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WO2025067876A1 - Systèmes et procédés de réduction du risque de collision d'oiseaux avec des éoliennes - Google Patents

Systèmes et procédés de réduction du risque de collision d'oiseaux avec des éoliennes Download PDF

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Publication number
WO2025067876A1
WO2025067876A1 PCT/EP2024/075363 EP2024075363W WO2025067876A1 WO 2025067876 A1 WO2025067876 A1 WO 2025067876A1 EP 2024075363 W EP2024075363 W EP 2024075363W WO 2025067876 A1 WO2025067876 A1 WO 2025067876A1
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WO
WIPO (PCT)
Prior art keywords
avian
track
target
wind turbine
risk
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Pending
Application number
PCT/EP2024/075363
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English (en)
Inventor
Rob VAN DER MEER
Jurjen WESTRA
Siete Hamminga
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Robin Radar Facilities BV
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Robin Radar Facilities BV
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Publication date
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Publication of WO2025067876A1 publication Critical patent/WO2025067876A1/fr
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Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/0276—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling rotor speed, e.g. variable speed
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D80/00—Details, components or accessories not provided for in groups F03D1/00 - F03D17/00
    • F03D80/004—Details, components or accessories not provided for in groups F03D1/00 - F03D17/00 for reducing adverse effects on animals, e.g. preventing avian collisions; Detection of animals
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/0264—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor for stopping; controlling in emergency situations
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/80—Devices generating input signals, e.g. transducers, sensors, cameras or strain gauges
    • F05B2270/805—Radars
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

Definitions

  • the present invention relates to a system and a method for reducing risks of collision between flying avians and objects that may pose a collision risk with the flying avians, and in particular for reducing risks of collision between flying avians and wind turbine blades.
  • the invention further relates to a system and a method for controlling wind turbines to reduce the risk of such collisions.
  • Wind turbines represent a hazard to flying avians such as migratory birds, raptors and other avians of interest. A bird hit by a wind turbine rotor blade may be killed, and the collision may also damage the rotor blade, which can result in stopping of the turbine and costly repairs of the blade. Wind farms can further impact birds through direct habitat loss due to wind farm construction; and displacement through indirect loss of habitat due to birds avoiding a new wind farm area.
  • US 8,742,977 B1 describes a solution that detects birds in the vicinity of wind turbines and engages a deterrent, like intense lights or sounds, to scare the birds away. Similar patents on detecting and repelling birds exist in the field of aviation. Some of these systems employ bird radars, such as disclosed in US 8456349, that have a static 3D coverage zone. Within that zone specific birds (e.g. large birds and flocks) generate an audio or visual alarm. This alarm can be used to e.g. scare away birds or to shut down wind turbines. The alarms are based on size and location of the bird only.
  • the above and other objects are obtained by the provision of a system that combines target track data obtained by radar devices configured for detection of avian objects with environmental risk factors affecting flying avian behaviour and technological parameters of any wind turbine in question.
  • the combined data is used as input for a risk model configured for determining risk levels of possible collisions between a wind turbine and avian objects.
  • the determined risk level can be used to determine a control value for a recommended change of blade velocity for one or more wind turbine, taking into account the corresponding technological parameters; or to trigger an avian dispersal system that disperses avian objects depending on the determined risk level and other parameters.
  • a method for controlling at least one wind turbine to reduce risk of collision between at least one flying avian and at least one wind turbine rotor blade comprising the steps of obtaining a number of radar plots corresponding to at least one avian detected in an avian detection area by an avian detection system comprising at least one avian radar; creating and storing a number of target tracks based on the obtained radar plots, each target track comprising target data determined from radar plots of corresponding avians; obtaining a number of environmental risk factors affecting flying avian behavior in the avian detection area; determining a risk level with respect to a possible collision between the at least one wind turbine and at least one avian using a risk model based on target data of the number of target tracks and the number of environmental risk factors; and determining a control value for at least one wind turbine based on the risk level and technological parameters of the wind turbine, wherein the control value defines a recommended change of blade velocity for the at least one wind turbine.
  • each target track is determined based on at least one obtained radar plot, and further based on sensor data on the corresponding avian detected in the avian detection area, wherein the sensor data is received from an additional sensor, such as a second radar, a camera, or a transponder.
  • an additional sensor such as a second radar, a camera, or a transponder.
  • each target track is based on a match between corresponding target data of at least two, three or four radar plots.
  • the match between corresponding target data of radar plots comprises a match between range data.
  • the match between corresponding target data of radar plots comprises a match between radial velocity data.
  • the match between corresponding target data of radar plots comprises a match between return energy data.
  • matching conditions are known in the art of radar scanning and track generation, but a match condition may have to be fulfilled for a set of target data, which are part of the radar plots being compared, when a new target track is generated based on a number radar plots, while the data of a new radar plot has to match corresponding data of a stored target track, when a new radar plot is used for updating an existing target track.
  • a match condition may be fulfilled for corresponding radar plot or target track, when the difference between the target data being matched is below a predetermined threshold difference.
  • each target track is created based on a plurality of obtained radar plots, preferably at least 4 consecutive radar plots obtained along a path corresponding to an avian flight path.
  • the environmental risk factors comprise information about at least one of: time of year, time of day, weather, visibility, and sun elevation in the avian detection area.
  • the technological parameters of the wind turbine comprise information about at least one of: shutdown delay, wear-and-tear, and expected turbine lifetime.
  • the collision risk level is a value between 0 and 100.
  • control value is a value between 0 and 100, wherein 0 corresponds to unimpeded turbine operation and 100 corresponds to a strong recommendation to completely stop the blades of the wind turbine.
  • control value is determined using an economic model, wherein the risk level and technological parameters of the wind turbine are inputs for the economic model.
  • the economic model further takes as input economic factors for determining the control value, the economic factors comprising at least one of a minimum shutdown duration based on predictable power production and shutdown-related turbine wear-and- tear, and a predetermined budget corresponding to a remaining amount of allowable reduction of blade velocity based on time windows of different power demands and type of avian.
  • the economic model further takes as input legal factors for determining the control value, such as expected reduction in collision casualties between flying avians and the wind turbine rotor blades, expected reduction in loss of habitat of flying avians, or a minimum barrier performance.
  • the risk model comprises a decision tree model based on at least one ruleset, each ruleset comprising at least one rule.
  • the target data determined from radar plots comprises target classification; target track altitude; and/or speed, heading direction and sinuosity with respect to the at least one wind turbine; wherein the decision tree model comprises a track filtering ruleset for determining whether a target track is a track of interest based on at least one of the target data fulfilling the conditions of a respective rule in the track filtering ruleset.
  • applying the decision tree model comprises filtering out all target tracks which do not correspond to a track of interest such as aircraft, birds flying sufficiently high, tracks that are not straight that exhibit foraging behavior, tracks which are not headed towards the at least one wind turbine, or tracks coming from a non-migrating direction.
  • the method comprises obtaining transponder data, and the target tracks which do not correspond to a track of interest are determined at least partially based on the transponder data.
  • the decision tree model comprises an alarm ruleset for determining whether and to what extent a wind turbine presents a risk for collision with an avian based on whether and how long the corresponding target track is located within a predefined alarm area and/or whether the corresponding target track crosses a predefined alarm line.
  • the decision tree model comprises a risk ruleset for determining the level of risk a wind turbine presents for a target track based on at least one environmental risk factor, such as time of year, time of day, weather, visibility, and sun elevation in the avian detection area.
  • the at least one rule is manually adjustable by a user via a GUI, by values for parameters defining the rules.
  • multiple rules may produce different risk levels for a single wind turbine, such as one rule to detect bird activity around sunset and another rule during daytime; and wherein either the highest risk level is applied for determining the control value, or wherein the different risk levels are converted to probabilities and are combined using probability theory for determining the control value.
  • multiple wind turbines may be controlled by a single rule, such as detecting bird migration based on track density.
  • the method comprises calculating statistical data from target data on heading direction and sinuosity of the number of target tracks for at least one predefined alarm area or alarm line, the statistical data comprising at least one of track density in the alarm area, migration traffic rate in the at least one alarm area, and crossing rate of the at least one alarm line; and wherein the risk model further takes into account the statistical data for determining the risk level.
  • the risk model comprises a decision tree model with an avian activity ruleset for determining whether a target track corresponds to local avian activity or migrating avian activity, based on the target data, and further based on the statistical data on at least one of track density in the at least one alarm area, migration traffic rate in the at least one alarm area, or crossing rate of the at least one alarm line.
  • the risk model comprises a decision tree model with a migration activity ruleset for determining whether migration activity has started within an alarm area comprising grid cells, based on whether the number of grid cells classified as migration activity within the alarm area is over a predefined migration activity threshold.
  • the method comprises the steps of: defining a regular grid over the alarm area, the regular grid comprising grid cells, recording target track activity over time in each grid cell, determining a track vector for each target track in each grid cell at each point in time, calculating a cell vector for each grid cell by adding up the track vector of each target track in the grid cell over time, normalizing the resulting cell vector for the number of target tracks to determine a normalized cell vector for each grid cell, and classifying each grid cell as local activity or migration activity based on the length of its respective normalized cell vector, wherein grid cells with a normalized cell vector below a predefined cell vector threshold are classified as local activity, and grid cells with a normalized cell vector over the predefined cell vector threshold are classified as migration activity.
  • classifying each grid cell further comprises the steps of: locating busy cells within the regular grid, wherein a grid cell is defined as a busy cell if its recorded target track count is larger than a predefined cell activity threshold, clustering adjacent busy cells into busy clusters, classifying each busy cluster as local activity or migration activity by determining a track vector for each target track in each busy cluster at each point in time, calculating a cell vector for each busy cluster by adding up the track vector of each target track in the busy cluster over time, normalizing the resulting cell vector for the number of target tracks to determine a normalized cell vector for each busy cluster, and classifying each busy cluster as local activity or migration activity based on the length of its respective normalized cell vector, wherein busy clusters with a normalized cell vector below a predefined cell vector threshold are classified as local activity, and busy clusters with a normalized cell vector over the predefined cell vector threshold are classified as migration activity, and applying the classification of each busy cluster to all cells in the convex hull of the respective busy cluster
  • classifying each grid cell comprises assigning a time-dependent track vector weight to each track vector based on respective age, and normalizing the cell vector takes into account each track vector weight to result in a smoothed cell vector.
  • classifying each grid cell comprises calculating a running average for the normalized cell vector of each grid cell, and classifying each grid cell based on the running average.
  • determining the control value is further based on directional migration activity towards the at least one wind turbine, wherein determining the directional migration activity comprises using a combination of temporal and spatial information by defining at least one probe line in proximity of a wind turbine, the probe lines being arranged substantially perpendicular to an expected migration direction towards the turbine, recording line crossing rate of target tracks crossing each probe line over time, calculating a migration profile corresponding to expected traffic over time by combining line crossing rates from the probe lines, and estimating directional migration activity towards the wind turbine based on the migration profile.
  • the economic model further takes into as input the directional migration activity towards the wind turbine for determining control values distributed over time, based on a remaining available shutdown budget and optionally based on time windows of different power demands.
  • determining the risk level comprises applying a machine learning model based on historical data on bird migration patterns and corresponding labelling of ground truth data.
  • a system for controlling at least one wind turbine to reduce risk of collision between at least one flying avian and at least one wind turbine rotor blade comprising: at least one wind turbine; at least one avian radar configured to detect avians in an avian detection area and return radar plots corresponding to the avians; and one or more processors configured to obtain the radar plots; create and store a number of target tracks based on the obtained radar plots, each target track comprising target data determined from radar plots of corresponding avians; obtain a number of environmental risk factors affecting flying avian behavior in the avian detection area; determine a risk level with respect to a possible collision between the at least one wind turbine and at least one avian using a risk model based on target data of the number of target tracks and the number of environmental risk factors; and determine a control value for at least one wind turbine based on the risk level and technological parameters of the wind turbine, wherein the control value defines a recommended change of blade velocity for the at least
  • the one or more processors are configured to execute a method according to possible implementation forms and embodiments of the first aspect.
  • a method for avian dispersal comprising obtaining radar plots corresponding to at least one avian detected in an avian detection area by an avian detection system comprising at least one avian radar; creating and storing a number of target tracks based on the obtained radar plots, each target track comprising target data determined from radar plots of corresponding avians; obtaining a number of environmental risk factors affecting flying avian behavior in the avian detection area; determining a risk level with respect to a possible collision between the at least one wind turbine and at least one avian using a risk model based on target data of the number of target tracks and the number of environmental risk factors; and triggering an avian dispersal event of an avian dispersal system based on whether the risk level calculated for the avian detection area exceeds an avian dispersal threshold.
  • the method comprises defining at least one of an alarm area or at least one alarm line within the avian detection area, and wherein determining the risk level is based on whether and how long the corresponding target track is located within the alarm area and/or whether the corresponding target track crosses the at least one alarm line.
  • each target track is determined based on at least one obtained radar plot, and further based on sensor data on the corresponding avian detected in the avian detection area, wherein the sensor data is received from an additional sensor, such as a second radar, a camera, or a transponder.
  • an additional sensor such as a second radar, a camera, or a transponder.
  • each target track is based on a match between corresponding target data of at least two, three or four radar plots.
  • the match between corresponding target data of radar plots comprises a match between range data.
  • the match between corresponding target data of radar plots comprises a match between radial velocity data.
  • the match between corresponding target data of radar plots comprises a match between return energy data.
  • a match condition may have to be fulfilled for a set of target data, which are part of the radar plots being compared, when a new target track is generated based on a number radar plots, while the data of a new radar plot has to match corresponding data of a stored target track, when a new radar plot is used for updating an existing target track.
  • a match condition may be fulfilled for corresponding radar plot or target track, when the difference between the target data being matched is below a predetermined threshold difference.
  • each target track is created based on a plurality of obtained radar plots, preferably at least 4 consecutive radar plots obtained along a path corresponding to an avian flight path.
  • the collision risk level is a value between 0 and 100.
  • the risk model comprises a decision tree model based on at least one ruleset, each ruleset comprising at least one rule.
  • the target data determined from radar plots comprises target classification; target track altitude; and/or speed, heading direction and sinuosity with respect to the at least one wind turbine; wherein the risk model comprises a decision tree model with a track filtering ruleset for determining whether a target track is a track of interest based on at least one of the target data fulfilling the conditions of a respective rule in the track filtering ruleset.
  • applying the decision tree model comprises filtering out all target tracks which do not correspond to a track of interest such as aircraft, birds flying sufficiently high, tracks that are not straight that exhibit foraging behavior, tracks which are not headed towards the at least one wind turbine, or tracks coming from a non-migrating direction.
  • the method comprises obtaining transponder data, and the target tracks which do not correspond to a track of interest are determined at least partially based on the transponder data.
  • the risk model comprises a decision tree model with a risk ruleset for determining the level of risk a wind turbine presents for a target track based on at least one environmental risk factor, such as time of year, time of day, weather, visibility, and sun elevation in the avian detection area.
  • at least one rule is manually adjustable by a user via a GUI, by values for parameters defining the rules.
  • multiple rules may produce different risk levels for a single wind turbine, such as one rule to detect bird activity around sunset and another rule during daytime.
  • the method comprises calculating statistical data from target data of the number of target tracks for at least one predefined alarm area or alarm line, the statistical data comprising at least one of track density in the at least one alarm area, migration traffic rate in the at least one alarm area, and crossing rate of the at least one alarm line; and wherein the decision tree model further takes into account the statistical data for determining the risk level.
  • the risk model comprises a decision tree model with an avian activity ruleset for determining whether a target track corresponds to local avian activity or migrating avian activity, based on the target data on heading direction and sinuosity, and further based on the statistical data on track density in the at least one alarm area, migration traffic rate in the at least one alarm area, and/or crossing rate of the at least one alarm line.
  • the risk model comprises a decision tree model with a migration activity ruleset for determining whether migration activity has started within an alarm area comprising grid cells, based on whether the number of grid cells classified as migration activity within the alarm area is over a predefined migration activity threshold.
  • the method comprises the steps of: defining a regular grid over the alarm area, the regular grid comprising grid cells, recording target track activity over time in each grid cell, determining a track vector for each target track in each grid cell at each point in time, calculating a cell vector for each grid cell by adding up the track vector of each target track in the grid cell over time, normalizing the resulting cell vector for the number of target tracks to determine a normalized cell vector for each grid cell, and classifying each grid cell as local activity or migration activity based on the length of its respective normalized cell vector, wherein grid cells with a normalized cell vector below a predefined cell vector threshold are classified as local activity, and grid cells with a normalized cell vector over the predefined cell vector threshold are classified as migration activity.
  • classifying each grid cell further comprises the steps of: locating busy cells within the regular grid, wherein a grid cell is defined as a busy cell if its recorded target track count is larger than a predefined cell activity threshold, clustering adjacent busy cells into busy clusters, classifying each busy cluster as local activity or migration activity by determining a track vector for each target track in each busy cluster at each point in time, calculating a cell vector for each busy cluster by adding up the track vector of each target track in the busy cluster over time, normalizing the resulting cell vector for the number of target tracks to determine a normalized cell vector for each busy cluster, and classifying each busy cluster as local activity or migration activity based on the length of its respective normalized cell vector, wherein busy clusters with a normalized cell vector below a predefined cell vector threshold are classified as local activity, and busy clusters with a normalized cell vector over the predefined cell vector threshold are classified as migration activity, and applying the classification of each busy cluster to all cells in the convex hull of the respective busy cluster
  • classifying each grid cell comprises calculating a running average for the normalized cell vector of each grid cell, and classifying each grid cell based on the running average.
  • triggering an avian dispersal event is further based on directional migration activity towards the at least one wind turbine
  • determining the directional migration activity comprises using a combination of temporal and spatial information by defining at least one probe line in proximity of a wind turbine, the probe lines being arranged substantially perpendicular to an expected migration direction towards the turbine, recording line crossing rate of target tracks crossing each probe line over time, calculating a migration profile corresponding to expected traffic over time by combining line crossing rates from the probe lines, and estimating directional migration activity towards the wind turbine based on the migration profile.
  • determining the risk level comprises applying a machine learning model based on historical data on bird migration patterns and corresponding labelling of ground truth data.
  • a system for avian dispersal comprising: at least one wind turbine; at least one avian radar configured to detect avians in an avian detection area and return radar plots corresponding to the avians; an avian dispersal system; and one or more processors configured to obtain the radar plots; create and store a number of target tracks based on the obtained radar plots, each target track comprising target data determined from radar plots of corresponding avians; obtain a number of environmental risk factors affecting flying avian behavior in the avian detection area; determine a risk level with respect to a possible collision between the at least one wind turbine and at least one avian using a risk model based on target data of the number of target tracks and the number of environmental risk factors; and trigger an avian dispersal event of the avian dispersal system based on whether the risk level calculated for the avian detection area exceeds an avian dispersal threshold.
  • the one or more processors are configured to execute a method according to possible implementation forms and embodiments of the third aspect.
  • Fig. 1 is a schematic illustration of a system for reducing risk of collision between flying avians and wind turbine rotor blades according to an example of the disclosure
  • Fig. 2 shows a schematic top view illustrating an alarm area around a wind turbine with detected target tracks according to an example of the disclosure
  • Fig. 3 shows an overview flow diagram of a method for determining a control value for a wind turbine according to an example of the disclosure
  • Fig. 4 shows a flow diagram of a method determining a risk level of a possible collision between a wind turbine and an avian according to an example of the disclosure
  • Fig. 5 shows a flow diagram of a method for determining whether a target track corresponds to local avian activity or migrating avian activity according to an example of the disclosure
  • Fig. 6 shows a flow diagram of a method for classifying grid cells as local or migrating avian activity according to an example of the disclosure
  • Fig. 7 shows a flow diagram of a method for determining start of a migration activity within an alarm area according to an example of the disclosure
  • Fig. 8 shows a schematic top view illustrating how a track vector is calculated for a target track in a grid cell according to an example of the disclosure
  • Fig. 9 shows a schematic top view illustrating how a normalized cell vector is calculated for grid cells in a grid according to an example of the disclosure
  • Fig. 10 shows a schematic top view illustrating how a smoothed cell vector is calculated for a grid cell according to an example of the disclosure
  • Fig. 11 shows a schematic top view illustrating a method of locating busy cells within the regular grid according to an example of the disclosure
  • Fig. 12 shows a schematic top view illustrating a method of clustering adjacent busy cells into busy clusters according to an example of the disclosure
  • Fig. 13 shows a schematic top view illustrating a method of determining directional migration activity according to an example of the disclosure
  • Fig. 14 illustrates calculating a migration profile over time according to an example of the disclosure
  • Fig. 15 is a schematic illustration of a system for avian dispersal at a wind turbine according to an example of the disclosure.
  • Fig. 16 shows a flow diagram of a method for avian dispersal at a wind turbine according to an example of the disclosure.
  • FIG. 1 there is shown a schematic block diagram illustrating the basic structure of an example embodiment of the present disclosure.
  • the system comprises basically at least one wind turbine 1 comprising rotor blades 2 and an avian detection system with at least one avian radar 5 emitting radar signals and receiving reflections from avians 3 in an avian detection area 4, for instance a flock of birds 3 heading towards the wind turbine 1 (the terms ‘birds’ and ‘avians’ are being used interchangeably throughout the description).
  • Fig. 1 only a single avian radar 5 is shown, but it is understood that more than one avian radar 5 may be used at the same time.
  • the radars can be local radars but also long-range radars may be applied.
  • two radars, one vertical and one horizontal are used.
  • the horizontal radar may provide latitude/longitude and size information of avians 3, while the vertical radar may provide height information and detailed information like wing beats.
  • the avian detection system is a 3D radar system that includes an avian radar 5 with an antenna provided with means for varying its effective pointing direction in elevation.
  • a radar transmitter is operatively connected to the antenna for generating a radar signal for emission via the antenna.
  • the radar transmitter transmits pulses of constant width at a constant pulse repetition frequency (PRF) at X-band or S-Band (or other bands).
  • PRF pulse repetition frequency
  • the avian detection system is a high range resolution FMCW radar system designed for detecting and characterizing avian objects 3 by using micro-Doppler analysis of obtained range-Doppler data.
  • the relative motion is characteristic for different classes of targets, e.g. the flapping motion of a bird’s wing vs.
  • a range-Doppler map or range-radial velocity map the moving parts of a body causes a characteristic Doppler signature, where the main contribution comes from the torso of the body, which causes the Doppler frequency of the target, while the flapping motion of bird wings or propeller blades induces modulation on the returned radar signal and generates sidebands around the central Doppler frequency of a Doppler signature, which may be referred to as micro-Doppler signatures.
  • the width of the sidebands of a micro-Doppler signature within a range-Doppler map/matrix may therefore be indicative of the type of target being hit by the transmitted radar waves.
  • the width of the sidebands of a micro-Doppler signature will then be given by the width of the radial velocity span of the micro-Doppler signature.
  • a radar receiver is operatively connected to the antenna.
  • One or more processors may be operatively connected to the receiver.
  • a sampling system may digitize the radar return video signal.
  • the processor(s) may be configured for detecting and localizing objects, such as birds 3, in azimuth and range.
  • the processor(s) may further be configured for estimating a height of a detected object or bird 3 based on relative amplitudes of echo returns as a function of elevation pointing direction of the antenna.
  • an azimuth scanner is operatively coupled to the antenna for rotating the radar 5 about an axis.
  • the radar 5 antenna may rotate continuously 360 degrees in azimuth while transmitting and receiving.
  • the inclination of the radar antenna may be fixed or variable.
  • Signals (raw data) from the radar 5 may be provided to a first processing unit that comprises image processors and filters known from the prior art and used for instance to remove signals from rain, ground clutter and moving objects that are not birds 3. It is important that the limitations of the used avian radar 5 are taken into account in the risk model 10. For example, it is usually not possible to detect birds 3 that are flying between the waves due to clutter.
  • the processed data is analysed in order to detect and define radar plots 6, and these plots 6 are searched within the avian detection area 4, e.g. in relation to an alarm line 21 or an alarm area 20 defined around a wind turbine 1 .
  • a radar plot 6 may be defined as a reflection (location and strength of the reflection) that is most probably a bird 3 and could be part of a target track 7. Data from a transponder receiver may also be analysed to define transponder plots 50.
  • the radar plots 6 (of multiple radars) are used to generate (3D) target tracks 7.
  • the tracker that can be used is a tracker developed by the applicant as a tool that uses multiple tracking algorithms like Kalman filtering.
  • a target track 7 may be further determined using additional sensor data on the corresponding avian 3 detected in the avian detection area 4, such as a camera used to compare radar reflection with captured images (species recognition).
  • the system Besides data from radars 5 and additional sensors, the system also relies on a number of environmental risk factors 9 affecting flying avian behavior in the avian detection area 4.
  • These environmental risk factors 9 comprise information about time of year, time of day, weather, visibility, and sun elevation in the defined avian detection area 4, among others, and can be provided by a local weather station and/or derived from historical radar data, field work, or biology.
  • the data from the radars 5, transponder receivers, and other sensors is provided to a processing unit, which in practice consists of a number of processors (which may depend on number of servers).
  • This input data may have multiple formats, but for the radars 5 it may consist of raw images with radar reflections.
  • the information from other sensors may be provided in the form of raw data for further processing to the processing unit.
  • the information provided by the avian detection system may be stored in a database (e.g. PostgreSQL or MySQL).
  • This database is preferably located on a separate server, with powerful data management/back-up facilities. Different processing steps performed in accordance with embodiments of the system and method of the present disclosure are discussed in the following and illustrated in connection with Figs. 3-14.
  • Fig. 3 shows an overview flow diagram of a method for reducing risk of collision between flying avians 3 and wind turbine rotor blades 2.
  • An avian radar system comprising at least one avian radar 5 is provided as shown in Fig. 1 , and radar plots 6 are obtained relating to avian objects 3 detected by the radar system. New data are regularly provided by the avian radar system and the optional transponder receiver and further sensors, and corresponding new or updated radar plots 6 and transponder plots 50 are obtained.
  • new horizontal radar data may be provided at regular intervals, such as intervals of or about 1 .3 seconds
  • new vertical radar data may be provided at regular intervals, such as intervals of or about 3 seconds
  • new transponder data may be provided at intervals which may be a bit random, with intervals in the range of 2-4 seconds.
  • raw image data is obtained when the horizontal radar detects an object.
  • the raw data is processed and filtered to remove clutter, where static clutter, such as clutter provided by buildings, can be removed based on raw data, which has been obtained within the last couple of hours.
  • Semi dynamic clutter such as clutter provided by falling leaves, can be removed based on raw data, which has been obtained within the last minutes.
  • Dynamic clutter, such as clutter provided by rain can be removed based on raw data, which has been obtained from the last received images.
  • the final obtained horizontal plot holds size data and location data of the detected object, where the location data comprises latitude and longitude data of the object.
  • the obtained plots may be used to create or update target tracks 7, as will be explained below.
  • raw image data is obtained when the vertical radar detects an object.
  • the raw data is processed and filtered to remove clutter in processes similar to the processes for the horizontal radar date.
  • the final obtained vertical plot holds size data, location data in the form of range data, and height data of the detected object.
  • the obtained plots may be used to create or update target tracks 7, as will be explained below.
  • transponder data is generally ADS-b transponder data associated with certain aircraft (manned or unmanned).
  • the relevant parameters of the received transponder data are selected to obtain a transponder plot 50 holding data which may represent all or part of the following: location, height, direction of movement, speed, and type (classification).
  • the obtained transponder plots 50 may be used to create transponder tracks, as will be explained below.
  • transponder data can be in the form of a Passive Integrated Transponder (PIT) tag, is received by a transponder receiver.
  • the obtained (optionally combined horizontal and vertical) radar plots 6 are compared in order to create and store a number of target tracks 7. If applicable, received transponder plots 50 are used to generate transponder tracks, and these transponder tracks are associated with target tracks 7 based on radar plots 6 to classify target tracks 7 e.g. as an aircraft or UAV.
  • PIT Passive Integrated Transponder
  • the next step is a match of direction of movements of object of the plots.
  • Received radar plots 6 may not hold direction data, and if there is no match between direction data due to lack of such data, the matching proceeds to a match of speed data.
  • Received radar plots 6 however may not hold speed data, and if there is no match between speed data due to lack of such data, the matching proceeds to a match of size data. All received plots hold size data, where for transponder plots the size data may be represented by classification data. If two matched plots 6 have matching size data, then the matching proceeds to the next step, where a new target track 7 is created holding object data 8 of the matched plots 6. It is preferred that a new target track 7 is based on at least four matching radar plots 6.
  • the target track 7 is stored and will be matched with new incoming or received plots 6.
  • the first data to be matched is position or location data, including range and latitude and longitude data for vertical and horizontal radar plot 6 data. If there is no match between position/location data, the plot data of the received plot is stored. If there is a match of position data, then the next step is a match of data for direction of movements of the avian objects 3.
  • Received radar plots 6 may not hold direction data, and if there is no match between direction data due to lack of such data, the matching proceeds to a match of speed data es described above, such as if there is no match between speed data due to lack of such data, the matching proceeds to a match of size data, and if there is no match between the size data, then the plot is stored.
  • the determination of when a match condition is fulfilled for a set of corresponding plot data or a set of corresponding plot and track data is based on a difference measure of values representing the data being matched, where the difference measure may be smaller than a predetermined threshold difference.
  • Received radar plots 6 hold data representing position or location of the detected avian objects 3, but data for direction of movement and speed may not be directly available from the radar plots 6.
  • the matching plots 6 may be part of a track 7, and based on position/location data of a number of received or successively received plots 6 of the same track 7, data for direction of movement and/or speed can be determined for the object of the track 7.
  • the track 7 can now be updated to also include data for direction of movement and/or speed. It is preferred that calculation or determination of data for direction of movement and/or speed is based on at least 4 or 5 received or successively received plots.
  • a target track 7 can represent a single bird 3 or several birds 3, such as a flock of birds 3, and the size data of a target track 7 can represent a single bird 3, several birds 3 or the size of a flock of birds 3.
  • the subsequent method steps include, as illustrated, obtaining environmental risk factors 9 affecting flying avian behavior in the avian detection area 4; and determining a collision risk level 11 using a risk model 10 that relies on the obtained environmental risk factors 9 and target data 8 of the target tracks 7, as will be explained below in more detail with respect to Fig. 4.
  • the economic model 14 can further take as input economic factors 15 for determining the control value 12, these economic factors 15 comprising, for example, a minimum shutdown duration based on predictable power production and shutdown-related turbine wear-and-tear, and/or a predetermined budget corresponding to a remaining amount of allowable reduction of blade velocity.
  • the economic model 14 can further take as input legal factors for determining the control value 12, such as expected reduction in collision casualties between flying avians 3 and the wind turbine rotor blades 2, expected reduction in loss of habitat of flying avians 3, and/or a minimum barrier performance (e.g. at least an average of 30%).
  • Such an output control value 12 can for example be a value between 0 and 100, wherein 0 corresponds to a suggested unimpeded turbine operation and 100 corresponds to a strong recommendation to completely stop the blades 2 of the wind turbine 1. This allows wind turbine operators to already slow down the turbine blades 2 during low pause recommendation in order to prevent an emergency stop at 100, in contrast to the current practice of automatic shutdown events triggered based on a simple threshold.
  • Fig. 4 shows a more detailed flow diagram of a method for determining a collision risk level 11 between wind turbine(s) 1 and avian(s) 3 using a risk model 10 according to an embodiment of the present disclosure.
  • the risk model 10 in this and related embodiments comprises a decision tree model 16 based on at least one ruleset, each ruleset comprising at least one rule.
  • the classification of the tracks may further comprise a classification based on stored track data representing size of the object.
  • the classification classes of a bird track may comprise: large bird, medium bird and small bird.
  • the classification classes of a collision object track may comprise: plane and vehicle. If received, the target classification can also be determined based on transponder data.
  • the decision tree model 16 comprises a track filtering ruleset 17 for determining whether a target track 7 is a track of interest 18 based on the target data 8 fulfilling the conditions of a respective rule in the track filtering ruleset 17, such as the altitude, speed, and/or heading direction of the detected avian 3 being within certain predefined ranges or above/below a certain threshold.
  • tracks of interest 18 are selected according to any or all of the following requirements:
  • the reflection and or the radar cross section (RCS) of a detected target track 7 is above a given threshold, such as above 0.025 m2 (in this case the RCS can represent one or more avian 3, and an RCS can be the sum of individual RCSs);
  • the height above ground of the target track 7 is within a predefined altitude window, such as being less than 150 meters above ground;
  • the ground or airspeed of the detected target track 7 is above a given threshold, such as above 20 m/s;
  • the track length of the detected target track 7(expressed in meters, seconds, or even number of plots) is above a given threshold, such as above 15 seconds when measured as a length of time, or the path of the target track 7 meets certain predefined criteria (such as the path having a predefined shape).
  • the target track 7 is not a track of interest 18, then the track 7 is filtered out, no risk level will be determined, and no resulting control value 12 needs to be calculated. If the target track 7 is a track of interest 18, then at least moving direction and speed of the avian 3 is determined for further risk assessment.
  • the decision tree model 16 also comprises an alarm ruleset 19 for taking into account whether and how long the corresponding target track 7 that has been identified as a track of interest 18 is located within a predefined alarm area 20 and/or whether the corresponding target track 7 crosses a predefined alarm line 21 , for determining whether and to what extent a wind turbine 1 presents a risk for collision with an avian 3.
  • the alarm area 20 and the alarm line(s) 21 are determined based on the current or planned location of the wind turbine(s) 1 in question, in particular based on the circular area where rotor blades 2 can be spinning, i.e. the rotor swept area.
  • the flying height of the avian 3 shall be less than a predetermined height, which can be set to 150 meters. This corresponds to defining the height of the alarm area 20.
  • the alarm area 20 therefore can be considered to extend in three dimensions, with the third dimension being the height, which can be defined by a maximum flying height for a bird 3 to be classified based on its target track 7 as a track of interest 18.
  • the target track 7 on the left is detected as a track of interest 18, and although it is not in the alarm area 20, it has crossed a predefined alarm line 21 arranged outside the alarm area to indicate a direction towards the rotor swept area. Therefore, this target track 7 is taken into account for determining a higher risk level 11 resulting in a control value 12 triggering or strongly suggesting a pause or slowdown for the rotor blades 2 of the wind turbine 1.
  • the target track 7 on the right is also detected as a track of interest 18, but it leaves the alarm area 20 before a determined time threshold, and heads in a direction away from the rotor swept area. Therefore, this target track 7 is not taken into account for calculating a control value 12.
  • the target track 7 on the right may still result in a higher risk level 11 and a suggested slowdown of the rotor blades 2 of the wind turbine 1 , based on sensitivity settings, turbine parameters 13 and further economic factors 15.
  • a detected bird 3 is flying towards the wind turbine 1 , but it is so close to the rotor swept area that it may collide with the rotor blades 2 within a time period being shorter than a determined reaction time, such as 100 seconds, then there is not enough time to react to the bird 3, and hence there is no reason to determine a risk level 11 or calculate a control value 12.
  • the decision tree model 16 also comprises a risk ruleset 22 for taking into account at least one environmental risk factor 9, such as time of year, time of day, weather, visibility, temperature, and sun elevation in the avian detection area 4. These all have a big impact on bird behavior and therefore risk of bird mortality.
  • these environmental risk factor 9 is at least partially data- driven, i.e. based on detecting patterns in the available data and associating these patterns with biological explanations. The following is a few examples of how environmental risk factor 9 can be taken into account in the model.
  • Time of day Some species are not active at night or vice versa. This can be especially relevant for the risk ruleset 22 if the goal is to protect a specific species. For example, in cases where wind turbines are located between a nesting and feeding area of a specific species, these species can be expected to exhibit very regular patterns moving from the nesting area to the feeding area. This can be used to enable/disable rules intended to protect this bird species based on time of day.
  • Sun elevation Biologically, night and day are more related to sun elevation than exact time of day, so the same considerations apply as above, as an alternative.
  • Another possible use of these environmental risk factor 9 originates from the fact that the radar 5 can also miss certain targets.
  • the above factors 9 can be used to estimate what is missed, i.e. what target tracks 7 have a higher possibility of going undetected. Accordingly, the model output can result in a higher control value to slow down or turn off a turbine, even based on the same radar measurements, when more bird activity is expected due to e.g. time of day.
  • the risk model 10 takes into account further statistical data 24 derived from target data 8 on heading direction and sinuosity of a number of target tracks 7, in relation to at least one predefined alarm area 20 or alarm line 21 .
  • avian radars 5 can be used as a reliable and long-term measurement tool, gathering scientific data on bird movements in the area, as well as migration activity. They can automatically detect and log thousands of birds 3 simultaneously, including RCS (as a proxy for physical size), speed, direction and flight path. Historically, this type of information would be gathered by human observers over one or more limited observation periods.
  • the information on bird activity, based on avian radar 5 data, can not only be used as an input for a risk model 10 for determining a collision risk level 11 , but also compared both pre- and post-construction of wind turbines 1 to measure the true impact on the local and migratory bird population.
  • the calculated statistical data 24 may comprise:
  • an alarm area 20 at some distance from the wind turbine 1 can be used. Its location depends on the expected direction of migration (e.g. North-South) and local features in the landscape such as rivers. Tracks 7 are filtered by their sinuosity (straightness) and heading. Then, track density or estimated migration traffic rate (MTR) is used to assess the likelihood of migration. MTR is a standard metric that can be estimated using track length or track speed. Alternatively, an alarm line 21 is used instead of an alarm area 20, and the number of line crossings is estimated per hour per kilometer.
  • MTR estimated migration traffic rate
  • Figures 6-12 illustrate an embodiment where the risk model 10 comprises a decision tree model 16 with a migration activity ruleset 37.
  • a regular grid 27 is defined over the alarm area 20 with regular grid cells 28.
  • the risk model 10 relies on this regular grid 27 for classifying each grid cell 28 as local activity 25 or migration activity 26 as illustrated in the flowchart of Fig. 6.
  • the model 10 can then determine whether migration activity 26 has started within an alarm area 20, based on a rule on whether the number of grid cells 28 classified as migration activity 26 is over a predefined migration activity threshold, as illustrated in the flowchart of Fig. 7.
  • the target track activity 44 is recorded over time in each grid cell 28, as illustrated in Fig. 8. Based on the recorded track activity 44, a track vector 29 is calculated for each target track 7 in each grid cell 28 at each point in time.
  • a cell vector 30 is calculated for each grid cell 28 by adding up the track vector 29 of each target track 7 in the grid cell 28 over time, as illustrated in Fig.
  • the resulting cell vector 30 is then normalized for the number of target tracks 7 to determine a normalized cell vector 31 for each grid cell 28 (right arrows).
  • this normalized cell vector 31 tends to be small, while during migration this normalized cell vector 31 tends to be large. The reason is simply because during migration almost all birds are traveling in the same direction. If there are sufficiently many migration cells it can be determined that migration has started.
  • Each grid cell 28 is thus classified as local activity 25 (top right arrow) or migration activity 26 (bottom right arrow) based on the length of its respective normalized cell vector 31 , wherein grid cells 28 with a normalized cell vector 31 below a predefined cell vector threshold are classified as local activity 25, and grid cells 28 with a normalized cell vector 31 over the predefined cell vector threshold are classified as migration activity 26.
  • a grid with relatively small grid cells 28 can exhibit sampling effects.
  • a running average is calculated for the normalized cell vector 31 of each grid cell 28 and classifying each grid cell 28 is based on this running average. This way sampling effects can be effectively smoothed in time, which in turn gives a more robust cell classification.
  • Figs. 11 and 12 illustrate an embodiment of the method where the classification of grid cells 28 is based on clustering. This allows the model to make decisions based on a limited number of clusters rather than many tracks.
  • busy cells 34 are located within the regular grid 27 as shown in Fig. 11 , wherein a grid cell 28 is defined as a busy cell 34 if its recorded target track 7 count is larger than a predefined cell activity threshold.
  • each busy cluster 35 is classified as local activity 25 or migration activity 26 according to one of the embodiments described before. Finally, the classification of each busy cluster 35 is extended to all cells in the convex hull 36 of the respective busy cluster 35.
  • the rules set for the decision tree model 16 and described before in relation to Figs. 4-10 may be manually adjustable by a user via a GUI, by values for parameters defining the rules.
  • the collision risk level 11 calculated may be given as a percentage number ranging from 0 -100, and a collision risk level above 80 is considered critical.
  • determining the risk level 11 comprises applying a machine learning model based on historical data on bird migration patterns and corresponding labelling of ground truth data.
  • multiple rules may produce different risk levels 11 for a single wind turbine 1 , such as one rule to detect bird activity around sunset and another rule during daytime.
  • either the highest risk level 11 is applied for determining the control value 12, or the different risk levels 11 are converted to probabilities and are combined using probability theory for determining a control value 12.
  • multiple wind turbines 1 may be controlled by a single rule, such as detecting bird migration based on track density.
  • a migration event typically starts with only a few birds. This by itself is hard to distinguish from local activity (“just a few birds”). The birds may also come in waves. This makes it hard to effectively plan turbine shutdown, especially given minimum reaction time and shutdown duration, and limited shutdown budgets.
  • a migration profile 41 can be used, based on both temporal and spatial information.
  • probe lines 39 it is possible to monitor traffic over time. Based on the location of the probe line 39 and average bird speed it is possible to estimate when the target tracks 7 will arrive at a turbine 1 (or complete wind farm). Nearby probe lines 39 can give relatively accurate estimates of core decision variables such as line crossings or MTR, but limited reaction time, while probe lines 39 at a greater distance give less accurate estimates but more reaction time. Combining the data from several probe lines 39 is therefore the best approach to estimate the expected traffic over time. This migration profile 41 can then be used to distribute the shutdown budget, given all constraints.
  • Figs. 13 and 14 illustrate such an embodiment of the method wherein determining the control value 12 is at least partially based on directional migration activity 38 towards the at least one wind turbine 1.
  • determining the directional migration activity 38 comprises using a combination of temporal and spatial information.
  • probe lines 39 are defined in proximity of a wind turbine 1 . These probe lines 39 are arranged substantially perpendicular to an expected migration direction towards the turbine.
  • the probe lines 39 can be placed along known migration routes (a river in this case).
  • line crossing rate 40 of target tracks 7 crossing each probe line 39 can be recorded over time, and a migration profile 41 corresponding to expected traffic over time can be calculated by combining line crossing rates 40 from the probe lines 39, as shown in Fig. 14. Based on the migration profile 41 , directional migration activity 38 towards the wind turbine 1 can be estimated. In the shown example, a migration profile 41 is created based on 3 measurements for each probe line 39. Two shutdown events are then planned for the two migration waves estimated.
  • FIGs. 15 and 16 illustrate another aspect of the present disclosure, wherein the collision risk between flying avians 3 and wind turbine rotor blades 2 is reduced by an avian dispersal system 42.
  • the system is essentially the same as described before with respect to Fig. 1 , with the difference of an additional avian dispersal system 42 arranged in the vicinity of the wind turbine(s) 1.
  • multiple dispersal devices are placed at carefully selected locations, e.g. at the center of alarm areas 20.
  • the avian dispersal system 42 can be any system known from the prior art that is suitable for locating around wind turbines 1 and can generate e.g. an audio or visual alarm that can deter flying avians 3.
  • the system relies on detected target tracks 7 and environmental risk factors 9 affecting flying avian behavior in the avian detection area 4.
  • These and further optional input, as described before in relation to Figs. 3-7, are used as input for a risk model 10 to determine a risk level 11 with respect to a possible collision between the wind turbine 1 and at least one avian 3.
  • an additional difference is that at least one alarm area 20 and/or alarm line 21 is defined within the avian detection area 4, and determining the risk level 11 is based on whether and how long the corresponding target track 7 is located within the alarm area 20 and and/or whether the corresponding target track 7 crosses the at least one alarm line 21 .
  • this risk level 11 is compared to an avian dispersal threshold, and an avian dispersal event 43 is triggered if the risk level 11 calculated for the avian detection area 4 exceeds this avian dispersal threshold. If the risk level 11 obtained is below the predetermined avian dispersal threshold value, no avian dispersal event 43 is triggered, and the target track 7 is stored. In an embodiment that the predetermined avian dispersal threshold value of the collision risk level 11 is set to a value of 60.
  • dispersal techniques does not rule out shutdown. It is entirely possible to have a completely separate set of rules for the aspect of wind turbine control using a recommended control value 12, next to a set of rules for dispersal as described above. Ideally, a combined system would be configured in such a way that wind turbine control is only triggered when a dispersal effort fails.

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Abstract

L'invention concerne un système et un procédé de réduction du risque de collision entre des oiseaux volants (3) et des pales de rotor (2) d'une éolienne (1) en combinant des données sur des pistes cibles (7) obtenues par au moins un radar aviaire (5) et des facteurs de risque environnemental (9) affectant le comportement aviaire dans une zone de détection aviaire (4) pour déterminer un niveau de risque (11) d'une collision possible, et en utilisant ce niveau de risque (11) et des paramètres technologiques (13) de l'éolienne (1) pour déterminer une valeur de commande (12) définissant un changement recommandé de vitesse de pale ; ou pour déclencher un système de dispersion aviaire (42) qui disperse des oiseaux (3) selon que le niveau de risque déterminé (11) dépasse un seuil de dispersion aviaire.
PCT/EP2024/075363 2023-09-27 2024-09-11 Systèmes et procédés de réduction du risque de collision d'oiseaux avec des éoliennes Pending WO2025067876A1 (fr)

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