Power coefficient prediction of tidal turbine by adaptive soft computing methodology

Abstract

The main aim of the study was to analyze power coefficient of tidal turbine. The analzying was performed based on input-output data pairs. To arrange the data pairs measurements were performed on the tidal turebine. Adaptive soft computing methdology was used for estimation of relationship between the data pairs. The soft computing methodoogz was afterwards used for prediction of the power coefficient of tidal turbine based on the learined knowledge about the data pairs. During measurement procedure three inputs and one output were considered. The inputs are tip speed ratio – TSR, swap area of the turbine and time step. The output is power coefficient. The soft computing approach, namely, adaptive neuro fuzzz inference system – ANFIS was used for prediction of the power coefficient. Finally obtained results were compared with classical neural networks.