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Cotter, Emma

Publications and source records attributed to Cotter, Emma.

Lab Collaboration Project (LCP) for Marine Energy: Quantifying Collision Risk for Fish and Turbines Final Technical Report (Task 10)

A persistent environmental concern for the widespread deployment of tidal turbines is the potential for fish and marine mammals to collide with rotating blades (Copping et al. 2016, Copping and Hemery 2020). This is a consequence of well-documented bird and bat mortalities around wind turbines (Smallwood 2007, Thompson et al. 2017), as well as fish mortality at conventional hydropower dams (Pracheil et al. 2016) and tidal barrages (Dadswell and Rulifson 1994). However, unlike hydropower dams or barrages, tidal turbines do not involve structures that channel all flow through the turbines. Similarly, while functionally similar to wind turbines, tidal turbines often operate at lower relative velocities and, depending on the end-use application, may be significantly smaller than utility-scale wind turbines. Both of these factors reduce the likelihood and severity of collision, but the knowledge base on this topic remains limited.

13 HYDRO ENERGY↗

Report on the 4th Marine Energy Instrumentation and Data Workshop

The 4th Marine Energy Instrumentation and Data workshop was held on March 16 - 17 2022. This gathering brought together marine energy (ME) developers, researchers, and stakeholders to discuss the current state of ME technologies and the industry's instrument and data needs. The overall objective of the workshop was to identify gaps facing the ME industry for needed data collection, processing, and analysis. This report contains findings and recommendations derived from the discussions and presentations during the workshop.

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Observing fish interactions with marine energy turbines using acoustic cameras

Abstract Marine current energy converters such as tidal and riverine turbines have the potential to provide reliable, clean power. The risk of collision of fishes with marine energy turbines is not yet well understood, in part due to the challenges associated with observing fish at turbine sites. Turbidity and light availability can limit the effectiveness of optical sensors like video cameras, motivating the use of acoustic cameras for this task. However, challenges persist in collecting and interpreting data acquired from acoustic cameras. Given the limited number of turbine deployments to date, it is prudent to draw on the application of acoustic cameras to monitor fish in other scenarios. This article synthesizes their use for other fisheries applications to inform best practices and set realistic expectations for the results of acoustic camera monitoring at turbine sites. We discuss six key tasks performed with acoustic cameras: detecting objects, identifying objects as fish, counting fish, measuring fish, classifying fish taxonomically and analysing fish behavior. Specific challenges to monitoring fish at turbine sites are discussed. This article is intended to serve as a reference for researchers, regulators and marine energy developers on effective use of acoustic cameras to monitor fish at turbine sites. The studies detailed in this article provide evidence that, in some scenarios, acoustic cameras can be used to inform the risk of fish collision with marine energy turbines but doing so requires careful study design and data processing.

Cotter, Emma↗

Powering Arctic Observations with Marine Energy

Energy derivation from renewable marine sources is a rapidly expanding field of research with large and small-scale applications. Large-scale applications such as tidal turbines supplying grid scale power are under development in several locations around the world but small-scale applications such as powering ocean observations with renewable energy have not received the same attention. Ocean observations such as temperature measurements are becoming increasingly valuable, especially in the Arctic where the effects of climate change are most dramatic. The marine environment of the Arctic poses unique opportunities and challenges for powering observations with renewable energy, and the technology to power sensors operating in the Arctic with marine renewable energy does not exist yet. This report discusses the power usage and requirements of instruments currently being used in the Arctic and how marine renewable energy could be used to power these instruments. Wave motion, tidal currents, and thermal gradients all are explored as having potential to be used for energy extraction and the powering of sensors at sea. These resources are investigated for Arctic locations and recommendations are made for future studies of specific use cases.

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2020 State of the Science Report, Chapter 10: Environmental Monitoring Technologies and Techniques for Detecting Interactions of Marine Animals with Turbines

The greatest potential risk from turbine operation continues to be perceived by regulators and other stakeholders to be that of marine animals colliding with turbine blades. These potential interactions are the most difficult to observe using common oceanographic instruments and must be undertaken in parts of the ocean where fast moving water and high waves make studies challenging. However, our collective understanding of the effects of marine renewable energy (MRE) devices on marine animals and their habitats has improved since the publication of the 2016 State of the Science report. https://tethys.pnnl.gov/publications/state-of-the-science-2020-chapter-10-environmental-monitoring

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Detection and classification capabilities of two multibeam sonars

Multibeam sonars can be used to detect and classify marine fauna under conditions when optical sensors are ineffective. In this work, we compare the detection and classification capabilities of two multibeam sonars with different operating frequencies, the Tritech Gemini 720is (720 kHz) and the Teledyne BlueView M900-2250 (2250 kHz). The two sonars were deployed with overlapping swaths in a narrow tidal channel with peak currents of approximately 2 m/s where seals, schools of fish, and diving birds were intermittently present. In comparing data concurrently acquired by both sonars, we observe differences in the appearance of detected targets and detection capabilities. The detected target tracks are classified using a random forest model as either individual biological targets (flora and fauna, including fish, diving birds, seals, and plant matter), fish schools, or nonbiological targets (including entrained air and sonar artifacts). Despite the observed differences in detection capabilities for the two sonars, automatic classification distinguishes between target classes with precision and recall above 0.7 and 0.9, respectively. Furthermore, these results suggest that while similar methodologies can be used for data analysis, some outcomes from environmental studies using multibeam sonars may be instrument-specific.

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Automatic Classification of Biological Targets in a Tidal Channel Using a Multibeam Sonar

Multibeam sonars are widely used for environmental monitoring of fauna at marine renewable energy sites. However, they can rapidly accrue vast volumes of data, which poses a challenge for data processing. Here, using data from a deployment in a tidal channel with peak currents of 1–2 m s –1 , we demonstrate the data-reduction benefits of real-time automatic classification of targets detected and tracked in multibeam sonar data. First, we evaluate classification capabilities for three machine learning algorithms: random forests, support vector machines, and k-nearest neighbors. For each algorithm, a hill-climbing search optimizes a set of hand-engineered attributes that describe tracked targets. Here, the random forest algorithm is found to be most effective—in postprocessing, discriminating between biological and nonbiological targets with a recall rate of 0.97 and a precision of 0.60. In addition, 89% of biological targets are correctly classified as either seals, diving birds, fish schools, or small targets. Model dependence on the volume of training data is evaluated. Second, a real-time implementation of the model is shown to distinguish between biological targets and nonbiological targets with nearly the same performance as in postprocessing. From this, we make general recommendations for implementing real-time classification of biological targets in multibeam sonar data and the transferability of trained models.

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Chapter 10 Supplement. Environmental Monitoring Technologies and Techniques for Detecting Interactions of Marine Animals with Marine Renewable Energy Devices

The Chapter 10 Supplementary Material provides information on instrument classes used for monitoring marine renewable energy devices such as passive acoustics, active acoustics, and video cameras. It also describes the applications and challenges of video cameras, and provides a technical glossary related to monitoring terminologies.

47 OTHER INSTRUMENTATION↗

An Intelligent Adaptable Monitoring Package. Final Report

The “Intelligent Adaptable Monitoring Package” project was a four-year effort that demonstrated the feasibility of integrated sensing packages at tidal and wave energy sites. Such integration is generally required by the breadth of sensors required to understand environmental effects at marine energy sites and the operational difficulty of deploying, maintaining, and recovering such sensors. Over the course of the project, the Adaptable Monitoring Package (AMP) was deployed in multiple settings, each corresponding to a project budget period: - Budget Period 1: Demonstration of cabled deployment at Pacific Northwest National Laboratory’s Marine Science Laboratory. The deployment highlighted AMP hardware endurance over a 4-month deployment in a tidally-dominated environment and laid the groundwork for machine learning algorithms to detect and classify targets present in active sonar data. - Budget Period 2: Demonstration of an autonomous deployment at PacWave South off the coast of Newport, Oregon. The deployment highlighted the stability of AMP hardware and software, with the autonomous package collecting data on a duty cycle over a 1.5-month deployment. - Budget Period 3: Demonstration of an autonomous deployment powered by a wave energy converter at the U.S. Navy’s Wave Energy Test Site. The deployment highlighted the potential of wave energy to power ocean observatories and led to the development of machine learning algorithms to detect and classify targets in optical camera data. In aggregate, this project’s greatest success was demonstrating the AMP’s flexibility in a range of deployment scenarios. Each budget period represented a “first of a kind” demonstration of integrated instrumentation – cabled AMP, autonomous AMP, wave-energy powered AMP – and each deployment helped to identify and set goals for the next. Further, despite the exploratory nature of these deployments, each one achieved high system up-time and proved that flexible integration of multiple sensors in a single package represents a viable strategy for marine energy environmental monitoring. The key lessons learned from the project are: - Without continuous power, either from a shore cable or in situ source, many of the benefits of integration are lost (If continuous power is not available, the ability to detect rare events is lost, as is the ability to minimize the risk of behavioral changes through adaptive sensing. However, even on a duty cycle, there is still value in being able to acquire synchronous data from multiple sensors.); and - Observations from a moving platform present substantially greater data processing challenges than those from stationary platforms. Finally, these deployments also demonstrate an important truth: successful integration alone does not guarantee that relevant data are collected. To grow the knowledge base about environmental interactions with marine energy converters, integrated systems, like the AMP, need to include the right sensor mix and connect the data pipelines to effective processing algorithms. These deployments establish a strong foundation for future collaborations with the environmental research community: not only to understand the environmental effects of marine energy, but also to improve our general ability to study life in the sea.

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