Turbine Site Research Articles

Turbulence Measurements with Dual-Doppler Scanning Lidars

Velocity-component variances can be directly computed from lidar measurements using information of the second-order statistics within the lidar probe volume. Specifically, by using the Doppler radial velocity spectrum, one can estimate the unfiltered radial velocity variance. This information is not always available in current lidar campaigns. The velocity-component variances can also be indirectly computed from the reconstructed velocities but they are biased compared to those computed from, e.g., sonic anemometers. Here we show, for the first time, how to estimate such biases for a multi-lidar system and we demonstrate, also for the first time, their dependence on the turbulence characteristics and the lidar beam scanning geometry relative to the wind direction. For a dual-Doppler lidar system, we also show that the indirect method has an advantage compared to the direct one for commonly-used scanning configurations due to the singularity of the system. We demonstrate that our estimates of the radial velocity and velocity-component biases are accurate by analysis of measurements performed over a flat site using a dual-Doppler lidar system, where both lidars stared over a volume close to a sonic anemometer at a height of 100 m. We also show that mapping these biases over a spatial domain helps to plan meteorological campaigns, where multi-lidar systems can potentially be used. Particularly, such maps help the multi-point mapping of wind resources and conditions, which improve the tools needed for wind turbine siting.

Grand challenges in the science of wind energy.

Harvested by advanced technical systems honed over decades of research and development, wind energy has become a mainstream energy resource. However, continued innovation is needed to realize the potential of wind to serve the global demand for clean energy. Here, we outline three interdependent, cross-disciplinary grand challenges underpinning this research endeavor. The first is the need for a deeper understanding of the physics of atmospheric flow in the critical zone of plant operation. The second involves science and engineering of the largest dynamic, rotating machines in the world. The third encompasses optimization and control of fleets of wind plants working synergistically within the electricity grid. Addressing these challenges could enable wind power to provide as much as half of our global electricity needs and perhaps beyond.

Does temporal variability limit standardized biological monitoring at wave and tidal energy sites?

Commercial viability of Marine Renewable Energy (MRE) is progressing but no national or international monitoring standards have been established for wave or tidal energy sites. Standardized monitoring within and across MRE sectors is necessary to expedite project permitting/consenting, detect environmental impacts, and enable comparison among sites and technologies. Acoustic backscatter from a bottom-deployed platform at a pilot wave energy site off Newport, Oregon was compared to data collected at a tidal turbine site in Admiralty Inlet, Washington. Metrics that describe fish and macrozooplankton densities and vertical distributions derived from acoustic backscattered energy were compared using wavelets and Autoregressive Moving Average models (ARMA). Average density and vertical distribution values significantly differed between sites. Metrics of density and location in the water column displayed diel (24 h) and tidal (12 h) cycles. Dispersion of animals in the water column varied at 64- and 128-h periods at both sites. Applicability of methods in both sectors suggests that a standard approach to biological monitoring is possible. Stationary acoustics and analytic methods presented here can be used to characterize pre-installation conditions and refine post-installation monitoring to site-specific characteristics to ensure cost-effective detection of impacts associated with MRE development.

COMPARATIVE ANALYSIS OF LSTM, RF AND SVM ARCHITECTURES FOR PREDICTING WIND NATURE FOR SMART CITY PLANNING

Abstract. Meteorological data and its effect have been the attention of the researchers of the smart city planning for thorough utilization and management of resources, that help in effective government management, convenient public services and sustainable industrial development. Renewable sources of energy like wind, solar, are being integrated into city planning to improve environmental quality. Wind energy is utilized through wind turbines and requires foreknowledge of wind parameters like speed and direction. The aim of this paper is to predict dominant wind speed and direction for time-series wind dataset, that can be incorporated into city planning for selecting suitable sites for wind turbines. This paper proposes three one-dimensional (1D) algorithms using Long Short Term Memory (LSTM), Random Forest (RF) and Support Vector Machine (SVM) for dominant wind speed and direction prediction. The proposed 1D LSTM (1DLSTM), RF (1DRF) and SVM (1DSVM) take successive time values in terms of wind speed and direction as input and predict the future dominant speed and direction, separately. The proposed algorithms are trained and tested using historical wind dataset of Stuttgart and Netherlands respectively. Prediction using 1DLSTM results in total accuracies reaching up to 93.9% and 94.7%, up to 92.8% and 93.8% using 1DSVM and up to 88.7% and 89.3% using 1DRF for speed and direction, respectively. Thus, prediction of wind nature using the proposed algorithms, will give city planners advanced knowledge of wind conditions.

An assessment of the mesoscale to microscale influences on wind turbine energy performance at a peri-urban coastal location from the Irish wind atlas and onsite LiDAR measurements

As the global wind energy industry advances to larger wind turbine systems, there still remains opportunities for deploying single medium-to-large-scale wind turbines in distributed wind energy applications. These include community wind farms and “behind-the-meter” wind applications. Such sites tend to be closer to population centres at lower elevations that have more complex wind regimes due to surrounding orography, local terrain and obstacles such as buildings. This research case study examines the regional mesoscale influences and local microscale influences on the post construction measured energy performance of an 850 kW rated wind turbine, with a 60 m hub height, at a peri-urban coastal location. The remodelled Irish wind atlas is used to characterise mesoscale and microscale influences on wind resource around the wind turbine site. A directional analysis of modelled predicted annual energy is compared to the measured wind turbine electrical energy rose. Data from a nine month LiDAR measurement campaign is used to assess directional wind shear profiles at the site. The shear profiles are examined with respect to local buildings obstacles to gain insights into the microscale sources of discrepancies between the predicted energy from the wind atlas and actual energy output of the wind turbine.

A hierarchical fuzzy data envelopment analysis for wind turbine site selection in Indonesia

The Indonesian archipelago has several suitable areas for building wind farms. In the site selection process, apart from wind intensity, there are other factors related to geographical and structural technicality that need to be considered. This research presents a hierarchical fuzzy data envelopment analysis model for identifying suitable locations for the construction of wind farms. The proposed hierarchy consists of two levels that are defined based on the 165 districts and 33 provinces in Indonesia based on data provided by different government organizations. Opinions from experts in different areas were collected and represented by the hesitant fuzzy linguistic term sets, which were converted to optimistic and pessimistic preferences by using linguistic aggregation and, later, integrated to the data envelopment analysis model. Principal component analysis by IBM SPSS was used to verify the importance of the factors that can reduce ineffective indicators from the analysis. The results show that the South Sumatra province has the highest potential for construction of wind farms, especially in the district of Palembang. The West Papua, Papua, and Maluku provinces have descending priority based on good infrastructure accessibility, high wind velocity, and lesser susceptibility to natural disasters.

A low-cost, high-speed, multi-channel Arduino-based data acquisition system for wind turbine systems

Commercially available high-speed multi-channel data acquisition systems can be expensive to purchase and are generally difficult to tailor to specific applications. Arduino is an open-source prototyping platform that facilitates the design and build of computer controlled systems for a wide variety of uses. This article describes in detail several separate high-speed data acquisition systems that have been integrated to simultaneously acquire data from sensors on the rotor, platform and tower of an operating 5 kW horizontal-axis wind turbine along with wind speed and direction data. Due to the presence of strong magnetic fields at the turbine site, turbine direction and yaw rate were measured using high-definition visual footage from a GoPro® and analysed in a MATLAB script. The processed data from the GoPro system was time-aligned with data from the Arduino-based systems allowing the response of the turbine to be analysed in detail.

Microturbines at Drinking Water Tanks Fed by Gravity Pipelines: A Method and Excel Tool for Maximizing Annual Energy Generation Based on Historical Tank Outflow Data

Wherever the flow of water in a gravity pipeline is regulated by a pressure control valve, hydraulic energy in the form of water pressure can instead be converted into useful mechanical and electrical energy via a turbine. Two classes of potential turbine sites exist—those with (class 1, “buffered”) and those without (class 2, “non-buffered”) a storage tank that decouples inflow from outflow, allowing the inflow regime to be modified to better suit turbine operation. A new method and Excel tool (freely downloadable, at no cost) were developed for determining the optimal hydraulic parameters of a turbine at class 1 sites that maximize annual energy generation. The method assumes a single microturbine with a narrow operating range and determines the optimal design flow rate based on the characteristic site curve and a historical time series of outflow data from the tank, simulating tank operation with a numerical model as it creates a new inflow regime. While no direct alternative methods could be found in the scientific literature or on the internet, three hypothetically applicable methods were gleaned from the German guidelines (published by the German Technical and Scientific Association for Gas and Water (DVGW)) and used as a basis of comparison. The tool and alternative methods were tested for nine sites in Germany.

Suitable map analysis for wind energy projects using remote sensing and GIS: a case study in Turkey.

The aim of the study is to create a suitable map for wind energy projects in a rural area. The primary goal here is to show a methodology using automatic object extraction of the target classes of buildings, vegetation, and ground. The secondary goal is to identify the potential effects for wind turbine sites based on four criteria: Wind speed, Slope, Building, and Vegetation using the fuzzy analytical hierarchy process (FAHP). This paper discusses two important situations for wind energy projects. The first strategy is to just determine the best suitable site locations of wind turbines, while the second strategy determines the locations of wind turbines with minimal negative effects on the rural area. The proposed approach is tested using the data obtained from a multi-sensor system in Evrencik, Turkey. In preliminary phases of renewable energy projects, successful results are dependent on evaluating the potential site's suitability with criteria such as social, environmental, physical, and economic conditions. Furthermore, an accuracy analysis is performed on the automatically extracted target classes for the study area, yielding a value of 89% in the remote sensing section of the study. Moreover, for the GIS section of the study, suitable and unsuitable areas are identified, and the suitability levels of the remaining areas are determined for the two strategies. According to the results, 11% of the areas are found to have high, moderate, and low suitability levels, and 89% are unsuitable for the first strategy, whereas these rates are, respectively, 2% and 98% for the second strategy.

Wind Data Analysis, Annual Resource Estimation And Comparison With Measured Annual Energy Yield at the University Wind Turbine

Evaluation of the power potential of a particular type of wind turbine at a specific site is necessary for economic decisions. Therefore, the information of a wind turbine and that of a site have to be measured or predicted and then combined with the power curve of a wind turbine. The main objective of this research was to predict the power potential of the existing small wind turbine with a diameter of 3m and the wind turbine site at the University of Siegen and compare with the annual energy calculated from the measured one year of wind and turbine data. Techniques for prediction of the wind speed distribution of a site were determined and modeled. The power curve of the wind turbine was modeled from data recorded by applying a technique from the novel methods for modelling the power curve. In this research, artificial neural network, Weibull and Rayleigh are the techniques modeled to predict wind speed distribution at the wind turbine site. Rayleigh and Weibull were chosen since the two models depict a better wind speed distribution and require the mean and the standard deviation of the wind speed at the wind turbine site. A neural network trained with the backward propagation levernberg-Marquardt algorithm was applied to predict the wind speed and power potential of the wind turbine site. A comparison between Weibull, Rayleigh and the Levernberg-Marquardt trained neural network wind speed was made. The power curve of the wind turbine was successfully evaluated from wind data and wind turbine data recorded. The results indicate that the annual mean wind speed of the region is 2.54 (m/s) and about 20% of the wind availability was blowing from the west. The annual energy yield predicted from the trained neural network was 372 (kWh) closer to that determined from measured wind speed 360 (kWh) than that determined from Weibull and Rayleigh 337 and 233 (kWh) respectively. The three prediction models are applicable in any region to predict the annual energy of a particular wind turbine site with minimal data available.

Wind Data Analysis, Annual Resource Estimation And Comparison With Measured Annual Energy Yield at the University Wind Turbine

Evaluation of the power potential of a particular type of wind turbine at a specific site is necessary for economic decisions. Therefore, the information of a wind turbine and that of a site have to be measured or predicted and then combined with the power curve of a wind turbine. The main objective of this research was to predict the power potential of the existing small wind turbine with a diameter of 3m and the wind turbine site at the University of Siegen and compare with the annual energy calculated from the measured one year of wind and turbine data. Techniques for prediction of the wind speed distribution of a site were determined and modeled. The power curve of the wind turbine was modeled from data recorded by applying a technique from the novel methods for modelling the power curve. In this research, artificial neural network, Weibull and Rayleigh are the techniques modeled to predict wind speed distribution at the wind turbine site. Rayleigh and Weibull were chosen since the two models depict a better wind speed distribution and require the mean and the standard deviation of the wind speed at the wind turbine site. A neural network trained with the backward propagation levernberg-Marquardt algorithm was applied to predict the wind speed and power potential of the wind turbine site. A comparison between Weibull, Rayleigh and the Levernberg-Marquardt trained neural network wind speed was made. The power curve of the wind turbine was successfully evaluated from wind data and wind turbine data recorded. The results indicate that the annual mean wind speed of the region is 2.54 (m/s) and about 20% of the wind availability was blowing from the west. The annual energy yield predicted from the trained neural network was 372 (kWh) closer to that determined from measured wind speed 360 (kWh) than that determined from Weibull and Rayleigh 337 and 233 (kWh) respectively. The three prediction models are applicable in any region to predict the annual energy of a particular wind turbine site with minimal data available.

Wind speed forecasting using neural networks

The prediction of wind speed is critical in the assessment of feasibility of a potential wind turbine site. This work presents a study on prediction of wind speed using artificial neural networks. Two variations of artificial neural networks, namely, nonlinear autoregressive neural network and nonlinear autoregressive neural network with exogenous inputs, were used to predict wind speed utilizing 1 year of hourly weather data from four locations around the United States to train, validate, and test these networks. This study optimized both neural network configurations and it demonstrated that both models were suitable for wind speed prediction. Both models outperformed persistence model (with a factor of about 2 to 10 in root mean square error ratio). Both artificial neural network models were implemented for single-step and multi-step-ahead prediction of wind speed for all four locations and results were compared. Nonlinear autoregressive neural network with exogenous inputs model gave better prediction performance than nonlinear autoregressive model and the difference was statistically significant.

Uncertainty Quantification of a Coupled Model for Wind Prediction at a Wind Farm in Japan

Reliable and accurate short-term prediction of wind speed at hub height is very important to optimize the integration of wind energy into existing electrical systems. To this end, a coupled model based on the Weather Research Forecasting (WRF) model and Open Source Field Operation and Manipulation (OpenFOAM) Computational Fluid Dynamics (CFD) model is proposed to improve the forecast of the wind fields over complex terrain regions. The proposed model has been validated with the quality-controlled observations of 15 turbine sites in a target wind farm in Japan. The numerical results show that the coupled model provides more precise forecasts compared to the WRF alone forecasts, with the overall improvements of 26%, 22% and 4% in mean error (ME), root mean square error (RMSE) and correlation coefficient (CC), respectively. As the first step to explore further improvement of the coupled system, the polynomial chaos expansion (PCE) approach is adopted to quantitatively evaluate the effects of several parameters in the coupled model. The statistics from the uncertainty quantification results show that the uncertainty in the inflow boundary conditions to the CFD model affects more dominantly the hub-height wind prediction in comparison with other parameters in the turbulence model, which suggests an effective approach to parameterize and assimilate the coupling interface of the model.

Testing efficacy of bird deterrents at wind turbine facilities: a pilot study in Nova Scotia, Canada

Wind energy has become one of the fastest-growing renewable electricity sources globally, and this trend is expected to continue. However, wind turbines cause avian mortality when birds collide with these structures. Although regulatory agencies in many jurisdictions require post-construction bird mortality monitoring at turbine sites, resulting mortality estimates are often imprecise and under-reported. This uncertainty is often attributed to searcher inefficiencies or scavenger losses. Furthermore, data regarding the effectiveness of active bird mortality mitigation at these facilities are also lacking. This pilot study assessed mitigation effectiveness of visual and audio deterrents, using predator owl deterrent models and bioacoustic alarm and predator calls deployed at a wind turbine facility in Nova Scotia, Canada. These deterrents did not deter birds from wind turbines in statistically significant ways, in comparison to control sites. Whilst results were inconclusive, it would be prudent to continue assessing mitigative options to minimize impacts on birds, considering the expected growth of the wind energy sector in Canada.Keywords: Wind energy development; Wind turbines; Bird mortality monitoring; Bird deterrents; Bird mortality mitigation

Optimal location-allocation of storage devices and renewable-based DG in distribution systems

This paper proposes a mixed integer conic programming (MICP) model to find the optimal type, size, and place of distributed generators (DG) over a multistage planning horizon in radial distribution systems. The proposed planning framework focuses on the optimal siting and sizing of wind turbines, photovoltaic panels, gas turbines, and energy storage devices (ESD). Inherently, renewable energy sources and electricity demands are subject to uncertainty. To handle such probabilistic situations in decision-making, the MICP model is extended into a two-stage stochastic programming model. To obtain more practical results, annual historical data are used to generate the scenarios. For the sake of tractability, the k-means clustering technique is used to reduce the number of scenarios while keeping the correlation between the uncertain data. Due to convexity, the proposed MICP model guarantees to find the global optimal solution. To show the potential and performance of the proposed model a 69-bus radial distribution system under different conditions is dully studied and a sensitivity analysis is conducted. Results and comparisons approve its effectiveness and usefulness.

Effects of wind power intermittency on generation and emissions

Wind power depends on environmental conditions that vary and are imperfectly forecastable. This intermittency could increase the desired flexibility of fossil fuel generation, impacting emissions. This paper finds that various types of wind generation intermittency are associated with a shift towards natural gas generation and decreasing CO2 emissions. The environmental effects of wind intermittency should be considered when determining the overall impact of intermittency-related activities such as expanded grid storage or turbine siting decisions.

A GIS-based decision support model for offshore floating wind turbine installation

ABSTRACTThis paper presents a decision support model that assesses offshore suitability for floating wind turbine installation. The model is based on Multi-Criteria Analysis and Geographic Information Systems. An implementation of the model for the Aegean Sea is also presented. The methodological approach followed consists of four stages. The first stage excludes sites that are inappropriate for wind turbine siting based on legislative constraints. At the second stage, a set of evaluation criteria are used in order to identify the most suitable areas for floating wind turbine installation. Next stage is the economic evaluation of the highest-scored sites, as they resulted from final suitability map. Finally, a sensitivity analysis on the weights of the criteria is carried out. Results indicated that only a small percentage of the case study is characterised as appropriate for floating wind turbine siting, although wind potential is considered strong in more sites.

Optimal Design of Building for Urban Wind Energy Utilization

The article deals with numerical and experimental investigation of wind flow in roof area and optimal design of input parts, where is possible to use IRWES system (Integrated Roof Wind Energy System). Orientation of some high-rise objects is suitable for using wind power. In the first phase, the selected building was investigated using CFD simulation for creating space for three small wind turbines in the area of two technical floors. By using 3D print technology, model of the structure in scale 1:300 with rough façade was created. Experimental measurements were performed in Boundary Layer Wind Tunnel in Bratislava. Measurements were made for 3 reference wind speeds, which fulfilled flow similarity of prototype and model. We have compared the results of the numerical simulations and experimental measurements and obtained information on the average wind speeds at the VAWT site. Comparison of the mean wind velocity and external wind pressure coefficient obtained by CFD simulation and experimental measurements showed a good match. Considering the annual average wind speed at about 100m above sea level, we compared the wind acceleration at the turbine site.

Wind Turbine Site Selection in Indonesia, based on a hierarchical Dual Data Envelopment Analysis model

Due to an increasing demand of energy from renewable resources, wind energy is of high demand, especially in areas that have constant high wind intensity. Indonesia is considered an archipelago, where there are many areas that are suitable for building wind farms. However, there are different factors that may affect the site selection for a wind farm. In this study, we propose a hierarchical dual data envelopment analysis (DDEA) model that can be used to identify the suitable location for building a wind farm. Important factors related to geographical and structural technicality are considered in the analysis. Locations from each region of Indonesia were selected at both district and provincial levels. Districts and provinces are considered for decision-making units (DMUs) which are evaluated for efficiency scores based on the DDEA method. The DDEA method is a multi-criterion and non-parametric estimation method that can be used to measure DMU performances. The proposed hierarchy consists of two levels, which are defined based on 165 districts and 33 provinces in Indonesia. The results show that South Sumatra province has the highest potential for construction of wind farms, especially in the district of Palembang. West Papua, Papua, and Maluku provinces have descending priority based on good infrastructure accessibility, high wind velocity, and are less prone to natural disaster.

Low cost of sustainable work and maintenance for offshore wind power speed-increasing device

For the problem of wind turbine outage caused by the failure of wind power transmission devices, we proposed an offshore wind power transmission device with low sustainable operation and low maintenance cost. Through the combination of gearbox A and B, the power transmission of the offshore wind turbine is uninterrupted, and the continuous operation of grid-connected power generation enhances the economic benefits of offshore wind turbines. The maritime maintenance man-hours is reduced by moving the speed increase gearbox A from the offshore wind turbine site to the land for maintenance inspection, and the maintenance cost is significantly reduced.