Hybrid cuckoo search algorithm and grey wolf optimiser-based optimal control strategy for performance enhancement of HVDC-based offshore wind farms

The hybridisation of two or more algorithms is recently emerging to detect superior solutions to the optimization troubles. In this study, a new hybrid cuckoo search algorithm and grey wolf optimiser (CSA-GWO) optimisation technique is exercised and exhibited to optimally design and tune the controller parameters installed in the voltage source converter (VSC) of an offshore wind farm (OWF). One of the widely used control strategies for VSC is the proportional-integral (PI) closed-loop control system. The new hybrid optimisation algorithm is used to design and tune the PI controllers' parameters to improve the performance of OWF. It shall be mentioned that these parameters are usually hard to obtain owing to the high level of embedded non-linearity in such energy systems. The performance of such optimally designed PI controllers is presented in both dynamic and transient conditions. To examine the realistic stability of the proposed algorithm, real wind speed pattern has been captured from Egypt wind farm at Zafarrana and simulated. The obtained results from this new hybrid optimisation CSA -GWO control system reflect its superiority over other traditional algorithms, such as genetic algorithm, especially during symmetrical and unsymmetrical faults. CSA-GWO algorithm was examined using MATLAB/Simulink.