Hybrid Particle Swarm and Gravitational Search Algorithm-Based Optimal Fractional Order PID Control Scheme for Performance Enhancement of Offshore Wind Farms

This article aimed to introduce a novel application of a hybrid particle swarm optimizer and gravitational search algorithm (HPSOGSA) that can be used for optimal control of offshore wind farms' voltage source converter connected to HVDC transmission lines. Specifically, the algorithm was used to design fractional-order proportional-integral-derivative (FOPID) controller parameters designed to minimize the system's objective function based on an integral squared error. The proposed FOPID controller was applied to improve offshore wind farm performance under different transient conditions, and its results were compared with a PI controller that was designed using a genetic algorithm and grey wolf optimization algorithm. The fault ride-through capabilities of the proposed control strategy were also evaluated. The findings suggest that the HPSOGSA-based FOPID controller outperformed the other two methods, significantly enhancing offshore wind farm operations. The control strategy was thoroughly tested using MATLAB/Simulink under various operating scenarios.

Automated summary

This article introduces a novel application of a hybrid particle swarm optimizer and gravitational search algorithm (HPSOGSA) for optimal control of offshore wind farms. The proposed fractional-order proportional-integral-derivative (FOPID) controller parameters are designed to minimize the system's objective function based on an integral squared error. The HPSOGSA-based FOPID controller outperforms other methods and significantly improves offshore wind farm operations.