Harnessing the power of wind has great appeal in terms of renewable energy. Wind power is appealing as, despite its variability, it is an energy source available throughout the day and in all seasons.
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Wind speeds are highest during seasons that create the most energy demand which helps to improve its reliability.1 For the US, where many coastal regions are densely populated and so energy transmission is more straightforward, wind power has the potential to double the total amount of energy generated.2
However, wind farms are often considered unsightly and disruptive due to the low-level noise and the potential risk to local wildlife. One way to address some of these concerns, and to take advantage of greater windspeeds, has been to develop windfarms that are in remote regions, particularly out at sea.
Remote instruments and infrastructure need to be capable of running independently with minimal external interaction. Despite the advantages of building windfarms in more remote locations, accessing such regions can be a costly and complicated task. This challenge continues to grow if there is a need to perform complex maintenance or to bring bulky equipment for diagnostics.
Developing offshore platforms benefits from accurate diagnostics that are capable of operating independently and providing 'early warning’ information about potential problems. One way of doing this has been to use fiber optic cables as sensors and for information transfer.
Optics in Wind Farms
Researchers worldwide have been developing fiber optic sensors to form distributed sensor networks to measure ground tremors and movement.3 A fiber optic can act as a sensor over its entire length using time-domain reflectometry techniques.4
In a time-domain reflectometry experiment, a signal is passed through a cable length, and the reflected signal's characteristics are measured. In an undamaged cable length, the reflected signal should look like the transmitted signal, detected at a time delay proportional to the length of the cable.
However, if there is any defect in the cable or type of perturbation, there may be significant changes in the amplitude of the detected signal. While some losses are inevitable, particularly along kilometers of fiber optic cabling, more significant losses can reflect damage and issues with the cabling.
For windfarms, fiber optic sensing can be used for two purposes: to detect changes in the environment and for seismic events such as earthquakes. These are a frequent problem for many offshore farms and in regions such as California, which already has several major windfarms generating a significant portion of its total electricity.5
The second benefit of using distributed fiber optic networks for continuous real-time sensing of offshore farms is to detect issues with the turbines themselves.4 As turbines start to experience faults, the frequency of the vibrations they emit begins to change. The high sensitivity of techniques such as time-domain optical reflectometry means even tiny deviations in these frequencies can be detected, and this information can be used to trigger warning alerts for maintenance.
Remote Sensing
As well as the sensitivity of using fiber optic cables to detect issues within wind turbines, another critical advantage of this technique is the ease with which it can be deployed to remote offshore regions. Many trans-continental fiber optic cable bundles are the backbone of our telecommunications infrastructure, and so there are numerous technologies available to protect the cabling from environmental hazards that range from earthquakes to marine wildlife.
Similar methodologies can be employed for offshore windfarms to ensure robust sensing networks. Fiber optic cables are also inherently well-suited to the transfer of large amounts of data due to the high bandwidth transmission capabilities of fibers and the fact that relying on electromagnetic pulses for information transfer means that this can be performed at the speed of light.
Such technologies can be used to monitor the status of several parts of the turbine, including the blades,6 and make an early-time intervention for maintenance possible. Helping to reduce maintenance costs is a key part of increasing the viability of offshore wind farms that generally have much higher energy-generating potential.
Wind power generation is crucial for decarbonization, particularly in countries still heavily reliant on burning fossil fuels for energy generation. Changes and optimization of the turbine design, including the supports for offshore structures, will be heavily reliant on the data that can be gathered from distributed fiber optic sensing networks, as this will help identify the structural stresses that such wind farms need to overcome.7 Due to the large and continual volumes of data generated by such networks, it is likely machine learning and similar automated analyses will become more common to exploit this information.
References and Further Reading
- Ledo, L., Torrabla, V., Soret, A., Ramon, J., & Doblas-Reyes, F. J. (2019). Seasonal forecasts of wind power generation. Renewable Energy, 143, 91–100. https://doi.org/10.1016/j.renene.2019.04.135
- DOE, (2016) National Offshore Wind Strategy, https://www.energy.gov/sites/default/files/2016/09/f33/National-Offshore-Wind-Strategy-report-09082016.pdf
- Ledo, L., Torrabla, V., Soret, A., Ramon, J., & Doblas-Reyes, F. J. (2019). Seasonal forecasts of wind power generation. Renewable Energy, 143, 91–100. https://doi.org/10.1016/j.renene.2019.04.135
- Wang, J., & Wu, Y. (2020). International Journal of Greenhouse Gas Control Wellhead based time domain reflectometry for casing integrity investigation. International Journal of Greenhouse Gas Control, 96, 103002. https://doi.org/10.1016/j.ijggc.2020.103002
- Perveen, R., Kishor, N., & Mohanty, S. R. (2014). Offshore wind farm development : Present status and challenges. Renewable and Sustainable Energy Reviews, 29, 780–792. https://doi.org/10.1016/j.rser.2013.08.108
- Rademakers, L. W. M. M., Vebruggen, T. W., Van Der Werff, P. A., Korterink, H., Richon, D., Rey, P., & Lancon, F. (2004). Fiber optic blade monitoring. European Wind Energy Conference.
- Watson, S., Moro, A., Reis, V., Baniotopoulos, C., Barth, S., Bartoli, G., Bauer, F., Boelman, E., Bosse, D., Cherubini, A., Croce, A., Fagiano, L., Fontana, M., Gambier, A., Gkoumas, K., Golightly, C., Iribas, M., Jamieson, P., Kaldellis, J., … Wiser, R. (2019). Future emerging technologies in the wind power sector : A European perspective. Renewable and Sustainable Energy Reviews, 113, 109270. https://doi.org/10.1016/j.rser.2019.109270
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