Avian radar puts wind farms on the correct flight path

10 April 2014

The use of dedicated radar within the wind power industry to monitor birds has undergone significant development in recent years and is still evolving rapidly. It is playing an increasingly important role in the planning processes for wind farms and in their operation.
Siete Hamminga, Robin Radar, The Hague, Netherlands

Wind power is now established as an attractive source of green, renewable energy but wind farms can come with an environmental cost of their own. Potentially, they can, for example, interfere with local bird populations, disrupting habitats and interrupting migration, and may even cause mortality through impact with turbine blades.

In Europe, impact on local bird populations is recognised within article 6.3 of the EU Habitat Directive, which requires an "appropriate assessment" for any project where there could be an issue. Natura 2000 areas in particular are protected ecological zones within the EU. In the event a project will interfere with habitats or species within the Natura 2000 zones, it may still be allowed to go ahead under two conditions: it must be in the public interest; and the implementers of the project must take steps to mitigate any negative effects.

From aviation to wind farms

Bird strikes - when birds collide with aircraft - have long been recognised as a problem at airports, and this has resulted in the development of avian (bird) radar monitoring systems. We are now seeing avian radar taking on an additional role: in the planning, construction and operation & maintenance of wind farms.

Bird monitoring has traditionally been done by human observation. Radar is not a substitute, but it does offer very powerful complementary capabilities. A human observer can monitor in only one direction at once, estimating a bird's location and height at a distance of up to about 1 km away during the day. Radar can detect birds 10 km away, all around, day and night. It automatically detects birds and logs them by size, speed, direction, exact location, height and weather information. This data can be interpreted to enable the identification of patterns such as: migration routes; seasonal occurrence; key areas for breeding, feeding, wintering, night roosts, etc; macro and micro wind turbine avoidance behaviour;
barrier effects; the impact of specific weather conditions; and adaptive behaviour.

"Long term radar data can serve as input for modelling (simulation and prediction) and enables the measuring of actual cumulative impact of a wind turbine."

Long term radar data can serve as input for modelling (simulation and prediction) and enables the measuring of actual cumulative impact of a wind turbine. Use of the systems in real time, can be linked to remote control of turbines, and used to shut them down in the case of massive migration for example.

Avian radar can be used as part of the appropriate assessment referred to above that is required by the EU Habitat Directive. One recent paper, published in the Journal of Applied Ecology, describes radar as the most appropriate method to collect bird data. This paper presents the results of radar monitoring of birds responding to an established UK wind farm development.

Data collected from avian radar can be utilised in two ways. First, it can be collected over a long period to produce the data required to qualify for the assessment of bird migration and local habitats. Second, it can be viewed in real time to locate flocks and individual birds surrounding the constructed wind farm. This data can help decide whether the wind farm is likely to interfere with massive migration, or even cause death of the birds.

Sensors and data processing

The avian systems developed by Robin Radar consist of two main elements: the sensors; and the data processing centre.
There are three main types of sensor:

  • The horizontal S-band sensor rotates 45 times per minute, emitting a pulse every 0.05 microseconds that travels at the speed of light. The time it takes for the pulse to return defines the distance to the target. It can identify large birds within a radius of 10 km and up to an altitude of 2 km.
  • The vertical X-band sensor can be used in two ways to produce different results. At a viewing angle of 10 degrees, it can identify smaller birds within a 2.5 km range or large birds within a radius of up to 5 km.
  • The FMCW (Frequency Modulated Continuous Wave) radar, was developed specifically for bird monitoring. It offers three viewing modes: the 'staring mode' allows the user to define an area for the radar to point to and focus on; 'scanning mode' allowing scanning of a 360 degree area and collection of data on all birds within a 3.5 km radius; and 'tracking mode', which is employed when the user is interested in a specific target, enabling it to be pinpointed and tracked.

An important feature of FMCW radar is the capability to measure wing beat patterns, which can allow a more detailed categorisation of bird size - small, medium and large. Other variables can be combined with this, such as airspeed and flight path. Sinuosity, the shape of the flight path, can for example reveal the presence of (often protected) soaring birds. Increased knowledge of the size of birds, combined with exact information about their altitude can help wind farm operators limit the need for shut-downs or deployment of deterrents.

Current research work is focused on taking the categorisation to the next level to identify specific bird species, as certain species are prone to flying into moving turbine blades, while others are not.

Signals from the sensors are received in the data processing centre. This facility has the ability to filter out noise from 'clutter' such as trees and rain. It can then show real time data or store data for analysis. The centre also monitors and controls the core functions of the radar.

Challenges of avian radar systems

It must be stressed that the use of avian radar systems is not without certain challenges. Radar was originally designed to track large objects, such as ships and planes. So locating and tracking birds has required considerable research and development work. A vital element is the development of algorithms for filtering birds out of the clutter from the raw radar image.

At Robin Radar, for example, we have developed a 'visualiser' that displays the results from a number of different filter techniques:

  • the 'land filter' for example, continuously removes static clutter from land and buildings;
  • the 'rotor filter' removes objects that move in a repetitive manner at a fixed location, like trees in the wind or turbine rotors;
  • the 'rain filter' detects and eliminates unwanted reflections from rain;
  • the 'object filter' clusters the remaining reflections and defines which ones are to be processed as bird tracks.

This tracking is done using a clever 'tracking algorithm'. It connects different plots belonging to the same target, revealing a bird's flight path as a red line. To prevent showing too much information, users can choose how far back these should be displayed.

The diameter of the circle indicated around the target indicates its size. A yellow line starts at the next predicted location and indicates its course. Its length represents the speed of the bird. For orientation, users can select a map or Google Earth as background. They can also zoom in on a specific area and add notes to locations or targets on the screen, for example human validation of the type of bird.
These notes are time-stamped and saved in the database together with bird observations in the form of MPEG movies.

Many benefits

When used for both long-term analysis and in real time, avian radar offers many benefits. Using the radar system in real time gives the user the ability to implement urgent bird deterrence techniques, as well as providing the data needed to decide whether a shut down is necessary, perhaps in the case of massive migration.

Several wind farms developers have already seen the benefits of employing avian radar on their sites, including:

  • Acciona Energy, to monitor bird activities surrounding its wind farm in Tarifa, Spain.
  • 3G, which uses mobile avian radar technology to analyse the environmental impact of planned wind farms in Poland.
  • The Norwegian Institute for Nature Research (NINA), which uses radar systems for environmental impact assessments.
  • The Austrian energy company EVN, which uses radar for bird monitoring at a wind farm in Kvarna, Bulgaria.
  • The Estonian energy company Eesti Energia, which is using a fixed radar system to monitor an ocean site where an offshore wind farm is proposed.

Transparency, validation, interpretation

Many users of Robin Radar's systems are researchers and we understand that they want to know how their radar gets its data, rather than use systems that are a 'black box' to them. So we take a collaborative approach to providing complete transparency as to how our systems work.

Transparency also means that we need to be realistic about the limitations of radar. No existing radar sees everything. While it offers great possibilities, it is no substitute for human observation. Performance, for example, is affected by clutter from insects, the sea and weather conditions. Also, as we have already mentioned, the ability to recognise individual species is still developing.

This is why we encourage the validation of bird radar systems in general, and especially of our own systems. We aim to do this in co-operation with users and scientists using multiple techniques - ranging from human observations to tagged birds and drones.

Avian radar can provide very important data for wind farm developers. But we recognize that data is not the same as information, and information requires professional interpretation before it can be used as a sound basis for conclusions or policy decisions.

Radar trace of geese and wind farm (Photo: Pawel Plonczkier) Radar trace of geese and wind farm (Photo: Pawel Plonczkier)
Radar van (photo: Pawel Plonczkier consultant radar ornithologist) Radar van (photo: Pawel Plonczkier consultant radar ornithologist)
Radar Radar

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