Stalling phenomenon prevention using artificial neural network based strategy for oscillation water column coupled to Wells turbine

In this paper, an Artificial Neural Network ANN based algorithm is proposed for energy conversion system adapting Oscillating Water Column (OWC) coupled to a Wells turbine. The presented algorithm integrates with the associated P-Q controlled grid connected Doubly Fed Induction Generator (DFIG) by calculating the optimized turbine speed that grantees stalling phenomenon avoidance. The proposed technique effectiveness has been attested by simulating various operation conditions featuring different input differential pressure levels. The presented technique offers simplified tuned algorithm in addition to fast transient response with minimal oscillation during steady state operation.