Compliance with regulatory requirements increasingly demands clear evidence on how birds behave within and around wind farms. To meet this demand, automated multi-sensor monitoring systems such as MUSE have been developed to provide robust data on real-life bird behaviour at and within wind farms. The MUSE system is based on integrated radar and digital camera(s) and was developed during a four-year pioneering investigation on seabird behaviour at offshore wind turbines; the Carbon Trust Bird Collision Avoidance Study 2014-2017.
Why is there a need to monitor bird behaviour?
Driven by ambitious renewable energy targets and reduced costs, the offshore wind industry has experienced a significant growth over the last 20 years, particularly in Europe, where the majority of installed capacity can be found. To obtain planning consent for an offshore wind development, the regulators require detailed assessments of the risk of environmental impacts of the project, along with efficient mitigation measures. One of the most significant environmental concerns is the potential risk of birds colliding with turbine blades during operation.
Most optimal locations for offshore wind farms are already being exploited and the remaining options typically have a high presence of birdlife. As a consequence, collision risk to birds represents one of the main barriers to further development of offshore wind. Therefore, a bird strike monitoring system capable of detecting and avoiding wind farm collision incidents with birds has been needed to improve wind farms ability to avoid or minimise this interaction.
How do we monitor bird behaviour?
Monitoring of bird behaviour at offshore wind farms is heavily constrained by the difficulty to get observers on the turbine platforms. One of the main aims of MUSE has therefore been to enable automated continuous collection of video data on seabird flight behaviour in an operational offshore wind farm. The developed sensor system is based on one or more pan-tilt digital cameras applied in digital communication with a surveillance radar. The digital communication enables the positioning of the camera(s) using the initial detection by the radar, and it allows coverage day and night by the camera(s) using motion detection and video tracking in larger zones of the wind farm.
The digital communication in MUSE is controlled by a FPGA-based data acquisition and preprocessing system (DAPS) and a software package DAPSControl, for controlling the DAPS. Detection data (blips) are sent directly from the DAPS to the TVADSTracker, the software for automated tracking and geo-referencing of species-specific track data and for storage of recorded tracks and videos. The DAPS samples at 100 Mhz and performs real time target identification using cross-correlation to known bird-echo-returns on data from the radar. The system is capable of handling data from multiple radars scanning in horizontal or vertical mode and multiple cameras.
Multi-sensor applications in an operational offshore wind farm?
As traditional surveys only represent snapshots and telemetry is based on few individuals, it is expected that multi-sensor systems like MUSE will become indispensable tools in collecting long-term monitoring data on seabird behaviour and help developers document environmental compliance and minimise environmental impacts. Curtailment represents an important application of MUSE in which the detection system is integrated with wind farm control software (SCADA) and issues early warning and shut down commands in response to a bird approaching the wind farm. Depending on the requirements of the project, both controlled and un-supervised curtailment can be applied.
Other methods of curtailment based on statistical approaches are inefficient as wind turbines are shut down based on weather and historic bird occurrence with potential large consequences for the industry in terms of lost energy production. Accordingly, multi-sensor systems will allow wind energy to realise its full commercial potential offshore by increasing response time, minimising curtailments and reducing costs.