Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment

Layout optimization is capable of increasing turbine density and reducing wake effects in wind plants. However, such optimized layouts do not guarantee fixed T-2-T distances any direction would be disadvantageous if reduction computational costs due to set-point updates also a priority. Regular are considered basic because coordinates can determined intuitively without the application algorithms. used intentionally create directions large distances, hence, achieve gains standard/non-optimized operations these directions, while having close other from which enjoyed. In this study, regular hexagonal layout deploy turbines within area dimension, turbulence intensity-constrained axial induction-based plant-wide carried out using particle swarm, artificial bee colony, differential evolution techniques. Optimized plant power for three deployments (4D, 5D, 6D) compared non-optimized 7D deployment mean inflows. Results suggest that increase up 37% possible with 4D deployment, increment decreasing as distance increases inflow increases.