An improved triangular mutated slime mould algorithm for developing an optimized frequency control of a Bi-zonal islanded microgrid power system
Ibrahim Musa Conteh, Ahmed Tijani Salawudeen, Aminu Onimisi Abdulsalami, Qingguo Du
Abstract
Global optimization spans various fields, offering solutions for complex, multi-variable problems that are often highly nonlinear and high-dimensional. Load–frequency control is essential for maintaining stability in an islanded microgrid. When a disturbance occurs, system frequency fluctuates, and it is crucial to dampen these fluctuations to ensure reliable operation within the islanded microgrid. This article presents research on optimizing a load frequency control in a Bi-zonal Islanded microgrid, proposing an enhanced slime mould algorithm (SMA) called triangular mutation slime mould algorithm (TMSMA), which integrates three key innovations into SMA for the first time. Firstly, the algorithm uses a good point set for population initialization, enhancing diversity compared to uniform random initialization. Secondly, it incorporates triangular mutation as the primary exploration mechanism to identify promising search regions efficiently. Finally, a multi-operator parallel search balancing exploration and exploitation improves the algorithm's adaptability in diverse conditions. TMSMA's performance was tested on CEC2017 benchmark functions and benchmarked against nine leading optimization algorithms. The results indicate that TMSMA achieves superior outcomes on these functions, underscoring its robust optimization capabilities. We further applied the TMSMA to solve frequency control in a bi-zonal islanded microgrid problem, and its performance was evaluated against benchmark methods under various load disturbance scenarios. Results showed that TMSMA reduced frequency deviation with an average of approximately 0.03 % across all three scenarios used in this study. This showed its practical engineering effectiveness, confirming its value in real-world applications. These findings suggest that TMSMA holds strong potential for applications requiring complex, efficient optimization solutions.