scholarly journals A Framework for Autonomous Wind Farms: Wind Direction Consensus

2018 ◽  
Author(s):  
Jennifer Annoni ◽  
Christopher Bay ◽  
Kathryn Johnson ◽  
Emiliano Dall'Anese ◽  
Eliot Quon ◽  
...  
Keyword(s):  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Wind Farms ◽  
Robust Estimate ◽  
Local Conditions ◽  
Efficient Operation ◽  
Account Information ◽  
Data Acquistion ◽  
Wind Vane

Abstract. Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. In an autonomous wind farm, enabling cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving an autonomous wind farm is to develop algorithms that provide necessary information to ensure reliable, robust, and efficient operation of wind turbines in a wind plant using local sensor information that is already being collected, such as supervisory control and data acquistion (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for implementing an autonomous wind farm that incorporates information from local sensors in real time to better align turbines in a wind farm. Oftentimes, measurements made at an individual turbine are noisy and unreliable. By incorporating measurements from multiple nearby turbines, a more robust estimate of the wind direction can be obtained at an individual turbine. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction and could decrease dynamic yaw misalignment, decrease the amount of time a turbine spends yawing due to a more robust input to the yaw controller, and increase resiliency to faulty wind-vane measurements.

scholarly journals Wind direction estimation using SCADA data with consensus-based optimization

2019 ◽  
Vol 4 (2) ◽  
pp. 355-368 ◽  
Author(s):  
Jennifer Annoni ◽  
Christopher Bay ◽  
Kathryn Johnson ◽  
Emiliano Dall'Anese ◽  
Eliot Quon ◽  
...  
Keyword(s):  
Real Time ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Control Strategies ◽  
Reliable Estimate ◽  
Local Conditions ◽  
Efficient Operation ◽  
Wind Vane ◽  
Inflow Conditions

Abstract. Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. To enable cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving a more efficient wind farm is to develop algorithms that ensure reliable, robust, real-time, and efficient operation of wind turbines in a wind farm using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for developing a cooperative wind farm that incorporates information from nearby turbines in real time to better align turbines in a wind farm. SCADA data from multiple turbines can be used to make better estimates of the local inflow conditions at each individual turbine. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. The consensus-based approach presented in this paper uses information from nearby turbines to estimate wind direction in an iterative way rather than aggregating all the data in a wind farm at once. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction. This estimated wind direction signal has implications for potentially decreasing dynamic yaw misalignment, decreasing the amount of time a turbine spends yawing due to a more reliable input to the yaw controller, increasing resiliency to faulty wind-vane measurements, and increasing the potential for wind farm control strategies such as wake steering.

Optimization of Large Wind Farms Using a Genetic Algorithm

2012 ◽  
Author(s):  
Anshul Mittal ◽  
Lafayette K. Taylor
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Wind Farms ◽  
Unit Power ◽  
Wake Model ◽  
Variable Wind ◽  
Large Wind

Optimizing the placement of the wind turbines in a wind farm to achieve optimal performance is an active area of research, with numerous research studies being published every year. Typically, the area available for the wind farm is divided into cells (a cell may/may not contain a wind turbine) and an optimization algorithm is used. In this study, the effect of the cell size on the optimal layout is being investigated by reducing it from five rotor diameter (previous studies) to 1/40 rotor diameter (present study). A code is developed for optimizing the placement of wind turbines in large wind farms. The objective is to minimize the cost per unit power produced from the wind farm. A genetic algorithm is employed for the optimization. The velocity deficits in the wake of the wind turbines are estimated using a simple wake model. The code is verified using the results from the previous studies. Results are obtained for three different wind regimes: (1) Constant wind speed and fixed wind direction, (2) constant wind speed and variable wind direction, and (3) variable wind speed and variable wind direction. Cost per unit power is reduced by 11.7% for Case 1, 11.8% for Case 2, and 15.9% for Case 3 for results obtained in this study. The advantages/benefits of a refined grid spacing of 1/40 rotor diameter (1 m) are evident and are discussed. To get an understanding of the sensitivity of the power produced to the wake model, optimized layout is obtained for the Case 1 using a different wake model.

scholarly journals The Application of Water Cycle Optimization Algorithm for Optimal Placement of Wind Turbines in Wind Farms

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4335 ◽  
Author(s):  
Hegazy Rezk ◽  
Ahmed Fathy ◽  
Ahmed A. Zaki Diab ◽  
Mujahed Al-Dhaifallah
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Water Cycle ◽  
Reliability And Validity ◽  
Wind Farms ◽  
Optimal Placement ◽  
Grey Wolf Optimizer ◽  
Variable Wind

Wind farms (WFs) include an enormous number of wind turbines (WTs) in order to achieve high capacity. The interaction among WTs reduces the extracted amount of wind energy because wind speed decreases in the wake region. The optimal placement of WTs within a WF is therefore vital for achieving high performance. This permits as many WTs as possible to be installed inside a narrow region. In this work, the water cycle algorithm (WCA), a recently developed optimizer, was employed to identify the optimal distribution of WTs. Minimization of the total cost per kilowatt was the objective of the optimization process. Two different cases were considered: the first assumed constant wind speed with variable wind direction, while the second applied variable wind speed with variable wind direction. The results obtained through the WCA optimizer were compared with other algorithms, namely, salp swarm algorithm (SSA), satin bowerbird optimization (SBO), grey wolf optimizer (GWO), and differential evolution (DE), as well as other reported works. WCA gave the best solution compared to other reported and programmed algorithms, thus confirming the reliability and validity of WCA in optimally configuring turbines in a wind farm for both the studied cases.

scholarly journals Wealth of Wind and Visitors: Tourist Industry Attitudes towards Wind Energy Development in Iceland

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 693
Author(s):  
Anna Dóra Sæþórsdóttir ◽  
Margrét Wendt ◽  
Edita Tverijonaite
Keyword(s):  
Land Use ◽  
Wind Energy ◽  
Wind Turbines ◽  
Wind Farm ◽  
Service Providers ◽  
Wind Farms ◽  
Energy Development ◽  
Tourism Industry ◽  
Land Use Conflicts ◽  
Wind Energy Development

The interest in harnessing wind energy keeps increasing globally. Iceland is considering building its first wind farms, but its landscape and nature are not only a resource for renewable energy production; they are also the main attraction for tourists. As wind turbines affect how the landscape is perceived and experienced, it is foreseeable that the construction of wind farms in Iceland will create land use conflicts between the energy sector and the tourism industry. This study sheds light on the impacts of wind farms on nature-based tourism as perceived by the tourism industry. Based on 47 semi-structured interviews with tourism service providers, it revealed that the impacts were perceived as mostly negative, since wind farms decrease the quality of the natural landscape. Furthermore, the study identified that the tourism industry considered the following as key factors for selecting suitable wind farm sites: the visibility of wind turbines, the number of tourists and tourist attractions in the area, the area’s degree of naturalness and the local need for energy. The research highlights the importance of analysing the various stakeholders’ opinions with the aim of mitigating land use conflicts and socioeconomic issues related to wind energy development.

Operation and approximation based on the history of failure modes recorded by SCADA system of Amougdoul Moroccan wind farm using FMECA maintenance model

2021 ◽  
pp. 0309524X2199245
Author(s):  
Kawtar Lamhour ◽  
Abdeslam Tizliouine
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Failure Modes ◽  
Wind Farms ◽  
Scada System ◽  
History Of ◽  
Criticality Analysis ◽  
Maintenance Model ◽  
Reliability And Availability ◽  
Critical Subsystems

The wind industry is trying to find tools to accurately predict and know the reliability and availability of newly installed wind turbines. Failure modes, effects and criticality analysis (FMECA) is a technique used to determine critical subsystems, causes and consequences of wind turbines. FMECA has been widely used by manufacturers of wind turbine assemblies to analyze, evaluate and prioritize potential/known failure modes. However, its actual implementation in wind farms has some limitations. This paper aims to determine the most critical subsystems, causes and consequences of the wind turbines of the Moroccan wind farm of Amougdoul during the years 2010–2019 by applying the maintenance model (FMECA), which is an analysis of failure modes, effects and criticality based on a history of failure modes occurred by the SCADA system and proposing solutions and recommendations.

A simulation-driven online scheduling algorithm for the maintenance and operation of wind farm systems

SIMULATION ◽  
2021 ◽  
pp. 003754972110286
Author(s):  
Eduardo Pérez
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Computational Study ◽  
Scheduling Algorithm ◽  
Discrete Event ◽  
Wind Farms ◽  
Online Scheduling ◽  
Maintenance Scheduling ◽  
Lead Times ◽  
System Specification

Wind turbines experience stochastic loading due to seasonal variations in wind speed and direction. These harsh operational conditions lead to failures of wind turbines, which are difficult to predict. Consequently, it is challenging to schedule maintenance actions that will avoid failures. In this article, a simulation-driven online maintenance scheduling algorithm for wind farm operational planning is derived. Online scheduling is a suitable framework for this problem since it integrates data that evolve over time into the maintenance scheduling decisions. The computational study presented in this article compares the performance of the simulation-driven online scheduling algorithm against two benchmark algorithms commonly used in practice: scheduled maintenance and condition-based monitoring maintenance. An existing discrete event system specification simulation model was used to test and study the benefits of the proposed algorithm. The computational study demonstrates the importance of avoiding over-simplistic assumptions when making maintenance decisions for wind farms. For instance, most literature assumes maintenance lead times are constant. The computational results show that allowing lead times to be adjusted in an online fashion improves the performance of wind farm operations in terms of the number of turbine failures, availability capacity, and power generation.

scholarly journals Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4291
Author(s):  
Paxis Marques João Roque ◽  
Shyama Pada Chowdhury ◽  
Zhongjie Huan
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Wind Farms ◽  
Operating Conditions ◽  
Wake Effect ◽  
Wind Generators ◽  
Wind Potential

District of Namaacha in Maputo Province of Mozambique presents a high wind potential, with an average wind speed of around 7.5 m/s and huge open fields that are favourable to the installation of wind farms. However, in order to make better use of the wind potential, it is necessary to evaluate the operating conditions of the turbines and guide the independent power producers (IPPs) on how to efficiently use wind power. The investigation of the wind farm operating conditions is justified by the fact that the implementation of wind power systems is quite expensive, and therefore, it is imperative to find alternatives to reduce power losses and improve energy production. Taking into account the power needs in Mozambique, this project applied hybrid optimisation of multiple energy resources (HOMER) to size the capacity of the wind farm and the number of turbines that guarantee an adequate supply of power. Moreover, considering the topographic conditions of the site and the operational parameters of the turbines, the system advisor model (SAM) was applied to evaluate the performance of the Vestas V82-1.65 horizontal axis turbines and the system’s power output as a result of the wake effect. For any wind farm, it is evident that wind turbines’ wake effects significantly reduce the performance of wind farms. The paper seeks to design and examine the proper layout for practical placements of wind generators. Firstly, a survey on the Namaacha’s electricity demand was carried out in order to obtain the district’s daily load profile required to size the wind farm’s capacity. Secondly, with the previous knowledge that the operation of wind farms is affected by wake losses, different wake effect models applied by SAM were examined and the Eddy–Viscosity model was selected to perform the analysis. Three distinct layouts result from SAM optimisation, and the best one is recommended for wind turbines installation for maximising wind to energy generation. Although it is understood that the wake effect occurs on any wind farm, it is observed that wake losses can be minimised through the proper design of the wind generators’ placement layout. Therefore, any wind farm project should, from its layout, examine the optimal wind farm arrangement, which will depend on the wind speed, wind direction, turbine hub height, and other topographical characteristics of the area. In that context, considering the topographic and climate features of Mozambique, the study brings novelty in the way wind farms should be placed in the district and wake losses minimised. The study is based on a real assumption that the project can be implemented in the district, and thus, considering the wind farm’s capacity, the district’s energy needs could be met. The optimal transversal and longitudinal distances between turbines recommended are 8Do and 10Do, respectively, arranged according to layout 1, with wake losses of about 1.7%, land utilisation of about 6.46 Km2, and power output estimated at 71.844 GWh per year.

Synchronization of wind farm power supply into the utility grid

2021 ◽  
Vol 6 ◽  
pp. 20-25
Author(s):  
Alexey Bogatyrev
Keyword(s):  
Wind Turbines ◽  
Power Supply ◽  
Distribution System ◽  
Wind Farm ◽  
Wind Farms ◽  
Time Signal ◽  
Electricity Distribution ◽  
Utility Grid ◽  
The Moment ◽  
External Time

Wind turbines and wind farms can be connected to the major electricity distribution system. This paper presents the research results on synchronization of wind farm power supply into the utility grid depending on parameters of the grid at the moment. Measurement time gets synchronized with the external time signal delivered from a navigating system like GLONASS. This can help eliminate antiphase operation of individual wind turbines. Connection diagrams and the whole methodology presented in this paper aim to make wind farm power supply into the grid more effective and loss-eliminating.

Wind farm cooperative control for optimal power generation

2018 ◽  
Vol 42 (6) ◽  
pp. 547-560 ◽  
Author(s):  
Fa Wang ◽  
Mario Garcia-Sanz
Keyword(s):  
Power Generation ◽  
Wind Turbines ◽  
Cooperative Control ◽  
Wind Farm ◽  
Wind Farms ◽  
Practical Method ◽  
Individual Case ◽  
Power Coefficient ◽  
The Individual ◽  
Large Wind

The power generation of a wind farm depends on the efficiency of the individual wind turbines of the farm. In large wind farms, wind turbines usually affect each other aerodynamically at some specific wind directions. Previous studies suggest that a way to maximize the power generation of these wind farms is to reduce the generation of the first rows wind turbines to allow the next rows to generate more power (coordinated case). Yet, other studies indicate that the maximum generation of the wind farm is reached when every wind turbine works at its individual maximum power coefficient CPmax (individual case). This article studies this paradigm and proposes a practical method to evaluate when the wind farm needs to be controlled according to the individual or the coordinated case. The discussion is based on basic principles, numerical computations, and wind tunnel experiments.

Mooring Analysis of a Dual-Spar Floating Wind Farm with a Shared Line

2021 ◽  
pp. 1-37
Author(s):  
Guodong Liang ◽  
Zhiyu Jiang ◽  
Karl O. Merz
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Sensitivity Study ◽  
Design Stage ◽  
Mooring Lines ◽  
Natural Period ◽  
Preliminary Design Stage ◽  
Decay Tests ◽  
Line Design

Abstract Wind farms with shared mooring lines have the potential to reduce mooring costs. However, such wind farms may encounter complex system dynamics because adjacent wind turbines are coupled. This paper presents an analysis of the shared mooring system with a focus on the system natural periods. We first apply Irvine's method to model both the shared line and the two-segment single lines. The response surface method is proposed to replace iterations of the catenary equations of the single lines, and a realistic single line design is presented for OC3 Hywind. Then, system linearization and eigenvalue analysis are performed for the wind farm consisting of two spar floating wind turbines, one shared line, and four single lines. The obtained natural periods and natural modes are verified by numerical free decay tests. Finally, a sensitivity study is carried out to investigate the influence of mooring properties. It is found that the shared line has a significant influence on the natural periods in the surge and sway modes. The natural periods in the surge and sway modes are also most sensitive to the mooring property variations. Two sway eigenmodes are identified, and the lower sway natural period varies between 23 s and 88 s in the sensitivity study. The present analysis method can be used to identify critical natural periods at the preliminary design stage of shared mooring systems.

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