Results for “NREL Sensor Laboratory” — Page 3

DOE OSTI2015

NWTC Researchers Field-Test Advanced Control Turbine Systems to Increase Performance, Decrease Structural Loading of Wind Turbines and Plants

Researchers at the National Renewable Energy Laboratory's (NREL's) National Wind Technology Center (NWTC) are studying component controls, including new advanced actuators and sensors, for both conventional turbines as well as wind plants. This research will help develop innovative control strategies that reduce aerodynamic structural loads and improve performance. Structural loads can cause damage that increase maintenance costs and shorten the life of a turbine or wind plant.

17 WIND ENERGY↗

DOE OSTI2025

Thermomagnetic generators for ultra-low-grade marine thermal energy harvesting

Low-grade thermal gradients in marine environments represent an underexploited energy source for autonomous sensing and monitoring. Converting such small temperature differences into usable electrical power remains a key challenge for ocean-deployed systems. We present a deployable thermomagnetic generator thoroughly characterized for marine-relevant energy harvesting. The device powers an internet-connected sensor and harvests ultra-low temperature differences akin to those at the ocean surface. It draws heat from water and rejects it to ambient air, operating optimally at a temperature difference (ΔT) of ~7.5 °C. Laboratory prototypes generated up to 6.7 mW at ΔT ~ 10 °C with gentle airflow (~1 m s -1 ). A separate controlled wave-tank demonstration validated stable operation and sensor powering under marine-like boundary conditions. Given its voltage and power margins, the generator could sustain multiple sensor nodes. Scalability and material assessments identify modular deployment and non-rare-earth alternatives as pathways toward practical marine energy harvesting and low-grade waste-heat recovery.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

TEAMER: Performance mapping of Re Vision's Persistance PTO

Contains datasets from experimental measurements that were used to validate Re Vision's Persistence PTO's efficiency and performance. These measurements were obtained using a dynamometer test bench setup. The data includes open-circuit voltage and loss measurements to validate machine characteristics, efficiency mapping tests to determine the generator's performance mapping, and efficiency mapping tests to determine the converter's efficiency over the feasible operating range. This data was collected between June 2023 and September 2023. The data was collected at the National Renewable Energy Laboratory's Flatirons Campus, Colorado, United States. The data was collected using NREL's 5-kW dynamometer test bench, equipped with a torque sensor and various voltage and current sensors fed to a dedicated data acquisition system. Units for the data are included in the data file headers for each data series. A text editor or spreadsheet software such as Excel is required to view the *.csv data. The data are also provided in *.mat files. To view data plots, a Matlab script with *.mat files are provided.

16 TIDAL AND WAVE POWER↗

DOE OSTI2013

Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Underwater thermomagnetic generator for remote marine thermal energy harvesting and sensing

Thermomagnetic generators offer a promising approach for sustainable power generation in remote marine environments. Here, this study presents the design, thermal modeling, and experimental validation of a passively driven underwater thermomagnetic generator developed for powering ocean observation and monitoring sensors. The generator was evaluated under varying working fluids, thermal boundary conditions, and extended operation to assess real-world applicability. Two fluids, deionized water and silicone-based Thermal C5, were tested under simulated shallow- and deep-ocean conditions. Deionized water outperformed Thermal C5, especially in colder environments (~5°C), achieving a peak output of 2.7 mW due to larger temperature gradients and enhanced convective-evaporative heat transfer. Long-duration tests revealed a transient evaporation-condensation cycle that temporarily reduced rotor immersion and performance before stabilizing. The generator powered commercial marine sensors for over 6 h without external batteries, demonstrating the viability of compact, passively cooled thermomagnetic systems for autonomous, off-grid marine sensing.

13 HYDRO ENERGY↗

DOE OSTI2022

Advanced Collision Detection and Site Monitoring for Avian and Bat Species for Offshore Wind Energy (Final Technical Report)

This final technical report summarizes the outcomes from a project that aimed to design, build, and test a persistent and autonomous monitoring system for avian and bat collisions with offshore wind turbines blades and structures. The system comprises four primary sensor modules: 1) on-blade sensor modules for collision detection and dual-vision image capture on each blade with both visible light and near-infrared imagers; 2) additional on-blade collision sensors mounted further from the root; 3) a nacelle-mounted unit including a 360º camera and ultrasonic microphone array; and, 4) an on-blade, high-performance infrared camera module. Primary targeted outcomes were high sensitivity for the detection of blade strikes from bats and small birds, and automatically captured visual confirmation of the striking object; these features are critical for monitoring offshore wind turbine installations, where ground-based methods are not viable. In addition, local recording will provide a long-term sensor recording database. Following laboratory validation, field testing was conducted on an operational wind turbine in collaboration with the National Wind Technology Center at the NREL Flatirons campus over two planned field tests.

17 WIND ENERGY↗

DOE OSTI2020

Thin Film Hydrogen Sensor Development, Testing and Integration Into Low Cost Wireless Sensing Systems (SBIR Phase 1 Final Report)

Element One, Inc. is reporting on the results of its DOE SBIR Phase I project for the development and testing of low-cost thin film hydrogen sensors and their subsequent integration into wireless sensing systems and networks. This work is based on Element One’s chemo-chromic sensing technology which indicates hydrogen presence (leaks) colorimetrically and electronically with no power required. The lack of required power makes them particularly suitable for passive Radio Frequency Identification Device (RFID) technology which can be deployed affordably and abundantly to detect leaks in hazardous environments. Element One built upon its experience with chemochromic materials that change color in the presence of hydrogen. Both thin films and pigments have been used, but thin films have the ability to change conductivity by more than three orders of magnitude making them ideal for resistive sensors. Prototype sensors were fabricated, tested and characterized. For the wireless component, Element One partnered with Esensor, Inc. to construct and test a wireless sensing network. The network was tested and validated at the National Renewable Energy Laboratory (NREL) in February and March. The tests involved exposing the wireless system to hydrogen gas concentrations of 1% and 5%. There were no significant problems encountered, and the system is ready for commercial development. The system demonstrated the viability of wireless hydrogen sensing networks for hydrogen leak detection and other applications.

08 HYDROGEN↗

DOE OSTI2014

NREL RSF Weather Data 2011

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance. This submission includes the Weather data form the RSF systems energy model. The weather data was used to contextualize and compare energy use data like heating and cooling. The weather data was also used by the RSF Energy Model to model the energy use of the RSF. Energy Model data and Measured Energy data related to the RSF Systems Model can be found in the "Related Datasets" section of this submission.

2011↗

DOE OSTI2014

NREL RSF Measured Data 2011

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance. This submission includes the Measured Energy Data from the RSF Systems Model. The data includes a csv with measured data from NREL's Research and Support Facility (RSF) for 2011. The dataset contains hourly data for: Total Cooling (kW)- Total Heating (kW)- Total Mechanical (kW)- Total Lighting (kW)- Total Plug Loads (kW)- Total Data Center (kW)- Total Building (kW)- PV (kW)- Building Net (kW). Energy Model data and Weather data related to the RSF Systems Model can be found in the "Related Datasets" section of this submission.

2011↗

DOE OSTI2014

NREL RSF Energy Model 2011

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance. This submission includes the Energy Model from the RSF Systems Model. Comparing actual performance (metered) with expected performance (modeled) is key to understand corrective actions to ensure performance as originally intended. The Energy Model in this submission was made to model energy usage in the RSF and was compared to actual metered data to verify the model. Measured data and Weather data related to the RSF Systems Model can be found in the "Related Datasets" section of this submission.

2011↗

DOE OSTI2016

Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance.

47 OTHER INSTRUMENTATION↗

DOE OSTI2016

Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Computer Vision on Edge Devices for the Short Term Prediction of Cloud Cover

Edge Computing and IoT are important pieces of today's technological landscape. Here, we build a low-cost IoT sensor for sky imaging and program it using AWS GreenGrass, one of the leading IoT platforms. We demonstrate remote reprogramming of this device to load software that predicts sun shading events through the linear advection method, which is a baseline algorithm that can be used to benchmark algorithmic improvements in future work. Some future directions for sky imaging research are enumerated.

14 SOLAR ENERGY↗

DOE OSTI2020

Hydrogen Mitigation Process Installation at Nevada Solar One

The National Renewable Energy Laboratory (NREL) and Acciona Solar Power (ASP) have developed and are implementing a process that addresses the issue of hydrogen buildup in parabolic trough power plants. Our method selectively removes hydrogen from the expansion tanks of the power plant to control hydrogen levels in the circulating heat-transfer fluid (HTF). During previous work, we developed a sensor that measures hydrogen partial pressure in the expansion-tank headspace gas. We demonstrated that our sensor measures hydrogen levels over a wide range of partial pressure—from 1.33 mbar down to 0.003 mbar. More recently, we conceived and developed an integrated process module that performs both hydrogen sensing and separating functions. The sensor/separator measures hydrogen partial pressure in the headspace gas in the same way as our original sensor design. Additionally, the integrated module separates hydrogen from the headspace gas to reduce hydrogen to the level needed to maintain the performance of receivers in the collector field. We demonstrated the performance of a laboratory-scale version of this module. Testing showed that the module performed as expected: the accuracy of the sensing function was ±7%, and the hydrogen extraction rate for the separating mode was consistent with our modeling predictions. The primary benefit of this module is its simple design, both in terms of function and incorporation into the HTF subsystem of the power plant. Most recently, NREL and ASP planned, specified, and designed a mitigation process that is based on the integrated module. We are currently completing installation of this process at ASP's Nevada Solar One power plant in Boulder City, Nevada, USA. The mitigation process is being installed at ground level below the HTF expansion tanks, where it draws headspace gas from the tanks, removes hydrogen, and returns the treated gas back to the tanks. In this paper, we report progress on the installation and describe some of the many design details and challenges that we addressed during the past year. We will generate initial performance data from the Nevada Solar One mitigation process in early 2020.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2023

Pioneer WEC concept design report

The "Pioneer WEC" project is targeted at developing a wave energy generator for the Coastal Surface Mooring (CSM) system within the Ocean Observatories Initiative (OOI) Pioneer Array. The CSM utilizes solar photovoltaic and wind generation systems, along with rechargeable batteries, to power multiple sensors on the buoy and along the mooring line. This approach provides continuous power for essential controller functions and a subset of instruments, and meets the full power demand roughly 70% of the time. Sandia has been tasked with designing a wave energy system to provide additional electrical power and bring the CSM up-time for satisfying the full-power demand to 100%. This project is a collaboration between Sandia and Woods Hole Oceanographic Institution (WHOI), along with Evergreen Innovations, Monterey Bay Aquarium Research Institute (MBARI), Eastern Carolina University (ECU), Johns Hopkins University (JHU), and the National Renewable Energy Laboratory (NREL). This report captures Phase I of an expected two phase project and presents project scoping and concept design results.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

WEBAT (Wind Energy with Bat AI-based Tracker) [SWR-24-121]

The WEBAT (Wind Energy with Bat AI-based Tracker) is a Python-based bat tracking software, integrating machine learning and computer vision with infrared thermal sensors to enhance the monitoring and protection of bats in proximity to wind turbines.

Ryu, Sora [National Renewable Energy Laboratory (N↗

DOE OSTI2020

Dedicated short range communications used as fail-safe in autonomous navigation

Here in this paper, we have analyzed scenarios for leader-follower vehicle convoys that have the potential to be unsafe, hazardous, or even fatal in order to provide insight on dangerous driving conditions for autonomous vehicle platoons. These scenarios were created in a simulation software called Prescan, and are made to reflect the behavior of the vehicle dynamics and sensor characteristics of a real-world vehicle convoy that is currently being tested at the American Center for Mobility (ACM) and sponsored by the Department of Energy (DOE), National Renewable Energy Laboratory (NREL), the U.S. Army Combat Capabilities Development Command - Ground Vehicle Systems Center (U.S. Army CC-DEVCOM) and the Michigan Department of Transportation (MDOT). The follower vehicle in this study is tested under multiple scenarios, equipped with a Dedicated Short Range vehicle-to-vehicle communication system (DSRC), a radar-based adaptive cruise control system (ACC) and a precision global positioning system (GPS).

42 ENGINEERING↗

DOE OSTI2025

Main Shaft Bending Moment Analysis for a Gamesa G97 2-MW Wind Turbine

In a collaborative project between Siemens Gamesa Renewable Energy and the National Renewable Energy Laboratory (NREL), a cost-effective method to measure dynamic main shaft torque and the influence of non-torque loads was applied to the gearbox of the Gamesa G97 2-megawatt (MW) wind turbine at the NREL Flatirons Campus and verified through a field test campaign that took place in 2023. The method involved the installation of fiber-optic sensors on the ring gear of the gearbox. Additionally, traditional strain gauges and a rotary encoder were installed on the main shaft to measure mechanical torque and orthogonal bending moments. We compare the measured total main shaft bending moments from the field test campaign to those predicted by an updated OpenFAST model of the G97 wind turbine for similar environmental conditions. We also assessed the sensitivity of the predicted bending moment to varying levels of turbulence intensity, shear exponent, and yaw error.

17 WIND ENERGY↗