Positioning Of Wind Turbines Research Articles

Optimal placement of wind turbine in distribution grid to minimize energy loss considering power generation probability

This research proposes an algorithm to determine the optimal position of wind turbines in a distribution grid for minimizing its annual energy loss. In this paper, the probability distribution of power generation at each wind turbine node is considered and the nonsimultaneous occurrence of the power generation at wind turbine nodes is also interested. Moreover, we also consider the wind energy exploitability at each node in the grid. This algorithm is coded in MATLAB environment and it is verified via a sample IEEE 33 bus distribution grid with a sample data of power generation probability. The verifying results indicated that the optimal number of wind turbines at each node to obtain a minimal annual energy loss. Results also indicated that the node voltage is varied because of the change of power generation; however, the voltage at all nodes is in normal operation range and its median value is between about 1.01 pu to 1.05 pu. The power factor of wind turbines has an impact on the optimal number of wind turbines at nodes and the efficiency of wind turbine installation.

Optimization of the Offshore Wind Turbines Layout Using Cuckoo Search Algorithm

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.

Optimal Positioning of Small Wind Turbines Into a Building Using On-Site Measurements and Computational Fluid Dynamic Simulation

Abstract Renewable energy solutions are essential for addressing several pressing issues, including climate change, the fossil fuels supply chain fragility and fuel price fluctuations. One promising technological solution is rooftop-mounted turbines into buildings. This study presents an evaluation of the potential for wind energy utilization on the rooftop of a 29 m tall building. The primary objective of this research is to develop a methodology that can effectively investigate the integration of small wind turbines (SWTs) into urban buildings, intending to promote energy sufficiency in urban areas. A robust framework has been developed that consists of seven steps. These steps include site selection, evaluating urban wind energy with computational fluid dynamics (CFD) simulation and on-site measurements, selecting an appropriate SWT, estimating the annual energy production (AEP), conducting an evaluation of the environmental impact, resilience, and economic analysis, and finally, installing the system. This straightforward yet reliable framework provides a comprehensive approach to assessing the viability of wind energy utilization in urban areas. The findings revealed that the most suitable location for installation had an estimated AEP of around 1030 kWh, leading to a reduction in emissions of 0.64 tCO2/y. Additionally, it was observed that the building's geometry and orientation significantly affected the wind flow, causing a substantial decrease in wind speed downstream. Selecting optimal sites and considering wind patterns are essential for maximizing energy generation in wind energy projects.

Optimization of offshore wind farm inspection paths based on K-means-GA.

As global demand for offshore wind energy continues to rise, the imperative to enhance the profitability of wind power projects and reduce their operational costs becomes increasingly urgent. This study proposes an innovative approach to optimize the inspection routes of offshore wind farms, which integrates the K-means clustering algorithm and genetic algorithm (GA). In this paper, the inspection route planning problem is formulated as a multiple traveling salesman problem (mTSP), and the advantages of the K-means clustering algorithm in distance similarity are utilized to effectively group the positions of wind turbines, thereby optimizing the inspection schedule for vessels. Subsequently, by harnessing the powerful optimization capability and robustness of genetic algorithms, further refinement is conducted to search for the optimal inspection routes, aiming to achieve cost reduction objectives. The results of simulation experiments demonstrate the effectiveness of this integrated approach. Compared to traditional genetic algorithms, the inspection route length has been significantly reduced, from 93 kilometers to 79.36 kilometers. Simultaneously, operational costs have also experienced a notable decrease, dropping from 141,500 Chinese Yuan to 125,600 Chinese Yuan.

Identifying wind turbines from multiresolution and multibackground remote sensing imagery

The wind energy industry has expanded in recent years. Promotion of the Sustainable Development Goals (SDGs) is expected to further increase the scale of the wind energy industry. Determining the location and quantity of wind turbines is crucial for monitoring the development status of the wind energy industry and evaluating wind energy production. In this study, we propose a method for simultaneously detecting and positioning wind turbines in remote sensing images, namely, Wind Turbine YOLO (WT-YOLO), based on the You Only Look Once version 5 model (YOLOv5). The wind turbine hub, base, and shadow hub are treated as key points in the proposed method. Regression terms are incorporated into the head of the YOLOv5 basic framework to predict the location of these three key points. The base point is utilized to determine the exact position of the wind turbine. A multibackground and multiresolution wind-turbine image dataset is constructed by sampling high-resolution images from Google Earth. The WT-YOLO method outperforms existing methods in both wind turbine detection and positioning on different types of land cover backgrounds and multiresolution images of the constructed dataset. In the spatial resolution range of 0.6 m to 5.4 m, WT-YOLO exhibits enhanced wind-turbine detection, where the average precision (AP) is 5.92 % to 15.43 % higher than that of existing wind-turbine detection methods. Wind-turbine positioning by WT-YOLO has a mean distance error (MDE) that is 16.06 m to 21.59 m lower than that of existing wind-turbine positioning methods. A comparative analysis showed that the shadow and the three key points are effective features for wind-turbine detection. The proposed WT-YOLO model can support detection, positioning and counting for wind turbines worldwide.

Constrained optimization of offshore wind turbine positions under uncertain wind conditions with correlated data

Offshore wind power production is subject to substantial uncertainty due to variability in wind conditions. Planning of new wind parks should take these uncertainties into account by means of stochastic modeling and uncertainty quantification. Wind speed and wind direction exhibit dependence that needs to be properly modeled to yield reliable uncertainty estimates. In particular, the dependence is strong when data are averaged over a short time-horizon, e.g., on a monthly rather than an annual basis. In this work we introduce a stochastic model for wind speed and wind direction using Rosenblatt transformation and an empirical model. This allows efficient numerical quadrature that replaces thousands of Monte Carlos samples by a few tens of model evaluations at quadrature points in parametric space. A robust design formulation for maximizing power production honoring dependent data is proposed and demonstrated on data from the North Sea site Sørlige Nordsjø II.

Optimization of the Number, Hub Height and Layout of Offshore Wind Turbines

In order to make full use of the potential of wind resources in a specific offshore area, this paper proposes a new method to simultaneously optimize the number, hub height and layout of a wind farm. The wind farm is subdivided by grids, and the intersection points are set as the potential wind turbine positions. The method adopts a genetic algorithm and encodes wind farm parameters into chromosomes in binary form. The length of chromosomes is decided by the number of potential positions and the hub heights to be selected. The optimization process includes selection, crossover, and mutation, while the efficiency of wind farm is set as the optimization objective. The proposed method is validated by three benchmark cases. It has proven to be effective in deciding the number of turbines and improving the efficiency of the wind farm. Another advantage of the proposed method is that it can be widely applied to wind farms of any shape. A case study applying the new method to an irregularly shaped wind farm in Hong Kong is demonstrated. By comparing the results with the original regularly shaped wind farm, the new method can improve power generation by 6.28%. Therefore, the proposed model is a supportive tool for designing the best number, hub heights and positions of wind turbines.

Stochastic gradient descent for wind farm optimization

Abstract. It is important to optimize wind turbine positions to mitigate potential wake losses. To perform this optimization, atmospheric conditions, such as the inflow speed and direction, are assigned probability distributions according to measured data, which are propagated through engineering wake models to estimate the annual energy production (AEP). This study presents stochastic gradient descent (SGD) for wind farm optimization, which is an approach that estimates the gradient of the AEP using Monte Carlo simulation, allowing for the consideration of an arbitrarily large number of atmospheric conditions. SGD is demonstrated using wind farms with square and circular boundaries, considering cases with 100, 144, 225, and 325 turbines, and the results are compared to a deterministic optimization approach. It is shown that SGD finds a larger optimal AEP in substantially less time than the deterministic counterpart as the number of wind turbines is increased.

Wind farm layout and hub height optimization with a novel wake model

This paper comprehensively investigates the impact of wind turbine layout and hub height on power generation of wind farm. Firstly, an engineering three-dimensional (3-D) wind turbine wake model is improved by the artificial neural network (ANN) technology. The novel 3-D ANN wake model reaches more than 99% accuracy of the original wake model. It can save about 80% of the computational time when predicting the downstream wind speed. Secondly, the influence of wind turbine hub height and position on the equivalent wind speed (EWS) and power is deeply studied. Specially, when reducing the hub height of the downstream wind turbine, both the wake impact from upstream turbines and EWS will decrease, so the overall influence should be assessed according to the specific situation. Finally, the problem of wind farm layout and height optimization is investigated. According to this study, simultaneously optimizing these two factors can obtain a better result than optimizing each factor individually. If economic factor is additionally considered, the optimized hub height and power output results will be quite different. Therefore, considering more factors is important to obtain an appropriate wind farm layout.

Offshore Wind Farm Layout Optimisation Considering Wake Effect and Power Losses

The last two decades have witnessed a new paradigm in terms of electrical energy production. The production of electricity from renewable sources has come to play a leading role, thus allowing us not only to face the global increase in energy consumption, but also to achieve the objectives of decarbonising the economies of several countries. In this scenario, where onshore wind energy is practically exhausted, several countries are betting on constructing offshore wind farms. Since all the costs involved are higher when compared to onshore, optimising the efficiency of this type of infrastructure as much as possible is essential. The main aim of this paper was to develop an optimisation model to find the best wind turbine locations for offshore wind farms and to obtain the wind farm layout to maximise the profit, avoiding cable crossings, taking into account the wake effect and power losses. The ideal positioning of wind turbines is important for maximising the production of electrical energy. Furthermore, a techno-economic analysis was performed to calculate the main economic indicators, namely the net present value, the internal rate of return, and the payback period, to support the decision-making. The results showed that the developed model found the best solution that maximised the profits of the wind farm during its lifetime. It also showed that the location of the offshore substation played a key role in achieving these goals.

Numerical investigation of aerodynamic responses and wake characteristics of a floating offshore wind turbine under atmospheric boundary layer inflows

The atmospheric boundary layer (ABL) inflow has significant effects on behaviors of floating offshore wind turbine (FOWT), especially for large-size wind turbine. In this study, numerical investigation of aerodynamics and wakes of a semi-submersible FOWT under ABL inflow is performed. The quasi-equilibrium ABL wind field is generated by large eddy simulations (LES) with sufficient simulation duration. The FOWT wakes are modeled by incorporating LES and actuator line model (ALM) in the computational fluid dynamics (CFD) framework, and the FOWT dynamic responses are simulated by FAST code. A two-way coupling procedure is employed, in which the wind velocity around wind turbine sampled in CFD framework and the wind turbine's body forces and positions solved by FAST code are delivered to each other. The simulation case of a bottom-fixed wind turbine is performed to provide some comparable data. It is revealed that the power variation of FOWT is dominated by atmospheric turbulence, more than platform motions. The slightly enhanced out-of-plane shear force and bending moment of FOWT are caused by platform motions. Owing to the entrance of ambient atmospheric flow into wind turbine wakes, significant deflection of wakes is visualized. In addition, the wake center of FOWT is far away from hub height level due to pitch motion of platform, which is a potential benefit factor for downstream wind turbines. The spatiotemporal characteristics of turbulence intensity in FOWT wakes are complex, and the discrepancies of wakes between floating and bottom-fixed scenarios are not significant.

Vertical Axis Wind Turbine Layout Optimization

Vertical Axis Wind Turbines (VAWTs) are not mature enough yet for offshore wind farms, but they offer benefits compared to conventional Horizontal Axis Wind Turbines (HAWTs). Higher power densities, reduced wakes, lower center of mass, and different power and thrust curves make VAWTs an interesting option to complement existing wind farms. The optimization of wind farm layouts—finding the optimal positions of wind turbines in a park—has proven crucial to extract more energy from conventional wind farms. In this study, we build an optimizer for VAWTs that can consider arbitrarily shaped layouts as well as obstacles in the area. We adapt a recent model for the wakes of VAWTs considering a Troposkien design. We can then model and optimize a large VAWT park in a real wind scenario and assess for the first time its performance operating Troposkien VAWTs. In addition, we present a novel model for wind farm optimization that considers the clockwise and counterclockwise rotation of turbines. This optimization exploits the asymmetric wakes of VAWTs, thus increasing the total energy production. We benchmark our optimization on realistic instances and compare VAWTs and HAWTs wind farm layouts, showing that VAWTs can achieve higher density and power production than HAWTs in the same area. Finally, the wake loss reduction is compared to the literature.

Self-reconfiguration simulations of turbines to reduce uneven farm degradation

This study explored the concept of a floating offshore wind farm (FOWF) that self-reconfigures wind turbine (WT) positions. The self-reconfiguration (SR) mechanism moves degraded turbines to different farm positions to delay failures occurring and reduce power loss. A 40-turbine agent-based simulation was created utilising wind and turbine performance data. For the first time, the SR mechanism was designed and optimised with principles of self-engineering complexity. The paper demonstrates the effectiveness of the SR mechanism through an agent-based simulation approach. The optimised SR was used in FOWF simulations of 50 years of operation; SR balanced fatigue across the FOWF and led to an increased income of £5–40M at years 27–33 of operation; however, outside of these years, there is no net positive income, and by year 50 SR has cost the FOWF £4–7M more in movement costs. Lastly, simulations with repair and maintenance restricted to 10 months or less were conducted. SR delayed turbine failures, so they occurred in months when repair could be conducted. The SR reduced power loss and increased net income by up to £20M, indicating that SR could be useful when repair and maintenance times are limited. In the absence of significant operational data, a qualitative validation with experts confirmed the approach and the validity of the simulation model for a range of FOWF scenarios.

Furthering knowledge on the flow pattern around high-rise buildings: LES investigation of the wind energy potential

To understand the favourable locations for maximum yield of urban wind energy, understanding the flow pattern on the roof region of high-rise buildings is crucial. However, very few studies in the literature report on the wind energy resource using high-fidelity methods, which combined with the lack of wind tunnel investigations, explains the failure of urban wind energy as a viable contributor to the renewable energy mix.This work aims at proposing a strategy for the reliable evaluation of the wind energy resource by building on validated Large Eddy Simulation and performance maps based on the wind power density and the turbulence intensity above the roof of a prismatic 1:3 square building. Analyses include the effect of flat and decked roof shapes in two wind directions. Results around the facade reveal zones of accelerated flow close to separation edges, while above the roof, a higher potential is noticeable at 45°, than at 0°. Decking of the roof shows insensitivity to the change in wind direction. The tested methodology can be extended to other configurations to predict the wind energy potential as well as used to indicate the positioning of wind turbines in the built environment.

Layout Optimization of a Modular Floating Wind Farm Based on the Full-Field Wake Model

By optimizing the positions of wind turbines in a wind farm, the power loss caused by wake effects can be reduced maximally. A new methodology of layout optimization is proposed utilizing a full-field wake model that integrates the near-field and far-field wake models after modifications, and a random search (RS) algorithm improved with a scale factor for acceleration in high-density layouts. The methodology is applied to a floating wind farm composed of modular platforms, which have a novel configuration and the ability to face toward the wind direction. The applicability and efficiency of the methodology and the improved RS algorithm are validated. The power production of optimized layouts shows a flat crest with an increased number of wind turbines. There is a layout with maximal output power in the wind farm. The real optimal layout should be determined in consideration of both output power and cost. Two sizes of platforms with different number of modules are compared in the application. The wind farm with smaller platforms produces more power. For comparison, a pattern search (PS) algorithm is also implemented in the application. The improved RS algorithm shows outperformance compared with the original RS and the PS algorithm.

The Sparrow Search Algorithm for Optimum Position of Wind Turbine on a Wind Farm

With more Renewable Energy (RE) integration in recent years, Wind farms (WFs) seem to produce more energy from Wind Turbines (WTs). Most WTs in WFs are designed to face a predetermined wind direction; this means that WTs can generate less electricity than they need due to the intermittent nature of the wind. Due to the non-linear nature of wind energy, optimization techniques are critical for successfully building a wind farm. This process involves performing layout optimization techniques using soft computing. WFs have a construction configuration with multiple turbines situated near together in a restricted terrain, contributing to higher energy losses due to the wake effects. Therefore, WTs on a WF to enhance the generated energy while meeting all constraints are pretty restrictive and complicated. We utilized the newly developed Sparrow Search Algorithm (SSA) to determine the most effective technique for the optimal positioning of WTs in WF. We can obtain the high efficiency of the WTs at the lowest possible level of turbine output. This article examined two case studies: the first one is a Constant Wind Speed (CWS) with Variable Wind Direction (VWD); the second one is a Variable Wind Speed (VWS) with Variable Wind Direction (VWD). It was determined how well the proposed method performed compared to the bulk of prior research that dealt with the same problem. Consequently, SSA is an effective technique for determining the WT position allocation problem to achieve the optimum position.

Strategic Environmental Assessment in the Application of Preventive Protection for Wind Farm Noise—Case Study: Maestrale Ring Wind Farm

Determining the spatial position of wind turbines is the initial and most important phase in the development of a wind farm project. In this sensitive phase, all potential problems that may arise in the later stages of project development should be prevented by means of spatial and urban planning instruments. This makes it possible to achieve maximum use of the potential of wind in a particular space and, thus, fulfil the technical and economic requirements of the project while respecting the goals of environmental protection in that same area, through preventive protection. Therefore, it is essential, even at the earliest planning and development stage of a wind farm project, for the requirements that are important for optimal spatial solutions to be balanced. In this process, strategic environmental assessment (SEA) is a support to the planning process and an invaluable instrument for finding optimal spatial solutions for the possible key spatial impacts of wind power with regard to noise, shadow flicker, ornithofauna and chiropterofauna. The weakness of SEA can be seen in its predominant application of expert qualitative methods that bring with them subjectivity, since they depend on expert knowledge and skills. This paper presents the aspect of noise impact assessment and its inclusion in the SEA for the Maestrale Ring wind farm in Serbia. The results of the research indicate how it is possible to achieve the principle of objectivity in the process of multicriteria expert evaluation by including the results of a partial impact assessment of the noise from wind farms, using results obtained from software modeling of the spatial dispersion of wind turbine noise in the SoundPlan 8.1 software package in the SEA process. These quantitative results predicting the noise level were used in a semi-quantitative method of multicriteria evaluation in the SEA through the definition of criteria to determine the ranking of impacts, which is elaborated in the paper. The results also show the significant of the contribution of applying a methodological approach based on a combination of qualitative and quantitative evaluation methods in SEA. These methods positively affect the application of the principle of preventive protection through the optimal selection of the number and position of wind turbines on one hand and the objectivity of drawing conclusions based on which strategic decisions are made in the final phase of the SEA process, on the other.

Wind turbine wake control strategies: A review and concept proposal

A wind farm’s overall power production is significantly less than its nominal power, defined as the sum of the wind turbines’ rated outputs. The primary cause for this significant loss is the exposure of downstream wind turbines to the aerodynamic wake of their upstream counterparts. Wind farm layout optimization aims at minimizing such adverse effects by finding optimal wind turbine positions that are less exposed to the upstream wake. Over the past few years, researchers have extended their fight against adverse wake effects beyond the layout optimization, i.e., the design process, onto the control and operation process. Several active wake control strategies have been proposed and studied to decrease the power loss of downstream wind turbines by steering or weakening the upstream wakes. First, the article reviews these strategies, including yaw control, pitch control, torque control, tilt control, and finally, cone angle control. Then, it proposes a novel wake steering technique where passive stationary vanes redirect the upstream wake. The authors conducted scaled wind tunnel experiments to investigate the performance of the proposed concept. Using a 3D-printed wake deflector between two in-line turbines increased the downstream turbine’s power production by more than 15%, which is significant compared to the existing wake control strategies. The article concludes by comparing the proposed technology’s effectiveness against existing wake control techniques.

Investigation of main bearing operating conditions in a three-Point mount wind turbine drivetrain

Wear-related failures of spherical roller bearings in the main bearing position of three-point mount wind turbines have been higher than expected and can contribute to higher than anticipated operation-and-maintenance costs. In this paper, the operational conditions of such a main bearing—including measured axial displacement and velocity subject to the estimated axial loads—are described for an instrumented, commercial wind turbine. The field measurements suggest a maximum axial speed between the bearing rings that is less than 2 millimeters per second. It is estimated that the axial speed between the ring and rollers is approximately 25% of this value. When compared to a speed of rolling from approximately 200–352 millimeters per second, the measured axial sliding is therefore significantly less than 1% of the speed of rolling. Previous numerical studies of lubricant film formation in rolling contacts have shown that the effect of axial sliding starts to be noticeable only when this ratio exceeds 10%; therefore, the axial velocity represents only a small disturbance to the nominal pure rolling case, and the influence on oil film building can be neglected. A simple analytic model of the main bearing motion was also developed and demonstrated similar displacement and velocity characteristics.

Seismic performance analysis of a wind turbine tower subjected to earthquake and ice actions.

Sea ice is one of the main loads acting on a wind turbine tower in areas prone to icing, and this threatens safe working life of the wind turbine tower. In our study, a simplified calculated model of ice, wind turbine tower, and water dynamic interaction under earthquake action was proposed, which could avoid to solve a large number of nonlinear equations. Then, the seismic behaviour of the wind turbine tower with and without the influence of sea ice was investigated, and we found that the influence of the greater mass of the sea ice on the seismic response of a wind turbine tower should be considered when the wind turbine tower is designed in an area with thick ice. With the influence of the most unfavourable ice mass, the deformation and energy dissipation capacity of the wind turbine tower are decreased, and the wall thickness or stiffening rib thickness should be increased to improve the seismic performance and ductility of the wind turbine tower; the shear force and bending moment increased significantly on the wind turbine tower, and the shear force changes at the bottom of the wind turbine tower and position of action of the sea ice: attention should be paid to the wind turbine tower design at these positions. Finally, we conducted the shaking table test, and verified the rationality of our proposed simplified model.