Comparative Analysis of Optimization Algorithms to Enhance WPGSs Performance: Malaysia Case Study

Abstract

The growing interest in wind power and its expanding integration into the electrical power grid necessitate significant efforts to enhance the efficiency and performance of wind power generation Systems (WPGS) through optimized design and operation. This paper investigates the implementation of five optimization algorithms:sine cosine algorithm (SCA), grey wolf optimizer (GWO), particle swarm optimizer (PSO), transient search optimization (TSO), and a hybrid sine cosine algorithm-transient search optimizer (HSCATSO), to optimally design control schemes (ODCSs) of frequency converters within grid-connected WPGS. These schemes are crucial for improving conversion efficiency and performance. The optimal design procedure involves all optimization algorithms to minimize the total control scheme error by identifying the best settings for six controllers in the proposed system. The feasibility of ODCSs is validated through extensive simulation analysis in the MATLAB/Simulink environment, considering the challenges posed by the low wind speed profiles of Malaysia and severe grid disturbance conditions. The findings from the comparative analysis show that the ODCS developed with HSCATSO achieved the highest conversion efficiency, reaching 98.57%, and exhibited superior system stability, followed by the ODCS utilizing SCA. In contrast, the ODCSs designed using PSO demonstrated the lowest conversion efficiency, at 93.79%, along with the least system stability. This outcome confirms the significant role of ODCSs in improving the efficiency and performance of WPGS under the mentioned challenges, while also revealing that their effectiveness varies depending on the precision of design established by the utilized optimization algorithm.

Keywords

• Wind turbine control
• power electronic converter
• MPPT
• PMSG
• VSWT
• WPGS