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A novel approach of intensified barnacles mating optimization for the mitigation of power system oscillations
Author(s) -
Devarapalli Ramesh,
Bhattacharyya Biplab,
Kumari Archana
Publication year - 2021
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.6303
Subject(s) - particle swarm optimization , electric power system , benchmark (surveying) , computer science , robustness (evolution) , cuckoo search , mathematical optimization , computation , process (computing) , control theory (sociology) , power (physics) , algorithm , mathematics , artificial intelligence , control (management) , biochemistry , physics , chemistry , geodesy , quantum mechanics , gene , geography , operating system
Optimization is the process of attaining the best solution from the available set of prioritized constraints to maximize or minimize the desired function involved in it. It is imperative in engineering to plan and design to find the best out of confined resource and time. A recently recommended barnacles matting optimization (BMO) has been recognized in computing the optimal parameters of the electric power system with excellent performance characteristics. In this paper, the intensified version of BMO has been proposed with the aid of cuckoo search (CS) and traditional particle swarm optimization (PSO). An in‐depth analysis was made on the BMO technique and proposed suitable modifications to deal with the electrical power system stability enhancement issue efficiently. The proposed technique in the power system stabilizer (PSS) parameter computation is validated on the 23 benchmark functions to examine for its suitability. The robustness of the proposed method is presented via statistical analysis and boxplot of the 23 benchmark functions. The PSS parameters are computed in a benchmark two area four machine system using intensified BMO under self‐clearing fault conditions. A multi‐objective function is designed to improve the damping nature offered under system uncertainties, and the comparative analysis is presented among conventional PSS, BMO, intensified BMO with CS and PSO (BMO‐CS and BMO‐PSO), and harris hawks optimizer.