Long Term Prediction of Tidal Currents

Numerous techniques have been suggested for extracting energy from the sea. Tidal current turbines are a convenient method for extracting power from oceanic currents. Tidal turbines share many similarities to wind turbines; however due to the higher density of sea water they can produce 800-900 time...

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Bibliographic Details
Published in:IEEE systems journal 2011-06, Vol.5 (2), p.146-155
Main Authors: Jahromi, Mahda, Maswood, Ali I., Tseng, King-Jet
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy sources
Artificial neural networks
Data models
Density
Harmonic analysis
Harmonic analysis of tides
marine energy
Mathematical model
Mathematical models
neural networks
Position (location)
prediction
Predictive models
tidal current
Tidal currents
Tides
Turbines
Wind turbines
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Summary:Numerous techniques have been suggested for extracting energy from the sea. Tidal current turbines are a convenient method for extracting power from oceanic currents. Tidal turbines share many similarities to wind turbines; however due to the higher density of sea water they can produce 800-900 times more power when compared to an equivalent wind turbine of similar size operating at the same speed. Apart from having remarkable power densities, tidal currents are robust to aesthetic issues like the lack of wind or fog that can affect other renewables. The nature of tide formation makes them very predictable, and so in a general sense tidal energy is very reliable; which is a crucial factor in successful integration of renewable resources into the grid. In this study the authors have tested the predictability of tidal currents using various algorithms and ultimately based on the conventional harmonic analysis of tides and model free estimators a new prediction method has been proposed. The performance of the proposed method is then tested using actual recorded data. It is found that if a tidal current flow regime at a particular location has been properly studied over time, its variations can be accurately predicted for both operational (Short Term) and planning (Long Term) purposes.
ISSN:1932-8184
1937-9234
DOI:10.1109/JSYST.2010.2090401