Quantum discrete particle swarm algorithm of tidal turbine array optimization

Abstract

To solve the problem of multi-parametermultiparameter and multi-constraint tidal turbine array optimization, an improved quantum discrete particle swarm (QDPS) algorithm is proposed. In the QDPS algorithm, the computational domain is discretized and each particle represents a turbine array layout. The generated energy is taken asconsidered the objective function and updated through iterative optimization. The QDPS algorithm is verified using flood and ebb spring tide data from the Pu-Hu waterway in the Zhoushan Islands of China, and the optimized effect is analyzed. The results show that autonomous intelligent optimization can be achieved by using the QDPS algorithm and the optimal velocity is rapid. Compared with the traditional crossing layout, 28.9% of total power generation is improved in the flood tide and 41.8% in the ebb tide. The optimized tidal turbine array layout is consistent with the power density distribution of the power density of tidal current energy. In summary, as a scientific tool, the QDPS algorithm can be used to study on tidal turbine array optimization.