Environmental Monitoring Technologies for Marine Energy

Projects

A diverse set of projects is underway  as part of Triton, including field studies, data analysis technique application, and algorithm development.  The common thread is the aim to improve environmental monitoring and understanding.  A couple of examples of studies are given below.

 

iAMP

The intelligent Adaptable Monitoring Package (iAMP) is designed to be deployed with marine energy devices to collect an integrated set of environmental parameters.  It has been developed by the University of Washington, in collaboration with Oregon State University, Sea Mammal Research Unit Ltd., and PNNL.  It is an integrated instrumentation package supporting multiple oceanographic sensors: stereo-optical cameras and lights, an acoustic camera, a multibeam sonar, a Doppler wave and current profiler, four passive hydrophones, and a passive fish tag receiver.  The iAMP connects to a docking station, which is connected to shore for power and data connectivity.  The system continuously streams sensor data to temporary storage, but only archives sensor data when a potential target is detected by an instrument (such as the passive hydrophones).  This allows targets to be captured by the higher resolution instruments without the massive data storage and analysis requirements associated with having data archived continuously.

 

Having been initially tested at the University of Washington, 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 showed that the package functioned well over many months, with greater than 90% uptime in 2016. PNNL staff assisted with system deployment, recovery, and benchmark testing.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Igiugig fish video analysis

 

This project seeks to analyze video data collected around a river turbine, with the purpose of developing a suitable suite of algorithms that will allow automatic detection of fish from video data.  Ocean Renewable Power Corp (ORPC) deployed their RivGen turbine at Igiugig, Alaska through July and August 2015.  Five video cameras were positioned around the turbine, and video collected from 19 to 25 July and 19 to 27 August.  Cameras operated at night to illuminate the turbine.  During the deployment, the first 10 minutes of each hour were analyzed to detect fish and describe their behavior, and the data reported to the Alaska Department of Fish and Game as part of the permitting requirements.

 

The whole dataset is now being analyzed by staff at MSL to determine:

  • Number of fish that go through the turbine
  • Number of fish that make contact with the turbine or surrounding platform
  • Difference in numbers seen during the day and night
  • Any detectable behavioral difference around the turbine

Previous work completed by the University of Maine is informing the analysis 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, algorithms are being developed to identify when fish are within the video data, to reduce the time for manual analysis in future.  This will enable more streamlined data analysis for future deployments and monitoring requirements.