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Wind profiles from ground-based Doppler lidar at site A1 were calculated for each 6-beam profiling scan (one point every ~20 s). The wind speed retrieval uses a modified version of the Sathe et al., 2015, paper in the references.
Wind profiles from ground-based Galion lidar at the RHOD site were calculated for each 6-beam profiling scan (one point every ~20 seconds). The wind speed retrieval uses the Sathe et al., 2015, paper in the references.
Wind profiles from ground-based Doppler lidar at site A1 were calculated for each 6-beam profiling scan (one point every ~20 s). The wind speed retrieval uses a modified version of the Sathe et al., 2015, paper in the references.
This dataset contains wind profiles retrieved from 6-beam Velocity Azimuth Display (VAD) scans done by the UC Davis scanning Lidar. The wind components (expressed as zonal, meridional and vertical) are retrieved through the algorithm of Paschke et al. (2015). The quality control of the radial wind speed is performed following the algorithm of Goring & Nikora (2002).
Abstract The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models.
To enable the fast growth of the floating offshore wind industry, simulation models must be validated with experimental data. Floating wind model-scale experiments in wind–wave facilities have been performed over the last two decades with varying levels of fidelity and limitations. However, the turbine controls in these experiments have considered only limited control strategies and implementations. To allow for control co-design, this research focuses on implementing and experimentally validating more advanced turbine control actions and strategies in a wind–wave basin for a 1:70-scale model of the International Energy Agency’s wind 15 MW reference wind turbine. The control strategies analyzed include torque control, collective pitch control, and transition region control (setpoint smoothing). Our experimental and numerical results include the effects of varying rotor speeds, blade pitches, and wind environments on the turbine thrust and torque. Numerical models from three different software tools are presented and compared to the experimental results. Their ability to effectively represent the aero-dynamic response of the wind turbine to the control actions is successfully validated. Finally, turbine controller tuning parameters based on the derivatives of thrust and torque are derived to allow for improved offshore wind turbine dynamics and to validate the ability of modeling tools to model the dynamics of floating offshore wind turbines with control co-design.
Extreme scenarios in wind power generation occur with higher frequency and larger magnitude in the recent years due to the ever-increasing extreme meteorological factors. Accurate forecasting of the occurrence of extreme values in wind power generation is of great concern to ensure reliable power system operation. Recently, deep learning models have surged in popularity for wind power forecasting, with the mean squared error (MSE) loss function being commonly used. However, the MSE loss function, being sensitive to extreme values, disproportionately penalizes larger errors, cannot adequately capture the extreme values present in wind energy data, and novel loss functions have seldom been tailored for wind power forecasting. To this end, in this paper, we introduce a novel loss function specifically crafted to capture extreme values in wind power forecasting. The experimental results with four fundamental deep learning methods on open source wind power dataset validate that the new loss function is efficient and superior in all cases compared to MSE in capturing extreme values while maintaining forecasting performance.
Unexpected failures in engineering systems lead to expensive maintenance actions and should be avoided if at all possible. This is particularly true for wind turbine systems for which unexpected failures not only demand costly repairs but also cause long downtime. Motivated by this need, we present an accumulation method for fault early warning and failure anticipation. Here, our research shows that one critical element allowing the ability of early warning is to accumulate the small-magnitude symptoms resulting from gradual changes in an engineering system like wind turbines. Our idea is inspired by the classical cumulative sum method, or CUSUM, but we have to redesign the accumulation mechanism for tackling unique challenges in wind turbine data. The new accumulation method is applied to two real wind turbine datasets, one with gearbox failures and the other with generator failures, and demonstrates superior performance as compared with CUSUM.
The purpose of the dataset is to provide filtered, averaged buoy data in NetCDF and CSV formats. The attached Lidar Buoy Data Dictionary provides further details on the various instruments mounted on the buoys, parameters measured by each instrument, and the frequency of data collection.
This presentation discusses the wind and solar resources in India using 10 years of historical weather data, focusing on utility-scale solar PV, distributed rooftop solar PV, onshore wind, and offshore wind. The data can be used in power sector modeling, including capacity expansion and production cost studies.
This study seeks to establish a comprehensive baseline of knowledge for the replacement and damage of main bearings in wind turbines. The purpose of this report is to provide a high-level summary of the data set, methodology, and results of this work. Full technical details and an extended analysis will be made available in a future publication. We collected data on main bearing replacements and reported damage from industrial partners based in Europe and the United States. In total, we obtained data for 167 wind power plants, with a combined capacity of 15.3 gigawatts (GW). Most of the data set was comprised of land-based, three-point mount, spherical roller bearings. Within this data set were 689 instances of main bearing replacement. Analysis was undertaken in two parts: first, a statistical analysis of the main bearing time-to-replacement data using survival analysis techniques; second, quantitative and qualitative analyses of the obtained damage information. Our results showed that 10% of a fixed main bearing population would be expected to have been replaced by 10.5 years. This is close to half of the 20-year design value. Fitted parametric distributions then indicated that by year 20, between 22% and 25% of main bearings are expected to have been replaced. Analysis of the damage reports revealed spalling to be the main type of damage listed. The additional presence of surface damage in the collected data indicates that at least part of the spalling cases are likely due to surface-initiated rolling contact fatigue. At this stage is not clear what proportion of spalling cases result from "wear induced", surface-initiated and subsurface-initiated rolling contact fatigue. While this work provides important insights into the current state of main bearing replacements and damage, many questions remain. An ongoing and expanding data collection and analysis effort focused on wind turbine main bearings is therefore recommended.
Along with the smart grid development, modern power systems are entering a ‘data-intensive’ era. A vast volume of data from power grids is being collected through advanced sensing and communication technologies, such as smart metering data, phasor measurement data, as well as meteorological data (e.g., wind speed and solar irradiance) related to renewable power generation. Such data contains comprehensive information about the power system covering equipment's health status, power grid's static and dynamic characteristics, renewable power generation, customers’ electricity usage pattern, etc. Therefore, advanced data-analytics techniques are needed to convert such data to knowledge for practical applications. In line with the trend of widespread data-driven applications in power systems, this Special Issue aims to present state-of-the-art research works on advanced data-analytics for power system's operation, control, and situational awareness. There are in total twenty-six papers accepted for publication in this Special Issue through careful peer reviews and revisions. Under the overarching theme of data-driven applications in power systems, the selected papers are broadly categorised into five topics. The summary of every topic is given below. You are, however, strongly encouraged to read the full paper if interested.
The State of Hawai'i has set a target to achieve a 100% Renewable Portfolio Standard (RPS) by 2045, and is well suited to become the first state to achieve this goal due to its relatively small load, high electricity prices, heavy reliance on imported fossil fuels, and favorable conditions for wind and solar. The Bureau of Ocean Energy Management contracted NREL to conduct a cost and feasibility study to provide information to decision makers on Hawai'i about the viability of floating offshore wind to be a part of the 100% RPS. We used NREL's Offshore Regional Cost Analyzer (ORCA) spatial cost model to evaluate the Levelized Cost of Energy (LCOE) in the region surrounding O'ahu as this is the island with the highest energy demand. The ORCA results showed that LCOE could range from around $\$$83 MWh to $\$$194 MWh for commercial operation dates in 2019 but has the potential to decrease to $\$$48 MWh - $\$$109 MWh by 2032 due to maturing global supply chains, increasing turbine rating, and new technological innovations. These costs are expected to be competitive with global floating wind costs in the early 2030s. The strong wind resource, proximity to infrastructure on land, and benign metocean conditions can potentially compensate for the logistical complexities of installing projects in Hawai'i far from mainland supply chains if sufficient investments are made to develop ports, grid infrastructure, and workforce on O'ahu to support the construction and operation of offshore wind projects. In addition to the cost results, this report also discusses the likely technologies that would comprise floating wind projects near O'ahu, the existing infrastructure available to projects, unique conditions facing offshore wind in the region such as exposure to hurricanes and limits on allowable export cable capacity, newly developed wind resource data sets for the region, and local stakeholder perspectives on offshore wind.
As the offshore wind industry emerges on the US East Coast, a comprehensive understanding of the wind resource – particularly extreme events – is vital to the industry's success. Such understanding has been hindered by a lack of publicly available wind profile observations in offshore wind energy areas. However, the New York State Energy Research and Development Authority recently funded the deployment of two floating lidars within two current lease areas off the coast of New Jersey. These floating lidars provide publicly available wind speed data from 20 to 200 m height with a 20 m vertical resolution. In this study, we leverage a year of these lidar data to quantify and characterize the frequent occurrence of high-wind-shear and low-level-jet events, both of which will have a considerable impact on turbine operation. In designing a detection algorithm for these events, we find that the typical, non-dimensional power-law-based wind shear exponent is insufficient to identify many of these extreme, high-wind-speed events. Rather, we find that the simple vertical gradient of wind speed better captures the events. Based on this detection method, we find that almost 100 independent events occur throughout the year with mean wind speed at 100 m height and wind speed gradient of 16 m s -1 and 0.05 s -1 , respectively. The events have strong seasonal variability, with the highest number of events in summer and the lowest in winter. A detailed analysis reveals that these events are enabled by an induced stable stratification when warmer air from the south flows over the colder mid-Atlantic waters, leading to a positive air–sea temperature difference.
The data was generated with ExaWind (https://github.com/Exawind) which couples AMR-Wind (https://github.com/Exawind/amr-wind/), Nalu-Wind (https://github.com/Exawind/nalu-wind), TIOGA (https://github.com/Exawind/tioga), and OpenFAST (https://github.com/OpenFAST/openfast). This is a large-scale simulation of a blade-resolved wind farm using the ExaWind software stack. ExaWind couples together a background flow solver, AMR-Wind, and a near-body solver, Nalu-Wind, through an overset technique from the TIOGA application. Another application, OpenFAST, handles the structural dynamics of the turbine blades and towers, which informs the fluid-structure interaction of the wind turbines with the flow solvers. This particular simulation includes four blade-resolved wind turbines operating in a turbulent atmospheric boundary layer. The AMR-Wind solver uses 500 million cells and is being solved on 256 AMD GPUs of the Oakridge Leadership Computing Facility Frontier supercomputer. Each turbine is assigned its own Nalu-Wind solver with over 13 million elements per turbine and solved using 448 CPU cores, for a total of 1792 CPU cores. For each node, 56 cores contain Nalu-Wind, while 8 cores correspond to AMR-Wind operations on the GPUs. Consequently, ExaWind is entirely utilizing the CPUs and the GPUs of the nodes concurrently. The data used in the visualization is full flow field data output from the simulation. It is lossy-compressed to a specific accuracy using ZFP and written to disk every 16 time-steps to enable real-time flow visualization. The flow fields are sampled at a high temporal frequency to enable real-time, 24fps visualization. The flow fields are sampled every 12 simulation time steps (every 0.04132s).
These data provide vertical mean profiles of wind and direction (30 min time step and 10 m range gate).
The data provide vertical mean profiles of wind and temperature.
Concentrated Solar Power (CSP) is a promising method for using Solar power for electricity generation with thermal energy storage. One of the primary drivers of structural design costs of CSP collector structures is wind loading. To date, the design of these structures has relied on data from wind tunnels that do not adequately capture the dynamic effects observed at scale. NREL initiated a field measurement campaign at the operational Nevada Solar One (NSO) powerplant that uses parabolic troughs as solar collectors. The aim of the project is a detailed characterization of prevailing wind and turbulence conditions and resulting operational loads on parabolic troughs, providing insights on structural dynamic response, and generating a first-of-a-kind, comprehensive, high-resolution wind-loading dataset available for validating simulations of wind loading on collector structures. The measurements at NSO consist of Sonic anemometers on masts at different heights to characterize the incoming flow and conditions within and above the trough field, in combination with a Doppler Lidar scanning the horizontal plane above the troughs. The wind measurements at NSO have been continuously operating since October 2021 and provide a year-long dataset characterizing wind and turbulence conditions. The structural load measurements start in November 2022 and will complement the wind measurements. In this poster, we present first results of the wind measurement campaign by highlighting days with different atmospheric flow conditions. We identified three main factors altering the flow over the parabolic troughs: Wind speed, wind direction, and the angle of the parabolic troughs. The highest loads are expected when the wind blows perpendicular to the trough rows. In this case, the first rows experience the highest loads and block the subsequent rows, creating conditions with decreased wind speed and enhanced turbulence within the trough field. Also, turbulent length scales change. These affected conditions produce unique load cases on the structures that will be captured by the load measurements. If the wind blows along the trough rows, wind and turbulence conditions are less influenced by the troughs.