Open AccessAn enhanced flower pollinated algorithm with a modified fluctuation rate for global optimisation and load frequency control system
Author(s) -
Oladipo Stephen,
Sun Yanxia,
Wang Zenghui
Publication year - 2022
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12435
Subject(s) - robustness (evolution) , computer science , rate of convergence , adaptability , automatic frequency control , algorithm , electric power system , control theory (sociology) , mathematical optimization , power (physics) , mathematics , control (management) , artificial intelligence , computer network , ecology , channel (broadcasting) , biochemistry , chemistry , telecommunications , physics , quantum mechanics , biology , gene
This paper proposes a new hybridised approach comprising the flower pollination algorithm and pathfinder algorithm (FPAPFA), in order to address optimisation problems and for load frequency control system. Although the FPA is a popular algorithm that has been widely used in diverse applications, its implementation is met with the tendency to be trapped in local optimal due to an imbalance between the exploration and exploitation process. Consequently, the FPA's exploration functionality can be enhanced by using the PFA features to shift certain pollens to moderately enhanced locations rather than leading them to random positions. Furthermore, a modified fluctuation rate is incorporated into the PFA to reinforce the exploitative competence of the FPAPFA. Compared to other popular techniques, the proposed algorithm's performance was evaluated against 23 standard mathematical optimisation functions and statistically tested using the Wilcoxon rank‐sum and Friedman rank tests. Moreover, the FPAPFA is applied to regulate two unequal multi‐area interconnected power systems with different generating units (thermal, hydro, diesel, and wind power plants) while also integrating redox flow batteries (RFBs) and interline power flow controller (IPFC). Simulation results show that the proposed FPAPFA delivered better results than other algorithms with improved convergence speed, stability, and robustness.