approaches and metaheuristic algorithms for optimizing wind turbine location inside wind farms. The growing need for renewable energy sources has fueled an increase in research towards efficient and sustainable wind farm designs. To address this challenge, various AI techniques, including genetic algorithms (GA), particle swarm optimization (PSO), simulated annealing, artificial neural networks (ANNs), convolutional neural networks (CNNs), and reinforcement learning, have been explored in combination with metaheuristic algorithms. The goal is to discover optimal sites for turbine placement based on a variety of parameters such as energy output, cost-effectiveness, environmental impact, and geographical restrictions. The paper examines the advantages and disadvantages of each strategy and highlights current breakthroughs in the area. This assessment adds to continuing efforts to optimize wind farm design and promote the use of clean and sustainable energy sources by offering significant insights into current advances. [ABSTRACT FROM AUTHOR]

In a wind farm, the grounding circuit is a complex electrical system made up of several interconnected groundings. Its role is very important for the protection of the electrical equipment during a direct lightning strike. To contribute to the optimization of the conception of this complex grounding system in his real configuration (wind turbine towers, grounding grids, and several hundred meters of buried conductor wires ...), we propose in this paper a realistic modeling. Realistic because, to model in all rigors using an electromagnetic field formalism a problem of propagation of electromagnetic transients in electrical device of large geometrical dimensions where the main environment (soil) is not homogeneous does not bring an appreciable advantage and consumes a very high calculation time. Also, the ideal for the engineer is to have a model that is easy to understand, accessible in its theory and obviously preserving the accuracy of the results and with very low computation time. In this work, to study the lightning surges in a wind farm, mainly to know the Ground Potential Rise and the distribution of the currents in the conductors, we use a modeling based on the transmission line equations. Our modeling is carried out by considering the effect of the frequency on the electrical characteristics (resistivity and dielectric permittivity) of the soil; the results we obtain are without loss of precision and with a very short calculation time. [ABSTRACT FROM AUTHOR]

Due to the intermittence of renewable sources, reliable customer service is not guaranteed unless hybrid-energy systems emerged with energy storage is employed together with an appropriate energy management system (EMS). Usually integrating different renewable energy sources can able to supply a reliable power to consumers. In this paper, an efficient EMS for a four-wire, 1000 kW Microgrid system is presented. The studied system consists of three wind farms and three solar plants with a central link to battery banks, an electrolyzer, and a fuel-cell system. A hybrid optimization algorithm is adapted to track the maximum power of PV arrays under variable weather and partial shading conditions. The tracking algorithm merges the benefits of the Whale Optimization and Perturb-and-Observe (P&O) technique. To track the maximum available power of wind plants, a P&O algorithm is established by calculating the proper duty cycle of a boost dc/dc converter that regulates the load current. A TS-Fuzzy-based controller for the inverter is applied to provide an acceptable power quality level associated with the battery bank, electrolyzer, and fuel cell. The suggested inverter controllers provide multi tasks such as reactive power compensator, voltage regulator under unbalanced loads, and active filter. The validity of the proposed system is verified via Extensive Hardware-in-Loop (HIL). [ABSTRACT FROM AUTHOR]

Over the years, researchers have proposed models for analyzing and predicting the low-frequency behavior of wind farm grounding systems. The lumped parameter modeling based on circuit theory presents an acceptable compromise between ease of implementation, consumption of computational resources, and accuracy of the results. This paper addresses the impact of mutual couplings between grounding elements in measuring the wind turbines grounding impedance using a clamp-on-ground meter. Furthermore, it proposes a new lumped parameter model for wind farm grounding to include the representation of such effects. In this study, computer simulation uses lumped parameter modeling and electromagnetic field modeling to evaluate turbine grounding impedance readings based on actual wind farm project data. The simulation results, assuming the electromagnetic model as a reference, have demonstrated that the proposed solution can estimate the low-frequency impedance of the measuring loop with an average error of 85.8% lower than existing state-of-the-art models. [ABSTRACT FROM AUTHOR]

Wind turbines have gained popularity as one of the efficient micro grid energy generation sources over the years. However, wind energy yield has been greatly impacted by the wind farm wake effect, especially when the size of the wind farm becomes very large. One way to address this challenge is through wind turbine arrangements and their scalability. Developing effective means of optimizing wind farm configurations is a major concern for energy communities. This has continuously led to increased power generation and a corresponding cost reduction. As such, this research article developed an optimal large-scale offshore wind turbine placement based on wind farm layout design. The system developed focuses on wind turbine placement in wind farm layouts that have a negative influence on the capital investment due to the increasing wake effect thereby reducing energy production. A multi-objective function consisting of wake effect and component costs within the wind farm is formulated. After-which a cuckoo search algorithm technique is employed in the wind farm model to optimize the optimization problem (minimizing the wake effect while improving power output and cost). Four test case scenarios were considered when implementing the wind model. The results obtained from the developed scheme were compared with those obtained when other optimization techniques were used, using power output and cost as performance metrics. The obtained power output and cost for the test case scenario in the ideal wind turbine position on the wind farm are 18288.3KW, 19680.1KW, 18879.9KW, 21105.8KW and 26.9, 28.7, 26.9, and 29.8KW, respectively. This shows that the result obtained from the developed scheme outperformed that obtained from PSO and that of WOA. [ABSTRACT FROM AUTHOR]

To address uncertainty as well as transient stability constraints simultaneously in the preventive control of wind farm systems, a novel three-stage optimization strategy is established in this paper. In the first stage, the probabilistic multi-objective particle swarm optimization based on the point estimate method is employed to cope with the stochastic factors. The transient security region of the system is accurately ensured by the interior point method in the second stage. Finally, the verification of the final optimal objectives and satisfied constraints are enforced in the last stage. Furthermore, the proposed strategy is a general framework that can combine other optimization algorithms. The proposed methodology is tested on the modified WSCC 9-bus system and the New England 39-bus system. The results verify the feasibility of the method. [ABSTRACT FROM AUTHOR]

The DFIG-based wind farm faces sub-synchronous oscillation (SSO) when it is integrated with a series-compensated transmission system. The equivalent SSO damping is influenced by both wind speed and compensation level. However, it is hard for the wind farm to obtain a compensation level in time to predict the SSO risk. In this paper, an SSO risk prediction method for a DFIG wind farm is proposed based on the characteristics identified from noise-like signals. First, SSO-related parameters are analyzed. Then, the potential SSO frequency and damping are identified from signals at normal working points by integration using variational mode decomposition and Prony analysis. Finally, a fuzzy inference system is established to predict the SSO risk of a DFIG wind farm. The effectiveness of the proposed method is verified by simulation. The proposed prediction method can predict SSO risks caused by the variation in wind speed, while the transmission line parameters are undetectable for the wind farm. [ABSTRACT FROM AUTHOR]

Despite the unique advantages a doubly fed induction generator (DFIG) offers to the grid‐integrated renewable energy systems, they have a limitation of being susceptible to grid fault as their stator windings are directly connected to the grid. The fault current limiters (FCLs) provide a sustainable solution by enhancing the fault ride‐through capability and thus it improve the transient performance of a DFIG. In this work, a multi‐inductor‐based H‐bridge fault current limiter (HBFCL) is proposed to augment the transient performance of a DFIG. The operational efficacy of the HBFCL is evaluated through the administration of both symmetrical and asymmetrical fault scenarios. The effectiveness of the HBFCL is further investigated by comparing the performance of the HBFCL with that of the bridge‐type series dynamic braking resistor (BSDBR). Both the graphical and numerical interpretations of the simulation result assert that the HBFCL improves the transient performance of a DFIG‐based wind farm and outweighs the performance of the BSDBR in all aspects. [ABSTRACT FROM AUTHOR]

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)