Turbine Cluster Research Articles

Wake Effects in Lower Carbon Future Scenarios

In August 2022, the U.S. Congress passed the Inflation Reduction Act (IRA), which intended to accelerate U.S. decarbonization, clean energy manufacturing, and deployment of new power and end-use technologies. The National Renewable Energy Laboratory has examined possible scenarios for growth by 2050 resulting from the IRA and other emissions reduction drivers and defined several possible scenarios for large-scale wind deployment. These scenarios incorporate large clusters of turbines operating as wind farms grouped around existing or likely transmission lines which will result in wind farm wakes. Using a numerical weather prediction (NWP) model, we assess these wake effects in a domain in the U. S. Southern Great Plains for a representative year with four scenarios in order to validate the simulations, estimate the internal wake impact, and quantify the cluster wake effect. Herein, we present a validation of the ”no wind farm” scenario and quantify the internal waking effect for the ”ONE” wind farm scenario. Future work will use the “MID” scenario (more than 8000 turbines) and the “HI” scenario (more than 16,000 turbines) to quantify the effect of cluster wakes or inter-farm wakes on power production.

Can mesoscale models capture the effect from cluster wakes offshore?

Long wakes from offshore wind turbine clusters can extend tens of kilometers downstream, affecting the wind resource of a large area. Given the ability of mesoscale numerical weather prediction models to capture important atmospheric phenomena and mechanisms relevant to wake evolution, they are often used to simulate wakes behind large wind turbine clusters and their impact over a wider region. Yet, uncertainty persists regarding the accuracy of representing cluster wakes via mesoscale models and their wind turbine parameterizations. Here, we evaluate the accuracy of the Fitch wind farm parameterization in the Weather Research and Forecasting model in capturing cluster-wake effects using two different options to represent turbulent mixing in the planetary boundary layer. To this end, we compare operational data from an offshore wind farm in the North Sea that is fully or partially waked by an upstream array against high-resolution mesoscale simulations. In general, we find that mesoscale models accurately represent the effect of cluster wakes on front-row turbines of a downstream wind farm. However, the same models may not accurately capture cluster-wake effects on an entire downstream wind farm, due to misrepresenting internal-wake effects.

The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects

Abstract. The growth in the number and size of wind energy projects in the last decade has revealed structural limitations in the current approach adopted by the wind industry to assess potential wind farm sites. These limitations are the result of neglecting the mutual interaction of large wind farms and the thermally stratified atmospheric boundary layer. While currently available analytical models are sufficiently accurate to conduct site assessments for isolated rotors or small wind turbine clusters, the wind farm's interaction with the atmosphere cannot be neglected for large-size arrays. Specifically, the wind farm displaces the boundary layer vertically, triggering atmospheric gravity waves that induce large-scale horizontal pressure gradients. These perturbations in pressure alter the velocity field at the turbine locations, ultimately affecting global wind farm power production. The implication of such dynamics can also produce an extended blockage region upstream of the first turbines and a favorable pressure gradient inside the wind farm. In this paper, we present the multi-scale coupled (MSC) model, a novel approach that allows the simultaneous prediction of micro-scale effects occurring at the wind turbine scale, such as individual wake interactions and rotor induction, and meso-scale phenomena occurring at the wind farm scale and larger, such as atmospheric gravity waves. This is achieved by evaluating wake models on a spatially heterogeneous background velocity field obtained from a reduced-order meso-scale model. Verification of the MSC model is performed against two large-eddy simulations (LESs) with similar average inflow velocity profiles and a different capping inversion strength, so that two distinct interfacial gravity wave regimes are produced, i.e. subcritical and supercritical. Interfacial waves can produce high blockage in the first case, as they are allowed to propagate upstream. On the other hand, in the supercritical regime their propagation speed is less than their advection velocity, and upstream blockage is only operated by internal waves. The MSC model not only proves to successfully capture both local induction and global blockage effects in the two analyzed regimes, but also captures the interaction between the wind farm and gravity waves, underestimating wind farm power by about only 2 % compared with the LES results. Conversely, wake models alone cannot distinguish between differences in thermal stratification, even if combined with a local induction model. Specifically, they are affected by a first-row overprediction bias that leads to an overestimation of the wind farm power by 13 % to 20 % for the modeled regimes.

Wind power forecasting method of large-scale wind turbine clusters based on DBSCAN clustering and an enhanced hunter-prey optimization algorithm

As the large-scale grid connection of wind turbines poses challenges to the safe and stable operation of the power grid, it is necessary to forecast the power of wind turbine clusters. However, with the rapid increase in the installed capacity of wind turbines, simultaneously improving the accuracy and efficiency of large-scale wind-turbine cluster power prediction models has become a challenge. In this research, a novel hybrid power-forecasting model for wind-turbine clusters has been designed using density-based spatial clustering of applications with noise (DBSCAN) and an enhanced hunter-prey optimization algorithm (ENHPO). First, the Pearson correlation coefficient was used to select multiple variables that significantly affected the wind power. Second, multiple wind turbines are clustered into different groups using the DBSCAN clustering algorithm, and ENHPO is employed to optimize the DBSCAN parameters to strengthen the clustering performance. Finally, one wind turbine with a high correlation was selected as the representative wind turbine in each group, and power prediction was achieved using the multivariate long short-term memory. Simulation results of four datasets in different seasons show that, compared with other clustering methods, such as fuzzy C-means, balanced iterative reduction, and clustering using hierarchies, K-means, and density peak clustering, the prediction accuracy and prediction efficiency of the proposed hybrid prediction model are improved by 21.85% and 18.07%, respectively, on average. The experimental results effectively demonstrate that the designed model can enhance the accuracy and efficiency of power forecasting simultaneously.

Speeding up large-wind-farm layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout

Abstract. As the use of wind energy expands worldwide, the wind energy industry is considering building larger clusters of turbines. Existing computational methods to design and optimize the layout of wind farms are well suited for medium-sized plants; however, these approaches need to be improved to ensure efficient scaling to large wind farms. This work investigates strategies for covering this gap, focusing on gradient-based (GB) approaches. We investigated the main bottlenecks of the problem, including the computational time per iteration, multi-start for GB optimization, and the number of iterations to achieve convergence. The open-source tools PyWake and TOPFARM were used to carry out the numerical experiments. The results show algorithmic differentiation (AD) as an effective strategy for reducing the time per iteration. The speedup reached by AD scales linearly with the number of wind turbines, reaching 75 times for a wind farm with 500 wind turbines. However, memory requirements may make AD unfeasible on personal computers or for larger farms. Moreover, flow case parallelization was found to reduce the time per iteration, but the speedup remains roughly constant with the number of wind turbines. Therefore, top-level parallelization of each multi-start was found to be a more efficient approach for GB optimization. The handling of spacing constraints was found to dominate the iteration time for large wind farms. In this study, we ran the optimizations without spacing constraints and observed that all wind turbines were separated by at least 1.4 D. The number of iterations until convergence was found to scale linearly with the number of wind turbines by a factor of 2.3, but further investigation is necessary for generalizations. Furthermore, we have found that initializing the layouts using a heuristic approach called Smart-Start (SMAST) significantly reduced the number of multi-starts during GB optimization. Running only one optimization for a wind farm with 279 turbines initialized with SMAST resulted in a higher final annual energy production (AEP) than 5000 optimizations initialized with random layouts. Finally, estimates for the total time reduction were made assuming that the trends found in this work for the time per iteration, number of iterations, and number of multi-starts hold for larger wind farms. One optimization of a wind farm with 500 wind turbines combining SMAST, AD, and flow case parallelization and without spacing constraints takes 15.6 h, whereas 5000 optimizations with random initial layouts, finite differences, spacing constraints, and top-level parallelization are expected to take around 300 years.

Co-evaluation of power system frequency performance and operational reliability considering the frequency regulation capability of wind power

With the increasing proportion of renewable energy, the power system inertia decreases, and the operation uncertainty rises. It brings concerns about the system frequency and operational reliability. However, the impacts of the power system frequency performance on the reliability parameters of generation units have not been fully investigated. This paper studies the frequency performance and the operational reliability co-evaluation for power systems considering wind turbines. Firstly, a power system frequency regulation model is established considering the regulation capability of wind turbines. Then, the cluster of equivalent wind turbines is incorporated into the frequency regulation architecture of thermal power units, which accelerates the analysis of frequency performance. Then, the frequency performance of the power system with the participation of wind turbines under the operation uncertainty and the unit random faults is quantitatively analyzed. A frequency-dependent generator reliability parameter model is derived. Next, a multi-time scale co-evaluation framework is proposed to realize the co-evaluation of frequency performance and operational reliability. Case studies are carried out on the modified IEEE RTS-79 system and a provincial power system. Results show that compared with the existing research, the proposed method can obtain the frequency performance and reliability results efficiently.

Optimization Layout and Aerodynamic Performance Research on Double Nautilus Vertical-Axis Wind Turbine

The double vertical-axis wind turbine (VAWT) system serves as a high-performance design solution. The Nautilus wind turbine investigated in this research imitated the structure of a Nautilus shell and is a vertical-axis drag-type wind turbine that exhibits relatively low efficiency. Therefore, the improvement of its wind energy efficiency is of paramount importance. This paper utilizes Computational Fluid Dynamics (CFD) software, based on the Reynolds-averaged Navier–Stokes equations and dynamic meshing techniques, to conduct numerical investigations on the aerodynamic performance of the Nautilus wind turbine array layout. The effects of wind direction, spacing ratio, and rotation direction are individually studied, and the interpretations and explanations are provided based on flow field characteristics. The results show that when the wind direction is 90°, i.e., a transverse layout, the closer the spacing between the transverse turbines, the higher the average power coefficient of the entire wind turbine system, with little effect from the three rotation directions. The maximum average power coefficient reached 28.9% and the power gain factor (TPGF) reached 11.1%. The enhancement effect primarily originates from the wake interaction among neighboring turbines. The experimental results showed a deviation of 8.1% compared to the CFD simulation results, thus validating the accuracy of the numerical CFD modeling. Ultimately, several array layouts are proposed, based on the prevalent wind direction and spacing ratio research. The enhancement of the wind turbine array’s situation could significantly increase the average efficiency of the entire wind turbine cluster. Consequently, this study provides a reference for the practical application of biomimetic vertical-axis drag-type wind turbine systems in actual engineering.

Performance analysis of vertical-axis wind turbine clusters: Effect of inter-turbine spacing and turbine rotation

Wind energy has emerged as a viable alternative to fossil fuels, with vertical-axis wind turbines (VAWTs) gaining popularity due to their efficiency and adaptability. Combining the actuator line method (ALM) with large eddy simulation (LES) enables accurate performance evaluations, facilitating the design and optimization of wind turbines. The present study invokes ALM-based methodology to perform calculations for the VAWTs. The results of the LES simulations of the VAWTs have been extensively validated against the available experimental and numerical data. The study further explores a VAWT cluster of three turbines by investigating the influence of turbine spacing (in both inline and staggered configuration) on cluster performance. This study shows that the configuration with a streamwise separation (Xsep) of 0.34 D and a transverse separation (Ysep) of 2.5 D exhibits superior performance to other combinations owing to increased kinetic energy in the wake for the downstream turbines. Furthermore, we have presented the effect of varying the rotation direction (in combinations of Clockwise and Counterclockwise rotation) for the individual turbines in the 3-turbine cluster for the two configurations: inline (Xsep = 0 D, Ysep = 2.5 D) and staggered (Xsep = 0.34 D, Ysep = 2.5 D). Staggered counter-rotating turbine cases show reduced performance compared to co-rotating cases, specifically, the clockwise co-rotating (C-C-C) configuration. In the inline configuration, counter-rotating setups outperform co-rotating ones. Counter-rotation analysis reveals that reducing streamwise separation allows turbines to align in line without sacrificing performance, thereby increasing the power density of the turbine cluster.

Enhancing wind turbine site selection through a novel wake penalty criterion

In this study, a novel approach that incorporates the wake effect as a penalty criterion within the Analytical Hierarchy Process (AHP) method is proposed. The research introduces the wake penalty criterion for the first time, considering both existing and newly located turbines. The results demonstrate the impact of the wake effect and identify areas with varying wake penalties. A comprehensive suitability analysis is conducted using AHP and Geographic Information System (GIS) techniques, resulting in a suitability map for wind turbine site selection. The analysis considers seven criteria, including the novel wake penalty criterion. The suitability map reveals a distribution of suitability ranges, with 73.8% of the total area excluded due to various constraints. Additionally, a comparative analysis is performed by excluding the wake penalty criterion, highlighting the contrasting effects of wake on turbine placement. Case studies in the Karaburun and Çeşme regions of İzmir further illustrate the influence of wake on turbine clusters and their alignment with prevailing wind directions. The findings indicate that the inclusion of wake effects provides a more precise and realistic depiction of viable wind turbine site selection. This is evident in the reduction of 55.5% and 18.6% in the most suitable region (80–100% suitability) and highly suitable region (60–80% suitability), respectively.

Power prediction of a Savonius wind turbine cluster considering wind direction characteristics on three sites

The Savonius wind turbine cluster installation significantly impacts its power output in real wind fields. This study aims to investigate the effect of wind conditions on the power output of a Savonius wind turbine cluster and obtain the optimal installation orientation of the cluster in three selected sites. Two-dimension simulations are conducted to evaluate the cluster performance, and a calculation method based on power curves is proposed to evaluate its annual power output. Results showed that the wake effect induced by wind direction is the primary factor leading to the performance decrease of wind turbines. Optimal installation orientations of the cluster at the three selected sites were obtained using the proposed method, and their corresponding power outputs are 1.25, 1.27, and 1.17 times that of the isolated turbine. The wind direction with high wind energy exerts greater effects than that with high wind frequency in determining the optimal installation orientation. The actual wind resource characteristics should be comprehensively considered in determining the optimal installation orientation in practice. This study provides guidance to the cluster installation in real wind conditions and contributes to the advancement of urban wind energy solutions.

A turbines-module adapted to the marine site for tidal farms layout optimization

The ocean energy exploitation is arousing growing interest in the renewable energy sector. In theshort term, horizontal axis tidal turbines are the most promising technology due to the accumulatedknow-how in the field of wind energy. In order to maximize the performance of the devices in acluster, it is essential to optimize the layout. The marine environment offers different conditions thanatmospheric situations, in terms of confinement and turbulence intensity. Moreover, tidal currentsexhibit a highly predictable pattern in speed intensity and direction unlike the wind resource, whichhas a more random behaviour. Nonetheless, most of tidal sites are characterized by the inversionof flow where the two prevailing directions are not perfectly aligned and opposite, hence the anglebetween those directions should be a design variable. In this work we will consider as a case studythe site proposed in [1], where this angle is ±20°.For those sites with a flow inversion of almost 180°, the staggered configuration is preferable toavoid wakes interference as mentioned in [2]. Furthermore, many studies [3] had analysed positiveinteraction between neighbouring devices in a cluster, hence it is important to establish the optimalrelative position accounting for fluid dynamic positive effects, and not only negative aspects suchas wake interactions. For this reason, in this work we present a novel approach to determine thebest configuration of a cluster of few turbines, a ”module”, which will be the optimized ”buildingblock” for the whole farm. The procedure to be followed consist of two phases in which boththe characteristics of the site and those of the turbine are taken into consideration. To place thedevices in an optimal configuration, we first consider the change of flow direction during the tidalcycle for the site of interest, allowing only those configurations which avoid wake interference forboth prevailing flow directions; then, we assess the best layout by exploiting positive interactionsbetween devices in the cluster. The mutual fluid dynamic influence is analysed by means of a 3DBlade Element Momentum model of the turbine [4] implemented in the Open Source SHYFEMcode. A series of simulations is performed to outline the power production trend of the module, andconsequently find the optimal distancing between the machines. CFD simulations are also used toextract the module wake characteristics.

Assessment of Five Wind-Farm Parameterizations in the Weather Research and Forecasting Model: A Case Study of Wind Farms in the North Sea

Abstract To simulate the large-scale impacts of wind farms, wind turbines are parameterized within mesoscale models in which grid sizes are typically much larger than turbine scales. Five wind-farm parameterizations were implemented in the Weather Research and Forecasting (WRF) Model v4.3.3 to simulate multiple operational wind farms in the North Sea, which were verified against a satellite image, airborne measurements, and the FINO-1 meteorological mast data on 14 October 2017. The parameterization by Volker et al. underestimated the turbulence and wind speed deficit compared to measurements and to the parameterization of Fitch et al., which is the default in WRF. The Abkar and Porté-Agel parameterization gave close predictions of wind speed to that of Fitch et al. with a lower magnitude of predicted turbulence, although the parameterization was sensitive to a tunable constant. The parameterization by Pan and Archer resulted in turbine-induced thrust and turbulence that were slightly less than that of Fitch et al., but resulted in a substantial drop in power generation due to the magnification of wind speed differences in the power calculation. The parameterization by Redfern et al. was not substantially different from Fitch et al. in the absence of conditions such as strong wind veer. The simulations indicated the need for a turbine-induced turbulence source within a wind-farm parameterization for improved prediction of near-surface wind speed, near-surface temperature, and turbulence. The induced turbulence was responsible for enhancing turbulent momentum flux near the surface, causing a local speed-up of near-surface wind speed inside a wind farm. Our findings highlighted that wakes from large offshore wind farms could extend 100 km downwind, reducing downwind power production as in the case of the 400-MW Bard Offshore 1 wind farm whose power output was reduced by the wakes of the 402-MW Veja Mate wind farm for this case study. Significance Statement Because wind farms are smaller than the common grid spacing of numerical weather prediction models, the impacts of wind farms on the weather have to be indirectly incorporated through parameterizations. Several approaches to parameterization are available and the most appropriate scheme is not always clear. The absence of a turbulence source in a parameterization leads to substantial inaccuracies in predicting near-surface wind speed and turbulence over a wind farm. The impact of large clusters of offshore wind turbines in the wind field can exceed 100 km downwind, resulting in a substantial loss of power for downwind turbines. The prediction of this power loss can be sensitive to the chosen parameterization, contributing to uncertainty in wind-farm economic planning.

Assessment of the aerodynamic benefits of collocating horizontal- and vertical-axis wind turbines in tandem using actuator line model

The actuator line model is used to study the vertically staggered wind turbine cluster composed of horizontal- and vertical-axis wind turbines (HAWTs and VAWTs) in a tandem layout. We consider three simple configurations, including VAWT upwind of HAWT (V + H), VAWT downwind of HAWT (H + V), and VAWT between the two HAWTs (H + V + H). A VAWT installed upwind of the HAWT can not only generate power by itself but can also enhance the power generation of the HAWT, and the total power increases by about 100 kW. When installed downstream the HAWT, the presence of the VAWT slightly reduces the power generation efficiency of the HAWT. However, the VAWT utilizes the increased wind speed between the HAWT and the ground and generates more power. The total power increases by about 60 kW. When installed between the two HAWTs, the beneficial effects of the VAWT on the downstream HAWT are not manifested. Nevertheless, the wind turbine cluster still generates 50 kW more power than that without the VAWT. Overall, even in the tandem layout where the wake effects are most pronounced, the collocation of VAWTs can still utilize the otherwise wasted wind resources, thus increasing the power generation density of wind farms.

Wind power forecasting: A hybrid forecasting model and multi-task learning-based framework

Accurate forecasting of wind power is of significance for scheduling the grid system when wind power is integrated. However, the deficiency of the training data restricts the models’ forecasting performance and modeling efficiency. In this study, we propose a hybrid forecasting model that is composed of a dual dilated convolution-based self-attention sub-model and an autoregressive sub-model. The dual-branch sub-model utilizes a dual convolution architecture to extract both global and local temporal patterns before capturing attention-based dependencies between multivariate inputs to reflect non-linear correlations. The autoregressive sub-model learns linear correlations to provide supplementary information that compensates for the insensitivity of model response. Furthermore, a multi-task learning-based framework is designed to address insufficient training data of a new turbine cluster. The framework can be divided into one task-shared linear component and multiple task-specific non-linear components. By weighting multiple forecasting tasks, the proposed framework utilizes the collaborative relationships between tasks to improve accuracy on the target turbines. Experiment results show that the proposed forecasting model presents the better forecasting accuracy on actual datasets, and the framework has a significant improvement of 20.08% in accuracy while further reducing dependence on training data, especially for source domain data in transfer learning.

Observation and Simulation of Lightning Strikes in an Offshore Wind Turbine Cluster

AbstractBased on the detection data of the Guangdong‐Hong Kong‐Macau lightning location system from 2013 to 2022, this paper analyzes the characteristics of lightning strikes in an offshore wind turbine cluster (WTC) in Guangdong. This is the first time to quantify the lightning strikes difference between the inner and outer wind turbines (WTs) and discuss the impact mechanisms of WTCs on the characteristics of lightning strikes by the simulation results of electric potential distribution. The observation results show that the stroke density, cloud‐to‐ground (CG) flash density, and average peak current within the affected area increase by 10.1%, 11%, and 5.1%, respectively, but the lightning multiplicity changes little. The inner and outer WTs show different characteristics of lightning strikes. The stroke density, lightning multiplicity, and average peak current in the area of outer WTs increase by 21%, 4.1%, and 6.8%, respectively, while in the area of inner WTs decrease by 20.2%, 19%, and 3.9%, respectively. The CG flash density in the area of outer WTs increases by 16.1% but changes little in the area of inner WTs. The simulation results show there is a significant difference in the electric potential distribution between the inner and outer WTs: the distortion effect of the outer WTs on spatial electric potential is stronger than that of the inner WTs at a greater height, but the difference is not obvious at a lower height. Such a difference in electric potential distribution leads to different characteristics of lightning strikes between the inner and outer WTs.

Observation and Simulation of Lightning Strikes in an Off-shore Wind Turbine Cluster

Observation and Simulation of Lightning Strikes in an Off-shore Wind Turbine Cluster

Observation and Simulation of Lightning Strikes in an Off-shore Wind Turbine Cluster

Observation and Simulation of Lightning Strikes in an Off-shore Wind Turbine Cluster

Optimization and performance study of bidirectional Savonius tidal turbine cluster with deflectors

Savonius vertical axis tidal turbines (S-VATT) show potential utilization in bidirectional tidal regions because of the high start-up characteristics and coupled enhancement effects. Previous studies suggested that deflectors can significantly improve the power output of the isolated turbine and cluster. However, few studies focus on the deflectors installed on the bidirectional S-VATT cluster. This study proposed a deflector system for a bidirectional cluster and used the Kriging method combined with genetic algorithm to optimize the configuration of deflectors. The optimization study showed that the optimized cluster has a power coefficient improvement of 34.5 % compared with the cluster without deflectors. The flow mechanisms of the optimized cluster were analyzed by mean and transient flow fields, which reveal that deflectors could reduce the high-pressure area at the returning blade and increase the low-pressure strength and area at the advancing blade. The effect of tidal direction on the optimized cluster was investigated, and the results showed that the cluster has a high efficiency with a flow angle range of 60°. Finally, this study developed a four-turbine cluster using the configuration of the optimized cluster, and the results showed a power improvement of 22.7 % to the four-turbine cluster without deflectors. In summary, this study provides a high-efficiency cluster with deflectors that maintained a potential to be developed into a large-scale farm installing bidirectional tidal regions.

Equivalent Modeling of LVRT Characteristics for Centralized DFIG Wind Farms Based on PSO and DBSCAN

As large-scale wind turbines are connected to the grid, modeling studies of wind farms are essential to the power system dynamic research. Due to the large number of wind turbines in the wind farm, detailed modeling of each wind turbine leads to high model complexity and low simulation efficiency. An equivalent modeling method for the wind farm is needed to reduce the complexity. For wind farms with widely used doubly-fed induction generators (DFIGs), the existing equivalent studies mainly focus on such continuous control parts as electrical control. These methods are unsuitable for the low voltage ride through (LVRT) part which is discontinuous due to switching control. Based on particle swarm optimization (PSO) and density-based spatial clustering of applications (DBSCAN), this paper proposes an equivalent method for LVRT characteristics of wind farms. Firstly, the multi-turbine equivalent model of the wind farm is established. Each wind turbine in the model represents a cluster of wind turbines with similar voltage variation characteristics. A single equivalent transmission line connects all wind turbines to the power grid. By changing the terminal voltage threshold to enter LVRT, each equivalent turbine can be in different LVRT states. Secondly, an LVRT parameter optimization method based on PSO is used to obtain the dynamic parameters of the equivalent wind turbines. This method of parameter optimization is applicable to the equivalent of LVRT parameters. Thirdly, a clustering method based on DBSCAN is used to obtain suitable clusters of wind turbines. This clustering method can classify wind turbines with similar electrical distances into the same cluster. Finally, two examples are set up to verify the proposed method.

Observation and Simulation of Lightning Strikes in an Off-shore Wind Turbine Cluster

Observation and Simulation of Lightning Strikes in an Off-shore Wind Turbine Cluster