Wind Farm In Order Research Articles

Study on Grid-Connected Power Quality Improvement of Wind Farms Based on Repetitive Controller

The project highlights the wind energy and the process followed in the wind farms in order to generate the energy by using wind power and wind capacity as well. Moreover, it has been observed that wind energy is also considered one of the most effective electrical energy sources. The application of wind energy can be beneficial for different aspects of the business sectors of different countries across the globe and this is possible because of its cost-effectiveness. Furthermore, this study encompasses the strategies employed within wind farms to preserve and efficiently harness wind power for the generation of electrical energy. Notably, the project delineates the utilization of a diverse array of instruments, each instrumental in the conversion of wind power into valuable wind energy. Beyond the realm of technology, wind farms grapple with a spectrum of formidable challenges, stemming from the inherently capricious nature of wind. In addition, various issues concerning power quality and the stability of the power system have also been identified within these wind farms. To ameliorate these multifaceted challenges, the project introduces a range of meticulous control measures for implementation.

Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas

There is an urgent need to develop accurate predictions of power production, wake losses and array–array interactions from multi-GW offshore wind farms in order to enable developments that maximize power benefits, minimize levelized cost of energy and reduce investment uncertainty. New, climatologically representative simulations with the Weather Research and Forecasting (WRF) model are presented and analyzed to address these research needs with a specific focus on offshore wind energy lease areas along the U.S. east coast. These, uniquely detailed, simulations are designed to quantify important sources of wake-loss projection uncertainty. They sample across different wind turbine deployment scenarios and thus span the range of plausible installed capacity densities (ICDs) and also include two wind farm parameterizations (WFPs; Fitch and explicit wake parameterization (EWP)) and consider the precise WRF model release used. System-wide mean capacity factors for ICDs of 3.5 to 6.0 MWkm−2 range from 39 to 45% based on output from Fitch and 50 to 55% from EWP. Wake losses are 27–37% (Fitch) and 11–19% (EWP). The discrepancy in CF and wake losses from the two WFPs derives from two linked effects. First, EWP generates a weaker ‘deep array effect’ within the largest wind farm cluster (area of 3675 km2), though both parameterizations indicate substantial within-array wake losses. If 15 MW wind turbines are deployed at an ICD of 6 MWkm−2 the most heavily waked wind turbines generate an average of only 32–35% of the power of those that experience the freestream (undisturbed) flow. Nevertheless, there is no evidence for saturation of the resource. The wind power density (electrical power generation per unit of surface area) increases with ICD and lies between 2 and 3 Wm−2. Second, EWP also systematically generates smaller whole wind farm wakes. Sampling across all offshore wind energy lease areas and the range of ICD considered, the whole wind farm wake extent for a velocity deficit of 5% is 1.18 to 1.38 times larger in simulations with Fitch. Over three-quarters of the variability in normalized wake extents is attributable to variations in freestream wind speeds, turbulent kinetic energy and boundary layer depth. These dependencies on meteorological parameters allow for the development of computationally efficient emulators of wake extents from Fitch and EWP.

Optimal position and distribution mode for on-site hydrogen electrolyzers in onshore wind farms for a minimal levelized cost of hydrogen (LCoH)

Abstract. Storing energy is a major challenge in achieving a 100 % renewable energy system. One promising approach is the production of green hydrogen from wind power. This work proposes a method for optimizing the design of wind–hydrogen systems for existing onshore wind farms in order to achieve the lowest possible levelized cost of hydrogen (LCoH). This is done by the application of a novel Python-based optimization model that iteratively determines the optimal electrolyzer position and distribution mode of hydrogen for given wind farm layouts. The model includes the costs of all required infrastructure components. It considers peripheral factors such as existing and new roads, necessary power cables and pipelines, wage and fuel costs for truck transportation, and the distance to the point of demand (POD). Based on the results, a decision can be made whether to distribute the hydrogen to the POD by truck or pipeline. For a 23.4 MW onshore wind farm in Germany, a minimal LCoH of EUR 4.58 kgH2-1 at an annual hydrogen production of 241.4 tH2a-1 is computed. These results are significantly affected by the position of the electrolyzer, the distribution mode, varying wind farm and electrolyzer sizes, and the distance to the POD. The influence of the ratio of electrolyzer power to wind farm power is also investigated. The ideal ratio between the rated power of the electrolyzer and the wind farm lies at around 10 %, with a resulting capacity factor of 78 % for the given case. The new model can be used by system planners and researchers to improve and accelerate the planning process for wind–hydrogen systems. Additionally, the economic efficiency, hence competitiveness, of wind–hydrogen systems is increased, which contributes to an urgently needed accelerated expansion of electrolyzers. The results of the influencing parameters on the LCoH will help to set development goals and indicate a path towards a cost-competitive green wind–hydrogen system.

Using Arithmetic Optimization Algorithm to Allocate and Size Wind Energy Systems in RDSs

Recent years have seen a marked increase in the world’s energy needs. Numerous studies have been conducted to examine distributed generation (D.G.) utilizing renewable energy sources (R.E.S.s) in order to address this need. The number of environmental problems that are resolved by the usage of traditional power plants is also decreased by these renewable sources. The ideal position and size of the RESs-DG significantly influence the bus voltage profile, power quality, and efficiency of Radial Distribution Systems (RDS) because of power losses. In this study, the use of wind energy systems as a DG source in RDS is investigated. One of the most common RESs used as DG sources, the ideal location and size for wind system, was chosen to demonstrate this enquiry. The goal of this optimization work, which used the Arithmetic Optimization Algorithm (AOA), was to increase system efficiency by minimizing power losses and improving the voltage profile and power quality. Three widely used RDS, including the IEEE 31 and 69 bus systems, have been used to evaluate how well the recommended technique has been implemented. Genetic Algorithm (GA) is offered to examine the efficacy of the recommended AOA. The findings show that the used AOA approach can pinpoint the appropriate size and positioning of a wind farm in order to reduce power loss, enhance voltage profile, and outperform other existing tactics with superiority over GA.

Using Arithmetic Optimization Algorithm to Allocate and Size Wind Energy Systems in RDSs

Recent years have seen a marked increase in the world’s energy needs. Numerous studies have been conducted to examine distributed generation (DG) utilizing renewable energy sources (RESs) in order to address this need. The number of environmental problems that are raised by the usage of traditional power plants is also decreased by these renewable sources. The ideal position and size of the RESs-DG significantly influence the bus voltage profile, power quality, and efficiency of Radial Distribution Systems (RDS) because of power losses. In this study, the use of wind energy systems as a DG source in RDS is investigated. One of the most common RESs used as DG sources, the ideal location and size for wind system, was chosen to demonstrate this enquiry. The goal of this optimization work, which used the Arithmetic Optimization Algorithm (AOA), was to increase system efficiency by minimizing power losses and improving the voltage profile and power quality. Two widely used RDS, including the IEEE 31 and 69 bus systems, have been used to evaluate how well the recommended technique has been implemented. Genetic Algorithm (GA) is offered to examine the efficacy of the recommended AOA. The findings show that the used AOA approach can pinpoint the appropriate size and positioning of a wind farm in order to reduce power loss, enhance voltage profile, and outperform other existing tactics with superiority over GA.

Atmospheric Drivers of Wind Turbine Blade Leading Edge Erosion: Review and Recommendations for Future Research

Leading edge erosion (LEE) of wind turbine blades causes decreased aerodynamic performance leading to lower power production and revenue and increased operations and maintenance costs. LEE is caused primarily by materials stresses when hydrometeors (rain and hail) impact on rotating blades. The kinetic energy transferred by these impacts is a function of the precipitation intensity, droplet size distributions (DSD), hydrometeor phase and the wind turbine rotational speed which in turn depends on the wind speed at hub-height. Hence, there is a need to better understand the hydrometeor properties and the joint probability distributions of precipitation and wind speeds at prospective and operating wind farms in order to quantify the potential for LEE and the financial efficacy of LEE mitigation measures. However, there are relatively few observational datasets of hydrometeor DSD available for such locations. Here, we analyze six observational datasets from spatially dispersed locations and compare them with existing literature and assumed DSD used in laboratory experiments of material fatigue. We show that the so-called Best DSD being recommended for use in whirling arm experiments does not represent the observational data. Neither does the Marshall Palmer approximation. We also use these data to derive and compare joint probability distributions of drivers of LEE; precipitation intensity (and phase) and wind speed. We further review and summarize observational metrologies for hydrometeor DSD, provide information regarding measurement uncertainty in the parameters of critical importance to kinetic energy transfer and closure of data sets from different instruments. A series of recommendations are made about research needed to evolve towards the required fidelity for a priori estimates of LEE potential.

Flow in a large wind field with multiple actuators in the presence of constant vorticity

Study of wind farms is an area of active research. Researchers have proposed simplified wind farm models that define the wake structure in a wind farm and how they affect the performance of the wind turbines. Interestingly, these models do not take into account an important aspect of fluid flow, i.e., the fluid–structure interaction (FSI) between the turbines and the wind, which has an important role to play. This motivated researchers to implement numerical analysis tools to model the geometry of the wind turbines in computational fluid dynamics (CFD) based models of a wind farm in order to better understand the wake structure and study the performance of the wind farms. However, modeling the complex geometry of the blades and the turbines makes these models computationally expensive. In this paper, we propose an FSI methodology which can simplify the blade resolving CFD models and eliminate the requirement for modeling these complex geometries during preliminary engineering phase. As an example, we present simulations of up to three back-to-back wind turbines and compare the results with those obtained from wind engineering software, FLORIS. The proposed methodology demonstrates how the approach can be used to develop a relatively less computationally expensive wind farm model. The approach formulated in this paper follows an intermediate way between the analytical wind farm models and CFD models by introducing modifications to one of the most basic wind farm models (Jensen's model) and using it to develop a simplified CFD model using the decomposed immersed interface method strategy.

In Situ Observations of Wind Turbines Wakes with Unmanned Aerial Vehicle BOREAL within the MOMEMTA Project

The MOMENTA project combines in situ and remote sensing observations, wind tunnel experiments, and numerical modeling to improve the knowledge of wake structure in wind farms in order to model its impact on the wind turbines and to optimize wind farm layout. In this context, we present the results of a first campaign conducted with a BOREAL unmanned aerial vehicle (UAV) designed to measure the three wind components with a horizontal resolution as fine as 3 m. The observations were performed at a wind farm where six turbines were installed. Despite the strong restrictions imposed by air traffic control authorities, we were able to document the wake area of two turbines during two flights in April 2021. The flight patterns consisted of horizontal racetracks with various orientations performed at different distances from the wind turbines; thus, horizontal wind speed fields were built in which the wind reduction area in the wake is clearly displayed. On a specific day, we observed an overspeed area between the individual wakes of two wind turbines, likely resulting from the cumulative effect of the wakes generated behind two successive rows of turbines. This study demonstrates the potential of BOREAL to document turbine wakes.

PSO Self-Tuning Power Controllers for Low Voltage Improvements of an Offshore Wind Farm in Taiwan

A de-loaded real power control strategy is proposed to decrease the real power output and increase the reactive power output of a grid-connected offshore wind farm in order to improve the voltage profile when the wind farm is subject to a grid fault. A simplified linear model of the wind farm is first derived and a fixed-gain proportional-integral (PI) real power controller is designed based on the pole-zero cancellation method. To improve the dynamic voltage response when the system is subject to a major disturbance such as a three-phase fault in the grid, a self-tuning controller based on particle swarm optimization (PSO) is proposed to adapt the PI controller gains based on the on-line measured system variables. Digital simulations using MATLAB/SIMULINK were performed on an offshore wind farm connected to the power grid in central Taiwan in order to validate the effectiveness of the proposed PSO controller. It is concluded from the simulation results that a better dynamic voltage response can be achieved by the proposed PSO self-tuning controller than the fixed-gain controller when the grid is subject to a three-phase fault. In addition, low voltage ride through (LVRT) requirements of the local utility can be met by the wind farm with the proposed power controller.

Multi-agent based simulation-optimization of maintenance routing in offshore wind farms

Offshore wind energy is expected to be the first source of energy in the future thanks to its numerous advantages. However, its maintenance is a complex and costly activity, which is increasingly attracting the attention of researchers and industrials. This paper proposes a simulation-optimization approach for the routing and the scheduling of maintenance for offshore wind farms in order to minimize cost while keeping a high availability of wind turbines. A multi-agent based modeling and simulation is introduced to deal with the complexity of the system. Ant Colony System (ACS) algorithm is used to optimize maintenance tasks routing. In order to make the proposed approach more realistic, we have considered several parameters and constraints such as weather conditions, resources cost, maintenance duration. Several scenarios are experimented to demonstrate the approach efficiency during all the life cycle of the farm. The obtained results show the improvement of cost, energy generation and turbine availability.

Set-point optimization in wind farms to mitigate effects of flow blockage induced by atmospheric gravity waves

Abstract. Recently, it has been shown that flow blockage in large wind farms may lift up the top of the boundary layer, thereby triggering atmospheric gravity waves in the inversion layer and in the free atmosphere. These waves impose significant pressure gradients in the boundary layer, causing detrimental consequences in terms of a farm's efficiency. In the current study, we investigate the idea of controlling the wind farm in order to mitigate the efficiency drop due to wind-farm-induced gravity waves and blockage. The analysis is performed using a fast boundary layer model which divides the vertical structure of the atmosphere into three layers. The wind-farm drag force is applied over the whole wind-farm area in the lowest layer and is directly proportional to the wind-farm thrust set-point distribution. We implement an optimization model in order to derive the thrust-coefficient distribution, which maximizes the wind-farm energy extraction. We use a continuous adjoint method to efficiently compute gradients for the optimization algorithm, which is based on a quasi-Newton method. Power gains are evaluated with respect to a reference thrust-coefficient distribution based on the Betz–Joukowsky set point. We consider thrust coefficients that can change in space, as well as in time, i.e. considering time-periodic signals. However, in all our optimization results, we find that optimal thrust-coefficient distributions are steady; any time-periodic distribution is less optimal. The (steady) optimal thrust-coefficient distribution is inversely related to the vertical displacement of the boundary layer. Hence, it assumes a sinusoidal behaviour in the streamwise direction in subcritical flow conditions, whereas it becomes a U-shaped curve when the flow is supercritical. The sensitivity of the power gain to the atmospheric state is studied using the developed optimization tool for almost 2000 different atmospheric states. Overall, power gains above 4 % were observed for 77 % of the cases with peaks up to 14 % for weakly stratified atmospheres in critical flow regimes.

Regulation for offshore wind power development in Brazil

Offshore wind power is one of the fastest growing renewable energies and the Brazilian coastline with nearly 9650 km has a great wind potential. Even though offshore oil and gas regulation is well established, this is still unknown for offshore wind power. This article aims to propose a regulatory framework for development of offshore wind power in Brazil. A literature review and case studies were conducted on offshore wind regulation in Denmark, United Kingdom and Germany and on Brazilian offshore oil and gas. The framework involves three macro phases: Pre-development, Development and Operation and Post-Operation. As Brazil is a new market, the use of two concession regimes is proposed: open door and tender. The framework proposes a feed-in tariff contracted in the tender with readjustments by the General Index of Market Prices. The required licenses, authorized by ANEEL, IBAMA and COMAER are: area concession, simplified license for studies, marine and air collision, cable laying, construction and operation and pre-decommissioning. The research concludes that the legal framework should consider the entire life cycle of wind farm in order to avoid future regulatory problems such as those that occurred with the offshore oil and gas, which did not consider the decommissioning stage.

Partial repowering analysis of a wind farm by turbine hub height variation to mitigate neighboring wind farm wake interference using mesoscale simulations

Repowering augments the power generation capacity of a wind farm in order to enhance the exploitation of the high mean wind speed regions. In this study, a partial repowering strategy has been discussed for an onshore commercial-scale wind farm with deteriorated performance under the influence of wakes originating from upstream wind farms. The mesoscale Weather Research and Forecasting (WRF) model with Wind Farm Parameterization has been employed to evaluate the wind speed and power deficit observed by the individual wind turbine generators. Wakes induced by the upstream wind farms are observed to cause a reduction in wind speed and power output by up to 15% and 35%, respectively. The predicted power was also validated by the observed data for each turbine generator. The hub heights of selected wind turbines with the highest power deficit are varied to alleviate the influence of upstream wind farm wake interference. Power output and wind shear profiles have been evaluated at new hub heights of 61.5 m and 100 m, and results are compared with the existing hub height turbine generators at 80 m. Power output reduced by up to 12% at the lower hub height of 61.5 m as compared to the default case, while an increase of about 13.6% is observed for the 100 m hub height case. A mean increase of 7.5% is estimated in the total wind power generation from the wind farm under evaluation. This paper presents a theoretical and engineering platform for partial repowering of wind farms with depleted generation due to inter-farm wake interaction.

АНАЛІЗ АКУСТИЧНОГО ВПЛИВУ ВІТРОЕНЕРГЕТИЧНИХ ПАРКІВ УКРАЇНСЬКИХ КАРПАТ НА ПРИЛЕГЛУ СЕЛІТЕБНУ ТЕРИТОРІЮ

У статті проаналізовано акустичне навантаження вітрових електростанцій з метою встановлення їх впливу на стан довкілля для забезпеченняекологічної безпеки. Оцінено вплив вітроенергетичних установок проектованої вітроенергетичної станції в межах Боржавської ПолониниСхідних флішових Карпат на акустичний та вібраційний режим прилеглої території. Проведено акустичні розрахунки у програмі АРМ «Акустика» 2.4МНПО «Екоблік» та порівняно отримані результати з допустимими рівнями акустичного навантаження за діючими нормативними документами. Урезультаті, розрахункові значення рівнів акустичного забруднення на межі санітарно-захисної зони відповідають чинним нормативам.

Control strategy to improve power transmission capacity from wind farms to weak AC grids

—With the increasing connection of large wind farms based on direct driven Permanent Magnet Synchronous Generators (PMSGs) to weak AC grids, the steady state characteristics of such wind farms, particularly those connected to very weak AC systems with a Short Circuit Ratio (SCR) of 1, are important factors for analysis with a view to improvement. In this paper, the issue is thus analysed in order to identify the factors limiting transfer capability from wind farm output. This paper thus presents a simulation model of a wind farm connected to a very weak AC grid, and a vector control strategy is used with respect to the full power converters of the wind farm in order to track current order injected from the converters into the AC system. A supplementary outer loop control is proposed to support grid voltage and to maximise transferred power into the very weak AC grid, based on a droop gain which is allowed to update the reactive power reference for reactive power control for the grid side converter based on changes to the output of the wind turbine, within grid code requirements. The simulation results prove that the proposed control system offers a promising method for addressing challenges arising in active power transmission, and thus increasing transferred active power from such wind farms to very weak grids.

An engineering condition indicator for condition monitoring of wind turbine bearings

AbstractCondition monitoring (CM) of wind turbine becomes significantly important part of wind farms in order to cut down operation and maintenance costs. The large amount of CM system vibration data collected from wind turbines are posing challenges to operators in signal processing. It is crucial to design sensitive and reliable condition indicator (CI) in wind turbine CM system. Bearing plays an important role in wind turbine because of its high impact on downtime and component replacement. CIs for wind turbine bearing monitoring are reviewed in the paper, and the advantages and disadvantages of these indicators are discussed in detail. A new engineering CI (ECI), which combined the energy and kurtosis representation of the vibration signal, is proposed to meet the requirement of easy applicability and early detection in wind turbine bearing monitoring. The quantitative threshold setting method of the ECI is provided for wind turbine CM practice. The bearing run‐to‐failure experiment data analysis demonstrates that ECI can evaluate the overall condition and is sensitive to incipient fault of bearing. The effectiveness in engineering of ECI is validated though a certain amount of real‐world wind turbine generator and gearbox bearing vibration data.

Lifetime extension of waked wind farms using active power control

This paper studies a new active power control (APC) of waked wind farms in order to extend the lifetime of highly loaded wind turbines. We demonstrate that the structural fatigue loading of a single turbine can be significantly alleviated, while the wind farm power production follows a power reference signal. Then, an optimization problem, subjected to a data-driven fatigue load model, is formulated to balance the lifetime fatigue loading of the wind turbines operating within a waked wind farm. A Game-Theoretic (GT) approach is employed to find a lifetime fraction, wherein a wind turbine should actively reject its own dynamic loadings due to turbulence. A large-eddy simulation model is employed for resolving the turbulent flow, the wake structures and its interaction with the atmospheric boundary layer. The applicability and key features of the controller are discussed with a wind farm example consisting of 2×2 turbines. The overall increase of wind farm lifetime is evaluated using the damage equivalent load (DEL) of the tower base fore-aft bending moment of the individual wind turbines.

Effects of axial induction control on wind farm energy production - A field test

Recent years have seen an increased interest in literature in Active Wake Control (AWC) strategies, which aim to reduce wake losses in wind farms in order to increase the energy yield and/or decrease loading. This paper presents the results from field tests with one of the AWC strategies, called axial induction control, at a commercial wind farm. To this end, the pitch angle offset for the most upstream wind turbines were optimized for each wind direction to maximize the power capture of the whole farm. This optimization is performed using the wake model FarmFlow, a code based on parabolized Computational Fluid Dynamics (CFD). After calibration of the wind direction measurement and implementation of the optimized pitch settings, a measurement campaign of one year has been performed. The analysis of the measurements indicate that axial induction control results in increased energy production.

An optimisation model for scheduling the decommissioning of an offshore wind farm

An optimisation model is proposed for scheduling the decommissioning of an offshore wind farm in order to minimise the total cost which is comprised of jack-up vessel, barge (transfer) vessel, inventory, processing and on-land transportation costs. This paper also presents a comprehensive review of the strategic issues relating to the decommissioning process and of scheduling models that have been applied to offshore wind farms. A mathematical model using integer linear programming is developed to determine the optimal schedule considering several constraints such as the availability of vessels and planning delays. As the decommissioning problem is challenging to solve, a matheuristic approach based on the hybridisation of a heuristic approach and an exact method is also proposed to find near optimal solutions for a test set of problems. A set of computational experiments has been carried out to assess the proposed approach.

Distributed Probabilistic ATC Assessment by Optimality Conditions Decomposition and LHS Considering Intermittent Wind Power Generation

This paper investigates a probabilistic assessment of available transfer capability (ATC) by optimality condition decomposition (OCD) techniques and latin hypercube sampling (LHS) method in power systems with penetration of wind energy resources. First, the ATC assessment is mathematically formulated as a non-linear optimal power flow problem. Then, an iterative decomposition-coordination methodology based on OCD techniques is conducted for distributed assessment of the ATC. In order to estimate the probability density function and empirical cumulative density function under wind power fluctuations, LHS method is utilized for obtaining samples of the integrated wind power sources. Next, wind power samples are appended into the proposed decomposition-coordination approach to provide a fast LHS-based Monte Carlo (MC) simulation of the ATC at the current system state. In order to provide a preliminary approximation of the range variation of the ATC before executing the MC technique, OCD-based distributed computations of the average-case, best-case, and worst-case scenarios subjected to wind power variations are developed by following three approaches: first, assuming that the average-case of the ATC occurs at the mean power output of integrated wind farms, second, adding box-constraints related to the power output of each wind farm in order to estimate the best-case scenario, and third, developing an iterative sensitivity-based scheme to estimate the worst-case scenario. Numerical experiments in the standard IEEE 118-bus system demonstrate the correctness of the proposed distributed probabilistic ATC assessment.