Currently, MSL supports projects focusing on noise, electromagnetic field measurements, studies of marine organism interaction, habitat mapping techniques, and integrated sensor packages. The common thread is the desire to improve our understanding and environmental monitoring of marine renewable energy devices to ensure the technologies are practical and cost effective. Click on the buttons above to explore the different projects Triton supports.
Triton Field Trials (TFiT) is a PNNL-led continuation of Triton designed to advance environmental monitoring for marine energy device deployment by developing standards and guidelines for collecting and analyzing data. In the beginning stages of TFiT, researchers are documenting the main areas of environmental concern for different types of marine energy development—such as animal interaction, noise, or loss of habitat—and identifying potential measurement techniques and the effectiveness of those methods based on previous applications. The techniques and technologies deemed most successful will be tested in the field under various conditions. The TFiT team will identify and map suitable sites in the northwest for field testing of these methods. This process will help researchers establish the most effective environmental monitoring methods for different settings and scenarios, with the aim to standardize techniques where possible.
The Final Step for Seven Projects
In 2019, researchers will complete final testing for seven technologies:
- WHOI EMF sensors
- BioSonics Perimeter Detector
- FAU’s UMSLI
- Integral Habitat Mapping
This last stage—deploying technologies in a higher energy environment—will help demonstrate the instruments’ capabilities in pre-permitted, grid-connected environments around wave energy converters.
The Hawai’i Wave Energy Test Site (WETS) (on the Kaneohe Marine Corps Base Hawai’i) will host six of the technologies. The Integral Habitat Mapping technology will be tested at the PacWave site off the coast of Oregon. Both are unique locations for advanced in-water testing of MRE and associated environmental monitoring devices. Additionally, the sites are fit with oceanographic instruments that provide wave and ocean current data during testing. This deployment phase will put several years of work and improvements to the test in a realistic environment.
Nicknamed the “Millennium Falcon,” the intelligent Adaptable Monitoring Package (iAMP) accompanies marine energy devices and collects an integrated set of environmental data. iAMP supports multiple oceanographic sensors, including stereo-optical cameras and lights, an acoustic camera, a multibeam sonar, an acoustic Doppler wave and current profiler, four passive hydrophones, and a passive fish tag receiver. It connects to a docking station that communicates to the shore for power and data connectivity. As it continuously streams sensor data, only data on detected targets is archived—the rest goes to temporary storage, thereby saving storage space so that the instrument can use its high-resolution sensors without the massive data storage and analysis requirements associated with constant archiving.
Having been initially tested at the University of Washington (UW), the iAMP was deployed in the inlet channel to Sequim Bay from August to November 2015 and January to May 2016. This long-term endurance monitoring enabled more efficient integration between the different sensors and demonstrated the package functioned well over many months, with greater than 90% uptime in 2016. Due to its success, UW has continued to improve the package with its third-generation model. See the 3G AMP project for details.
UW developed the instrument package in collaboration with Oregon State University, Sea Mammal Research Unit Ltd, and PNNL.
- Access to the R/V Jack Robertson
- Boat crew
- Scientific dive team
- Personnel help to set up and deploy
- Permitted Sequim Bay site for long term deployment
- Onshore assistance
- Electronics lab space
- An acoustic Doppler current profiler (ADCP)
- Data backup storage
The Igiugig Fish Video Analysis project included analyzing underwater video data collected around a river turbine and developing a suite of algorithms that allow automatic detection of fish from video data. Through these efforts, collaborators determined that using a combined approach of automatic detection software and human analysis can reduce labor time by half and improve reporting accuracy over sampling-based methods.
The Ocean Renewable Power Company, Inc. (ORPC) deployed their RivGen® turbine at Igiugig, Alaska, for two months in 2015. Researchers positioned five video cameras around the turbine and collected video for approximately one week each month, with lights that illuminated the turbine at night to allow for continuous monitoring.
During the deployment, researchers manually analyzed the video data the first 10 minutes of each hour in order to detect fish and describe their behavior. The Alaska Department of Fish and Game used the analyzed data for permitting requirements.
At MSL, researchers analyzed the video datasets to determine the number of fish that went through the turbine, the number of fish that made contact with the turbine or surrounding platform, and the difference in quantity of fish seen during the day versus night. This effort also noted any detectable behavioral differences in the fish when they were around the turbine.
Previous work completed by the University of Maine informed the analyses to ensure compatible analytic results between acoustic cameras observing fish around turbines, and the use of video data for the same purpose. In parallel, drawing on collaboration with the University of Washington (UW), PNNL researchers created EyeSea, a machine-learning tool that automatically identifies the presence of fish in the video data and helps reduce the time for future manual analysis.
Over the course of the project, collaborators discovered that most interactions between fish and the turbine occurred at night, and the frequency of fish interactions did not appear to be affected by whether the turbine was spinning or static. Not surprisingly, the analyses also showed that adult fish are qualitatively more likely to avoid collision and show avoidance behavior compared to juveniles.
In terms of analysis of the data, the fish detection algorithms helped eliminate most video that did not contain fish, allowing subsequent human analysis to focus on segments most likely to contain fish. This helped researchers focus the sampling of videos on only those that were most likely to include video of fish.
- EyeSea Fish video analysis