Akdeniz'de Deniz Akıntı Hızı ve Güç Potansiyelinin İstatistiksel Analizi

Yıl 2023, , 726 - 737, 10.03.2023

Alper Yıldırım

https://doi.org/10.47495/okufbed.1166738

Öz

Dünyada enerji kaynakları hızla tükenmektedir. Bu nedenle Dünya’da yeni enerji kaynak arayışları hızla artmaktadır. Deniz akıntılarından yüksek miktarda enerji sağlama potansiyeli, bu kaynağı cazip hale getirmiştir. Bu çalışmada, Akdeniz’de yer alan Silifke bölgesinde, deniz yüzeyinden 20 metre altındaki akıntıların enerji potansiyeli incelenmiştir. Meteorolojik şamandıra ölçümlerinden elde edilen deniz akıntısı verileri kullanılmıştır. Ayrıca bu çalışmada, Weibull ve Rayleigh modelleri kullanılarak istatistiksel analizler de yapılmıştır. Weibull olasılık dağılımı kullanmanın deniz akıntı hızı analizini kolaylaştırdığı, ayrıca güç yoğunluğunu yüksek doğrulukla tahmin ettiği görülmüştür. Son olarak, bu makale, bu istasyonun makul bir deniz akıntı gücü potansiyeline sahip olduğunu ve deniz akıntısı enerji türbinlerinin kurulumu için kullanılabileceğini kanıtlamıştır. Bu istasyonda deniz akıntısı güç yoğunluğu değeri 20 m derinlikte 46,56 W/m2 olarak bulunmuştur. Bu araştırmanın bulgularının, bu istasyondan deniz akıntısından elde edilecek enerji miktarını görmemize ve buranın deniz hidrodinamiğini anlamamıza yardımcı olması beklenmektedir.

Anahtar Kelimeler

Deniz hidrodinamiği, Weibull olasılık fonksiyonu, Rayleigholasılık fonksiyonu, Deniz akıntı hızı, Yenilenebilir enerji, Okyanus gücü

Statistical Analysis of Sea Current Velocity and Power Potential in the Mediterranean

Öz

Energy resources in the world are depleting rapidly. For this reason, the search for new energy sources in the world is increasing rapidly. The potential to generate large amounts of energy from sea currents has made this source attractive. In this study, the energy potential of the currents 20 meters below the sea surface in the Silifke region in the Mediterranean was investigated. Sea current data obtained from meteorological buoy measurements were used. In addition, statistical analyzes were performed using Weibull and Rayleigh models in this study. It has been found that using the Weibull probability distribution facilitates the analysis of sea current velocity and also predicts the power density with high accuracy. Finally, this article has proven that this station has reasonable offshore power potential and can be used for the installation of sea current energy turbines. The sea current power density value at this station was found to be 46.56 W/m2 at a depth of 20 m. It is expected that the findings of this research will help us to see the amount of energy that will be obtained from the sea current from this station and to help us understand the marine hydrodynamics of this station.

Anahtar Kelimeler

Marine hydrodynamics, Weibull probability function, Rayleigh probability function, Sea current velocity, Renewable energy, Ocean power

Kaynakça

• Albani A., Ibrahim MZ. The probabılıty densıty dıstrıbutıon for ocean current speed at selected sıtes in Malaysia. Journal of Critical Reviews 2020; 7: 5224–5229
• Arhan İ., Bilgin Z. Yenilenebilir Sistemlerde Maksimum Güç Noktası Takibi ve Enerji Yönetimiyle Enerji Verimliliği. OKU Journal of The Institute of Science and Technology 2022; 5: 75–91.
• Ashkenazy Y., Gildor H. On the probability and spatial distribution of ocean surface currents. The Journal of Physical Oceanography 2011; 41: 2295–2306
• Barnier B., Domina A., Gulev S., et al. Modelling the impact of flow-driven turbine power plants on great wind-driven ocean currents and the assessment of their energy potential. Nature Energy 2020; 5: 240–249.
• Bento PMR., Pombo JAN., Mendes RPG., Calado MRA., Mariano et al. Ocean wave energy forecasting using optimised deep learning neural networks. Ocean Engineering 2021; 219: 108372.
• Bilgili M., Sahin B. Investigation of Wind Energy Density in the Southern and Southwestern Region of Turkey. Journal of Energy Engineering 2009; 135: 12–20.
• Bilgili M., Yildirim A., Ozbek A., et al. Long short-term memory (LSTM) neural network and adaptive neuro-fuzzy inference system (ANFIS) approach in modeling renewable electricity generation forecasting. International Journal of Green Energy 2021; 18: 578–594
• Canepa E., Pensieri S., Bozzano R., et al. The ODAS Italia 1 buoy: More than forty years of activity in the Ligurian Sea. Progress in Oceanography 2015; 135: 48–63.
• Chu PC. Weibull distribution for the global surface current speeds obtained from satellite altimetry. International Geoscience and Remote Sensing Symposium 2008; 3: 11–15.
• Freilich MH., Dunbar RS. The accuracy of the NSCAT 1 vector winds: Comparisons with National Data Buoy Center buoys. Journal of Geophysıcal Research-Oceans 1999; 104: 11231–11246.
• Hays GC. Ocean currents and marine life. Current Biology 2017; 27: R470–R473.
• Kabir A., Lemongo-Tchamba I., Fernandez A. An assessment of available ocean current hydrokinetic energy near the North Carolina shore. Renewable Energy 2015; 80: 301–307
• Kim DK., Wong EWC., Lee EB., et al. A method for the empirical formulation of current profile. Ships and Offshore Structures 2019; 14: 176–192.
• Mandal S., Sil S., Gangopadhyay A., et al. On extracting high-frequency tidal variability from HF radar data in the northwestern Bay of Bengal. Journal of Operational Oceanography 2018; 11: 65–81.
• Mears CA., Smith DK., Wentz FJ. Comparison of Special Sensor Microwave Imager and buoy-measured wind speeds from 1987 to 1997. Journal of Geophysical Research: Oceans 2001; 106: 11719–11729.
• Minesto. Ocean energy, https://minesto.com/about-us (2020, accessed 12 December 2020).
• Neelamani S., Al-Osairi Y. Probability distribution, statistical characteristics, and power potential of seawater velocity around boubyan island in Kuwait. Journal of Engineering Research 2019; 7: 143–166.
• Petersen GN. Meteorological buoy measurements in the Iceland Sea, 2007-2009. Earth System Science Data 2017; 9: 779–789.
• Suzuki T., Mori N., Cox DT. Statistical modeling of near-bed pressure gradients measured on a natural beach. Coastal Engineering Journal 2009; 51: 101–121.
• Vílchez M., Clavero M., Baquerizo A., et al. An Approximation to the Statistical Characteristics of Wind Waves in Front and from the Toe of the Structure to the Toe of the Crown of Nonovertopped Breakwaters. Coastal Engineering Journal; 59:3, 1750012-1-1750012-38
• Wagner V., Hageberg AA., Michelsen C. EGOS-European Group on Ocean Stations providing real time buoy observations from data sparse areas of the North Atlantic Ocean and adjacent seas. Elsevier Oceanography Series 2003; 69: 340–344.