Engineering Papers⌕ Search

Engineering topics

Clifton, Andrew

Publications and source records attributed to Clifton, Andrew.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Grand challenges in the digitalisation of wind energy

The availability of large amounts of data is starting to impact how the wind energy community works. From turbine design to plant layout, construction, commissioning, and maintenance and operations, new processes and business models are springing up. This is the process of digitalisation, and it promises improved efficiency and greater insight, ultimately leading to increased energy capture and significant savings for wind plant operators, thus reducing the levelised cost of energy. Digitalisation is also impacting research, where it is both easing and speeding up collaboration, as well as making research results more accessible. This is the basis for innovations that can be taken up by end users. But digitalisation faces barriers. This paper uses a literature survey and the results from an expert elicitation to identify three common industry-wide barriers to the digitalisation of wind energy. Comparison with other networked industries and past and ongoing initiatives to foster digitalisation show that these barriers can only be overcome by wide-reaching strategic efforts, and so we see these as “grand challenges” in the digitalisation of wind energy. They are, first, creating FAIR data frameworks; secondly, connecting people and data to foster innovation; and finally, enabling collaboration and competition between organisations. The grand challenges in the digitalisation of wind energy thus include a mix of technical, cultural, and business aspects that will need collaboration between businesses, academia, and government to solve. Working to mitigate them is the beginning of a dynamic process that will position wind energy as an essential part of a global clean energy future.

17 WIND ENERGY↗
DOE OSTI2022

Grand Challenges: wind energy research needs for a global energy transition

Wind energy is anticipated to play a central role in enabling a rapid transition from fossil fuels to a system based largely on renewable power. For wind power to fulfill its expected role as the backbone – providing nearly half of the electrical energy – of a renewable-based, carbon-neutral energy system, critical challenges around design, manufacture, and deployment of land and offshore technologies must be addressed. During the past 3 years, the wind research community has invested significant effort toward understanding the nature and implications of these challenges and identifying associated gaps. The outcomes of these efforts are summarized in a series of 10 articles, some under review by Wind Energy Science (WES) and others planned for submission during the coming months. This letter explains the genesis, significance, and impacts of these efforts.

17 WIND ENERGY↗
DOE OSTI2020

Grand Challenges in the Science of Wind Energy

The energy system of the future that depends on 80% or more renewable energy will put new demands on the wind systems that support it. There are significant areas of scientific uncertainly that need to be addressed to enable the innovation required for success. This presentation, based on a 2019 article in Science, lays out those areas of scientific challenges and proposes an integrated wind energy discipline that encompasses the needed research.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2020

The Power Curve Working Group's assessment of wind turbine power performance prediction methods

Wind turbine power production deviates from the reference power curve in real-world atmospheric conditions. Correctly predicting turbine power performance requires models to be validated for a wide range of wind turbines using inflow in different locations. The Share-3 exercise is the most recent intelligence-sharing exercise of the Power Curve Working Group, which aims to advance the modeling of turbine performance. The goal of the exercise is to search for modeling methods that reduce error and uncertainty in power prediction when wind shear and turbulence digress from design conditions. Herein, we analyze data from 55 wind turbine power performance tests from nine contributing organizations with statistical tests to quantify the skills of the prediction-correction methods. We assess the accuracy and precision of four proposed trial methods against the baseline method, which uses the conventional definition of a power curve with wind speed and air density at hub height. The trial methods reduce power-production prediction errors compared to the baseline method at high wind speeds, which contribute heavily to power production; however, the trial methods fail to significantly reduce prediction uncertainty in most meteorological conditions. For the meteorological conditions when a wind turbine produces less than the power its reference power curve suggests, using power deviation matrices leads to more accurate power prediction. We also determine that for more than half of the submissions, the data set has a large influence on the effectiveness of a trial method. Overall, this work affirms the value of data-sharing efforts in advancing power curve modeling and establishes the groundwork for future collaborations.

17 WIND ENERGY↗
DOE OSTI2019

The Power Curve Working Group's Assessment of Wind Turbine Power Performance Prediction Methods

A wind turbine power curve is often only strictly valid for a subset of all atmospheric conditions (i.e., the inner range), while wind turbines also operate in other scenarios (i.e., the outer range). Hence, modeling the power output in real-world conditions is a fundamental challenge. For example, the power deviation matrix (PDM) in Figure 1 displays an overprediction of power production using a reference power curve when wind speed (WS) and turbulence intensity (TI) are both low. Many experts use the PDM approach to observe any systematic bias in power curves and correct this in energy yield models. The mission of the Power Curve Working Group (PCWG) is to bring together wind industry stakeholders to help identify, validate, and develop ways to improve modeling of wind turbine performance in all atmospheric conditions.

17 WIND ENERGY↗
DOE OSTI2019

The Second Wind Forecast Improvement Project (WFIP2): Observational Field Campaign

The science of wind energy forecasting has taken a leap forward with the unique meteorological observations gathered in complex terrain during the Second Wind Forecast Improvement Project (WFIP2). The Second Wind Forecast Improvement Project (WFIP2) is a U.S. Department of Energy (DOE) and National Oceanic and Atmospheric Administration (NOAA) funded program, with private-sector and university partners, which aims to improve the accuracy of numerical weather prediction (NWP) model forecasts of wind speed in complex terrain for wind energy applications. A core component of WFIP2 was an 18-month field campaign which took place in the U.S. Pacific Northwest between October 2015 and March 2017. A large suite of instrumentation was deployed in a series of telescoping arrays, ranging from 500 km across to a densely instrumented 2 x 2 km area similar in size to a high-resolution NWP model grid cell. Observations from these instruments are being used to improve our understanding of the meteorological phenomena that affect wind energy production in complex terrain, and to evaluate and improve model physical parameterization schemes. We present several brief case studies using these observations to describe phenomena that are routinely difficult to forecast, including wintertime cold pools, diurnally driven gap flows, and mountain waves/wakes. Observing system and data product improvements developed during WFIP2 are also described.

17 WIND ENERGY↗
DOE OSTI2019

IEA Wind TCP: Results of IEA Wind TCP Workshop on a Grand Vision for Wind Energy Technology

This report provides workshop findings including the future landscape of the electricity system with over 50% of the electricity supply from wind energy, a review of key innovation opportunities in different topic areas that will be embodied in the wind power plant of the future (including innovations that both reduce levelized cost of energy and increase system value of wind energy), and identification of the key R&D challenges that must be addressed to realize the wind power plant of the future and the Grand Vision.

17 WIND ENERGY↗
DOE OSTI2018

NREL Renewable Energy Resource Data

This data is derived from the Wind Integration National Dataset (WIND) Toolkit and the Solar Integration National Dataset (SIND). The WIND Toolkit provides meteorological conditions and turbine power for more than 126,000 land-based and offshore wind sites across the continental United States, and the SIND Toolkit provides subhourly solar power data designed to assist researchers and utilities in solar generation integration studies This data set includes meteorological variables, including wind speed, direction, temperature, pressure, air density, irradiance, and surface temperature. Parameters such as wind profiles, atmospheric stability, and solar radiation data are also included.

P↗
DOE OSTI2018

NREL Renewable Energy Resource Data

This data is derived from the Wind Integration National Dataset (WIND) Toolkit and the Solar Integration National Dataset (SIND). The WIND Toolkit provides meteorological conditions and turbine power for more than 126,000 land-based and offshore wind sites across the continental United States, and the SIND Toolkit provides subhourly solar power data designed to assist researchers and utilities in solar generation integration studies This data set includes meteorological variables, including wind speed, direction, temperature, pressure, air density, irradiance, and surface temperature. Parameters such as wind profiles, atmospheric stability, and solar radiation data are also included.

Rossol, Michael↗
DOE OSTI2018

L-TERRA [SWR-15-34]

Lidar (light detection and ranging) instruments are often used in wind energy studies in place of meteorological towers, as they can collect measurements at higher heights than met towers and are more easily moved to different parts of a site. While lidars can accurately measure mean wind speeds, they frequently measure different values of turbulence intensity (TI) than would be measured by a cup anemometer on a tower. Discrepancies in TI values are caused by several factors, including instrument noise and volume averaging. The inability of lidars to accurately measure turbulence is a significant barrier to replacing met towers with lidars in the future. There is currently no accepted standard process for quantifying and removing these errors in lidar TI measurements. The Software is an error reduction algorithm for lidar-measured turbulence. The algorithm is trained using co-located lidar and meteorological tower data. Lidar-measured parameters, such as wind speed, shear, and TI, will be used to predict the difference between the lidar- and tower-measured TI. The algorithm will be disseminated as a set of computer code that can be used by lidar users and manufacturers to correct lidar TI data in real time or in post-processing.

Newman, Jennifer↗
DOE OSTI2017

Wind and solar resource data sets

The range of resource data sets spans from static cartography showing the mean annual wind speed or solar irradiance across a region to high temporal and high spatial resolution products that provide detailed information at a potential wind or solar energy facility. These data sets are used to support continental-scale, national, or regional renewable energy development; facilitate prospecting by developers; and enable grid integration studies. This review first provides an introduction to the wind and solar resource data sets, then provides an overview of the common methods used for their creation and validation. A brief history of wind and solar resource data sets is then presented, followed by areas for future research.

14 SOLAR ENERGY↗
DOE OSTI2017

Development of a Dynamic Lidar Uncertainty Framework

As wind turbine sizes increase and wind energy expands to more complex and remote sites, remote-sensing devices such as lidars are expected to play a key role in wind resource assessment and power performance testing. The switch to remote-sensing devices represents a paradigm shift in the way the wind industry typically obtains and interprets measurement data for wind energy. For example, the measurement techniques and sources of uncertainty for a remote-sensing device are vastly different from those associated with a cup anemometer on a meteorological tower. Current IEC standards for quantifying remote sensing device uncertainty for power performance testing consider uncertainty due to mounting, calibration, and classification of the remote sensing device, among other parameters. Values of the uncertainty are typically given as a function of the mean wind speed measured by a reference device and are generally fixed, leading to climatic uncertainty values that apply to the entire measurement campaign. However, real-world experience and a consideration of the fundamentals of the measurement process have shown that lidar performance is highly dependent on atmospheric conditions, such as wind shear, turbulence, and aerosol content. At present, these conditions are not directly incorporated into the estimated uncertainty of a lidar device. In this presentation, we describe the development of a new dynamic lidar uncertainty framework that adapts to current flow conditions and more accurately represents the actual uncertainty inherent in lidar measurements under different conditions. In this new framework, sources of uncertainty are identified for estimation of the line-of-sight wind speed and reconstruction of the three-dimensional wind field. These sources are then related to physical processes caused by the atmosphere and lidar operating conditions. The framework is applied to lidar data from a field measurement site to assess the ability of the framework to predict errors in lidar-measured wind speed. The results show how uncertainty varies over time and can be used to help select data with different levels of uncertainty for different applications, for example, low uncertainty data for power performance testing versus all data for plant performance monitoring.

17 WIND ENERGY↗
DOE OSTI2017

Wind Lidar Activities in the United States

IEA Wind Task 32 seeks to identify and mitigate the barriers to the adoption of lidar for wind energy applications. This work is partly achieved by sharing experience across researchers and practitioners in the United States and worldwide. This presentation is a short summary of some wind lidar-related activities taking place in the country, and was presented by Andrew Clifton at the Task 32 meeting in December 2016 in his role as the U.S. Department of Energy-nominated country representative to the task.

17 WIND ENERGY↗
DOE OSTI2017

A New Framework for Quantifying Lidar Uncertainty

As wind turbine sizes increase and wind energy expands to more complex and remote sites, remote sensing devices such as lidars are expected to play a key role in wind resource assessment and power performance testing. The switch to remote sensing devices represents a paradigm shift in the way the wind industry typically obtains and interprets measurement data for wind energy. For example, the measurement techniques and sources of uncertainty for a remote sensing device are vastly different from those associated with a cup anemometer on a meteorological tower. Current IEC standards discuss uncertainty due to mounting, calibration, and classification of the remote sensing device, among other parameters. Values of the uncertainty are typically given as a function of the mean wind speed measured by a reference device. However, real-world experience has shown that lidar performance is highly dependent on atmospheric conditions, such as wind shear, turbulence, and aerosol content. At present, these conditions are not directly incorporated into the estimated uncertainty of a lidar device. In this presentation, we propose the development of a new lidar uncertainty framework that adapts to current flow conditions and more accurately represents the actual uncertainty inherent in lidar measurements under different conditions. In this new framework, sources of uncertainty are identified for estimation of the line-of-sight wind speed and reconstruction of the three-dimensional wind field. These sources are then related to physical processes caused by the atmosphere and lidar operating conditions. The framework is applied to lidar data from an operational wind farm to assess the ability of the framework to predict errors in lidar-measured wind speed.

17 WIND ENERGY↗
DOE OSTI2017

Moving Beyond 2% Uncertainty: A New Framework for Quantifying Lidar Uncertainty

Remote sensing of wind using lidar is revolutionizing wind energy. However, current generations of wind lidar are ascribed a climatic value of uncertainty, which is based on a poor description of lidar sensitivity to external conditions. In this presentation, we show how it is important to consider the complete lidar measurement process to define the measurement uncertainty, which in turn offers the ability to define a much more granular and dynamic measurement uncertainty. This approach is a progression from the 'white box' lidar uncertainty method.

17 WIND ENERGY↗
DOE OSTI2017

Assessing state-of-the-art capabilities for probing the atmospheric boundary layer: The XPIA field campaign

To assess current capabilities for measuring flow within the atmospheric boundary layer, including within wind farms, the U.S. Dept. of Energy sponsored the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign at the Boulder Atmospheric Observatory (BAO) in spring 2015. Herein, we summarize the XPIA field experiment, highlight novel measurement approaches, and quantify uncertainties associated with these measurement methods. Line-of-sight velocities measured by scanning lidars and radars exhibit close agreement with tower measurements, despite differences in measurement volumes. Virtual towers of wind measurements, from multiple lidars or radars, also agree well with tower and profiling lidar measurements. Estimates of winds over volumes from scanning lidars and radars are in close agreement, enabling assessment of spatial variability. Strengths of the radar systems used here include high scan rates, large domain coverage, and availability during most precipitation events, but they struggle at times to provide data during periods with limited atmospheric scatterers. In contrast, for the deployment geometry tested here, the lidars have slower scan rates and less range, but provide more data during non-precipitating atmospheric conditions. Microwave radiometers provide temperature profiles with approximately the same uncertainty as Radio-Acoustic Sounding Systems (RASS). Using a motion platform, we assess motion-compensation algorithms for lidars to be mounted on offshore platforms. As a result, we highlight cases for validation of mesoscale or large-eddy simulations, providing information on accessing the archived dataset. We conclude that modern remote sensing systems provide a generational improvement in observational capabilities, enabling resolution of fine-scale processes critical to understanding inhomogeneous boundary-layer flows.

17 WIND ENERGY↗
DOE OSTI2017

Ground-Based Remote or In Situ Measurement of Vertical Profiles of Wind in the Lower Troposphere

Knowledge of winds in the lower troposphere is essential for a range of applications, including weather forecasting, transportation, natural hazards, and wind energy. This presentation focuses on the measurement of vertical profiles of wind in the lower troposphere for wind energy applications. This presentation introduces the information that wind energy site development and operations require, how it used, and the benefits and problems of current measurements from in-situ measurements and remote sensing. The development of commercial Doppler wind lidar systems over the last 10 years are shown, along with the lessons learned from this experience. Finally, potential developments in wind profiling aimed at reducing uncertainty and increasing data availability are introduced.

17 WIND ENERGY↗