Wind Energy Monitoring and Mitigation Technologies Tool

Willmott et al. (2015) analysed data collected by the ATOM system over 16 months (December 2011 to March 2013) at two turbines: one in Delaware (US) and one off the coast of North Carolina (US). Thermographic, ultrasound acoustic, and audio acoustic data were collected and used to evaluate system performance.

Willmott et al. (2014) designed and deployed the ATOM system off the coast of North Carolina (US). Seabird observations with the system were compared with those expected.

Popper et al. (2022) identified seven research priority areas relating to offshore wind development in the United States. ADEON is identified as an existing source of monitoring data to build upon.

Miksis-Olds et al. (2021) compared backscatter relating to fish and zooplankton observed from three nodes of ADEON which contain multi-frequency echosounder systems. Data from these sites was collected from November 2017 to December 2020 and analyzed for baseline ecosystem patterns.

Miksis-Olds et al. (2018) detail the deployment, calibration, and use recommendations for ADEON. Experimental procedures relating to mobile platform data collection are described as well.

Lagerveld et al. (2020) evaluated various technologies developed to detect bird and bat collisions with wind turbines.

Przybycin et al. (2019) evaluates the first prototypes of B-Finder. Freshly dead birds and plastic objects were dropped with drones or rockets. B-Finder achieved 95% efficiency at distances from 50-100m. Tests were conducted in western Poland from November 1027 - November 2019.

Good et al. (2022) tested the effectiveness of curtailment combined with NRG Systems' Bat Deterrent System to reduce bat fatalities at the Pilot Hill Wind and Kelly Creek Wind Farms in Illinois (US) during fall migration (between 1 August and 15 October in 2018).

Weaver et al. (2020) quantified bat fatalities at the Los Vientos III, IV, and V wind energy facilities in Texas (US) from 31 July through 30 October in 2017 and 2018, and assessed deterrent effectiveness using generalized linear mixed models.

Bellman et al. (2020) determined the noise reductions achieved by three commercially available pile driving noise mitigation methods through a cross-project analysis of 21 project reports produced by companies involved in offshore construction in the North Sea and Baltic Sea between 2012 and 2019.

Dähne et al. (2017) analysed the effects of construction of the DanTysk offshore wind farm (Germany) on harbour propoises from February to December of 2013 through acoustic monitoring of pile driving noise and harbour porpoise echolocation. Noise reduction was studied for the application of two types of bubble curtains.

Mercader et al. (2019) conducted tank experiments using Biohuts as artifical habitat to observe the relationship between juvenile survival rate and artificial habitat.

Bouchoucha et al. (2016) observed the effects of Biohut implementation on Diplodis species in marinas on the French Mediterranean coast between April and August 2013 and 2014.

Mercader et al. (2017) evaluated the ecosystem effects of 107 Biohuts installed at a large commercial port in the Northern Mediterranean between June and September 2014.

Gradolewski et al. (2021) developed and tested a detection and deterrence system which drew on technologies from previous works (strobing light, sound-based deterrence, artificial intelligence tracking). The system was tested in northern Poland on a land-based wind turbine between May and July of 2020. Validation tests with a fixed-wing drone equipped with GPS and verifying observations by ornithologists have been used to determine the detection efficiency

Nilsson et al. (2018) compared radar systems aimed at bird tracking in southern Sweden from September to November of 2015. Migration intensity, flight direction, and flight speed were evaluated.

Hill et al. (2014) reviewed the various bird detection technologies utilized for bird monitoring at the offshore wind farm alpha ventus in Germany.

Neumann et al. (2009) developed the a fixed pencil beam radar system in order to quantify the migration intensity of birds. The system was developed from ship and military radar systems modified to have greater range and distinguish between avian and non-avian echo signatures

Zehtindjiev et al. (2019) discuss the findings of a year-long study (2018) of the Integrated System for Protection of Birds in Kaliakra, Bulgaria. The study area included 114 wind turbines and bird activity was monitored using three different radar systems: Bird Scan MS1, Deltatrack Radar System, and Radar System Robin.

Michev et al. (2017) observed nocturnal bird migration and anthropogenic bird mortality in Northeast Bulgaria in September, 2014 using the MS1 BirdScan radar system.

Skov et al. (2009) evaluated bird migration levels at the Horns Rev II Offshore Wind Farm, the Horns Rev 1 Transformer Station, and Blåvands Huk from September to November of 2008 using four radar technologies. Birdtracker software was used for flight track identification. Radar data and analysis was compared to visual observations made during the same period.

Hermans et al. (2020) reviewed add-on designs for ecosystem support in offshore wind development. The report provides design drawings for the Cod Hotel.

Degraer et al. (2020) discuss the effects of offshore wind farms on fish populations, particularly as they relate to the introduction of an artificial reef structure (turbine foundation).

Benhemma-Le Gall et al. (2021) observed the effects of construction on harbor porpoise occurrence at two offshore wind farms in Scotland throughout 2017 to 2019 . Harbor porpoise activity was monitored using passive acoustic monitoring (C-PODs) and calibrated noise recorders (SoundTraps and SM2Ms).

Jacobson et al. (2017) estimated the effective harbor porpoise detection area of C-POD passive acoustic monitoring sensors. Population estimates from passive acoustic sensor detection were compared against estimations made with visual observations and a Bayesian model.

Redden et al. (2015) evaluated the performance of different hydrophone technologies by surveying marine mammals in Nova Scotia, Canada from December 2013 to June 2014.

Cryan et al. (2021) applied UV light arrays to a wind turbine in Boulder, Colorado (US) between August 2018 and October 2019 and observed night flying animal behavior using thermal imagery.

Gorresen et al. (2015) illuminated trees with UV in the habitat area of the Hawaiian hoary bat in Hawaii (US) between September of 2009 and October of 2010 in order to observe the effect of the light on bat behavior.

Terrill et al. (2018) used fixed-wing UAVs to evaluate the performance of the DTBird detection and deterrent-triggering systems at the Manzana Wind Power Project located in Kern County, California (US) between December 2016 and August 2017.

Litsgård et al. (2016) monitored bird movement to evaluate the effectiveness of the DTBird system on a wind turbine near Lundsbrunn, Sweden from July to September 2015.

Hanagasioglu et al. (2015) data collected using the DTBird and BTBat systems at the Calandawind wind turbine in Switzerland between June 2014 and October 2014. Data collected were compared to data collected by bird and bat specialists.

May et al. (2012) evaluated the capability of the DTBird system in detecting birds near the rotor swept area of a wind turbine and in studying the flight patterns of birds close to turbines in Norway. Data were collected with the DTBird system between March and September of 2012 at the Smøla wind-power plant in Norway.

Natural Power conducted a validation study at Alliant Energy’s 170 MW English Farms wind power plant in Iowa (US) that compared bat fatalities and energy production at 69 wind turbines under three scenarios (minimal curtailment, blanket curtailment, and DARC curtailment) between August and October 2020.

Corbeau et al. (2021) assessed the use of EchoTrack for tracking bird activity at onshore wind farms in France.

Becker et al. (2019) used EchoTrack to observe bird activity at a proposed wind farm on the Cape west coast of South African.

Jenkins et al. (2018) utilized EchoTrack radar and software to observe great white pelican activity at a proposed wind facility on the Cape west coast of South Africa.

Sella et al. (2021) evaluated the structural and biological efficacy of the ECO Mat over two years from April 2017 to April 2019 in Florida (US).

Cinti (2021) compared the fish assemblage change associated with the placement of ECO Mat material and control material in Port Everglades, Florida (US).

Nehls et al. (2007) compared the costs and efficacy of three methods of noise reduction in pile driving: bubble curtains, modifications to the pile hammer, and pile sleeves. Bubble curtain technologies compared design, diameter, air supply, water depth, and noise reduction. The EGS Bubble Curtain was larger than other bubble curtains, with comparable noise reduction.

Würsig et al. (2000) developed a 25m radius bubble curtain using hose with 3mm holes every 0.3-0.4m through which air was emitted at 20m^3/minute. The sound reduction produced was evaluated for curtain application around a pile driving operation in Hong Kong in April 1996. Noise reductions ranging from 5 to 20 dB were observed, with the greatest reductions in the 1 - 6 kHz frequency range.

Esteban et al. (2019) reviewed the advantages and disadvantages of available gravity based foundations in future offshore wind development.

Marmo et al. (2013) modelled the operating noise produced by offshore wind turbines with three different foundation types: steel monopile, gravity based, and jacket. The gravity based foundation considered was modelled off of the Gravitas Foundation.

Delprat et al. (2011) presented the concept of the ID stat system at the Conference on Wind energy and Wildlife impacts in Norway in May 2011. A small scale study of the technology was scheduled for March 2012 at an onshore wind turbine.

Delprat & Alcuri (2011) presented at the Conference on Wind Energy and Wildlife Impacts on the 2nd-5th of May 2011, in Trondheim, Norway.

Aschwanden et al. (2020) monitored bird movements to assess the effectiveness of the IdentiFlight system at the Wind Energy Research Cluster South in Stötten, Germany between April and May 2020.

McClure et al. (2018) used human observation to test and compare the ability of IdentiFlight to detect, classify, and track birds at the Top of the World wind farm in Wyoming (US) from August to September 2016.

Rogers (2022) evaluated the efficacy of the Identiflight Avian Detection System at the Castle Hill Wind Farm in Tasmania from August 2020 to February 2022.

Deng et al. (2021) designed a animal tracking transmitter with the goal of reducing weight and increasing transmission range. Drones were used to assess the effective range of the transmitter near Richland, Washington (US) in December 2021.

Deng et al (2019) implemented three designs of animal tracking transmitters aimed at tracking bats for applications in wind energy. Lab scale testing and algorithm validation were undertaken in 2018.

May et al. (2017) evaluated the detection ranges of the MERLIN Avian Radar by using an unmanned aerial vehicle to simulate various flight patterns and bird sizes. The study took place off the coast of Central Norway in August 2009.

Skov et al. (2016) utilized the MERLIN Avian Radar in a study of soaring migrants and their attraction to an offshore wind farm in Denmark during the autumn raptor migration in 2010 and 2011.

Fijn et al. (2015) implemented the MERLIN Avian Radar system at the Dutch offshore wind farm Egmond aan Zee from June 2007 to May 2010 in order to study bird flight intensity in the rotor swept zone (25m-115m) over the North Sea. The Merlin system was used to observe fluxes as well as flight altitudes and paths.

Krijgsveld et al. (2011) studied the collision risks and barrier effects of birds due to the Offshore Wind farm Egmond aan Zee in the Netherlands using the MERLIN Avian Radar System between April 2007 and June 2010.

Elzinga et al. (2019) tested the Noise Mitigation System at the Butendiek and Luchterduinen Offshore Wind Parks in the Netherlands in the fall of 2018.

Wochner (2019) discussed the three approaches to reducing noise from pile driving: reducing at the source, breaking the transmissions path, and noise absorption.

Clausen et al. (2019) examined the performance of different passive acoustic monitoring filters and detectors in varying ocean noise environments. The PAMGuard system was used to analyse harbour porpoise clicks and ambient noise data collected in the spring of 2013 and summer of 2014 in the west of Denmark.

Sarnocinska et al. (2017) observed harbor porpoises in the Danish Great and Little Belts between June and November, 2015 with C-PODS and PAMGuard. The accuracy of both methods was compared.

Keating et al. (2013) describe the beaked whale detectors and classifiers used during beaked whale surveys in Southern California (US) in the summer and fall of 2012.

Yack et al. (2009) evaluated the efficacy of using PAMGuard software (version 1.0) to detect cetaceans in marine environments by comparing manual detections to those made by PAMGuard during a SWFSC dolphin survey conducted in the eastern tropical Pacific Ocean from 20 August to 28 November, 2007.

Bureau Waardenburg (2020) conducted a survey of reef establishment techniques in the interest of enhancing ecosystem health around wind farms in the North Sea. Artificial reef structures such as reef cubes were included in the "toolbox" discussed.

Brock et al. (1989) compared the efficacy of four artificial reef structures in establishing reef habitat over 12 years off of Oahu, Hawaii (US).

Del Vita (2016) conducted hydraulic analysis of reef balls in a flume at the University of Naples.

Scyphers et al. (2015) compared the habitat value of oyster shell bags and Reef Balls along eroding coastline in Alabama (US) over the course of two years.

Langhamer et al. (2012) evaluated the state of artificial reef technology as it relates to the development of marine energy resources. Reef Balls were considered in their application around the base of marine energy infrastructure projects.

Wilson (2007) discussed benthic habitat changes expected in the development of offshore wind turbine. Measures for improving habitat such as artificial reef structures and scour protection measures were also discussed.

Rabie et al. (2022) used ReBat as part of a Turbine Integrated Mortality Reduction (TIMR) system-curtailed turbines on 10 turbines at a wind farm in Fond Du Lac County, Wisconsin (US) in 2008-2009.

Foo et al. (2017) used Rebat on two operational wind turbines at Wolf Ridge Wind farm in the southern Great Plains (US) from July 2010 - 2011 to look at bat mortality rates and forging habits in proximity to wind farms.

Zehtindjiev & Whitfield (2021) used the Robin Radar 3D Flex system at the Kaliakra Wind Farm (Bulgaria) to reduce collision risk between December 2020 and February 2021 and to test the system's ability to shut down a turbine to avoid collisions.

Niemi & Tanttu (2020) used the Robin Radar 3D Flex system as part of a deep learning–based automatic bird identification system that was evaluated in terms of bird detection and identification ability at the Tahkoluoto Offshore Wind Farm (Finland).

Soni et al. (2020) evaluated the SafeWind System at Hassel Wind Park in Germany in 2018. They looked at detection range and reaction range for several species.

Salkanović et al. (2020) evaluated various systems which use artificial intelligence software with the intent of predicting bird and bat turbine collisions.

Roche et al. (2017) tested the SafeWind system on a 2MW turbine in Mayenne, France during October 2016. Four cameras were installed in addition to a collision recording device. Infrared and video recordings were taken in addition to meteorological measurements. Experimentation is ongoing.