Enhanced salp swarm algorithm: Application to variable speed wind generators

Abstract

This article presented a novel modification and application of the salp swarm algorithm (SSA) that is inspired by the chain behavior of salp fishes that live in deep oceans. Firstly, the enhanced salp swarm algorithm (ESSA) is proposed to improve the inadequate results of the SSA compared to the other algorithms, especially for the high dimensional functions. The ESSA algorithm is verified using twenty-three benchmark test functions and compared with the original SSA algorithm and other algorithms. The statistical analysis of the obtained results revealed that the ESSA algorithm is significantly improved and the convergence curves showed the fast convergence to the best solution. Secondly, The SSA and ESSA algorithms are applied to enhance the maximum power point tracking and the fault-ride through ability of a grid-tied permanent magnet synchronous generator driven by a variable speed wind turbine (PMSG-VSWT). The multi-objective function (integral squared error) is minimized to find the high dimensional parameters of Takagi–Sugeno–Kang fuzzy logic controllers (TSK-FLC) used in the cascaded control of grid-tied PMSG-VSWT. The simulation results using PSCAD/EMTDC proved that the produced power when using ESSA is higher than when using SSA which mean higher efficiency and lower cost.